KR102633306B1 - Battery management system using artificial neural network - Google Patents

Abstract

According to an embodiment of the present invention, a battery management method is disclosed. The battery management method may include: obtaining first charge amount information by inputting battery state information at a first time point into a first model; determining a charge prediction time based on the obtained first charge amount information; obtaining second charge amount information by inputting battery state information at a second time point into the first model when the charge prediction time is reached; and determining whether to continue charging the battery based on the second charge amount information.

Description

Battery management system using artificial neural network {BATTERY MANAGEMENT SYSTEM USING ARTIFICIAL NEURAL NETWORK}

The present invention relates to a battery management system, and more specifically, to controlling battery charging using an artificial neural network.

Batteries can store power and allow electronic devices to operate without external power supply. The battery management system plays a role in managing the charging/discharging of the battery. Batteries generate heat in the process of storing and discharging power, and the charging/discharging of the battery itself affects the lifespan of the battery, and the lifespan may be further deteriorated depending on the heat generated.

Accordingly, there is a demand for a battery management system to maintain battery life and performance.

The present invention was developed based on the above-described background technology and relates to a battery management system for controlling charging and discharging of a battery.

A battery management method according to an embodiment of the present invention is disclosed. The battery management method includes obtaining first state of charge (SOC) information by inputting battery state information at a first point in time into a first model; determining a charging prediction time based on the obtained first charging amount information; When the predicted charging time is reached, inputting battery state information at a second point in time into the first model to obtain second charge amount information; and determining whether to continue charging the battery based on the second charge amount information.

Alternatively, the battery state information may include at least one of battery voltage, current, and temperature information.

Alternatively, the first model may be a model that is trained using training data including battery state information labeled with charge amount information and is trained to output charge amount information based on the battery state information.

Alternatively, the learning data may include a first learning data subset consisting of battery state information when charging and charge amount information according to the battery state information, and a second learning data subset consisting of battery state information when discharging and charge amount information according to the battery state information. May include a subset of training data.

Alternatively, the first learning data subset and the second learning data subset may be composed of training data, verification data, and test data, respectively.

Alternatively, the first model is trained with training data from the first training data subset and then verified with validation data from a second training data subset, and includes test data from the first training data subset and It may be a model tested with test data of the second learning data subset.

Alternatively, the first model is trained with training data from the second training data subset and then verified with validation data from the first training data subset, and includes test data from the first training data subset and It may be a model tested with test data of the second learning data subset.

Alternatively, the first model may include one or more submodels configured to correspond to each battery classified by battery information.

Alternatively, the battery information includes information to distinguish the battery from other types of batteries, and may include information related to the physical specifications of the battery, battery manufacturer information, and battery physical property information.

Alternatively, the method may further include selecting a submodel for processing battery state information from among one or more submodels of the first model based on the battery information.

Alternatively, the predicted charging time may be determined based on a first map configured to output the predicted charging time based on the charging amount information, battery state information, battery health information, and charging goal.

Alternatively, the charging target may include a charging elapsed time and a target voltage value.

Alternatively, the battery health information may be updated according to a predetermined period based on battery information and battery cycle information.

Alternatively, when the second charge amount information is less than a predetermined charge amount, the step of maintaining the charging state and re-determining the charging prediction time based on the second charge amount information may be further included.

Alternatively, if the second charge amount information is greater than or equal to a predetermined charge amount, the step of stopping battery charging and converting the battery to a discharged state may be further included.

Alternatively, the method may further include acquiring third charge amount information based on battery state information at a third time after the battery is converted to a discharged state.

Alternatively, the method may further include displaying charging information including battery status information, charge amount information, battery health information, and charging prediction time.

Alternatively, the charging information may include a temperature information visual object expressed in a color determined based on temperature information of the battery state.

Alternatively, the temperature information visual object may further display a visual highlight when the temperature information of the battery state information is outside a normal range.

Alternatively, the charging information includes a battery health information visual object and a charge amount information visual object, wherein the battery health information visual object is configured to visually identify a deteriorated portion of the battery and a rechargeable portion, and the battery health information visual object is configured to visually identify a deteriorated portion of the battery and a chargeable portion. The information visual object and the charging amount information visual object may be composed of achromatic colors, and the temperature information visual object may be composed of chromatic colors.

Alternatively, when the battery health information is below a predetermined level, the battery health information visual object and the charge amount information visual object are composed of chromatic colors, the temperature information visual object is composed of achromatic colors, and the battery health information visual object and The charging amount information visual object may be displayed larger than the size of the temperature information visual object.

Alternatively, if the battery health information is below a predetermined level and the temperature information of the battery status information is outside the normal range, the temperature information visual object is displayed larger than the size of other visual objects, and battery replacement notification information is additionally displayed. can be displayed.

Alternatively, a user device is disclosed, the user device comprising: one or more processors; a memory storing instructions executable by the one or more processors; and a display, wherein the one or more processors input battery state information at a first point in time into a first model to obtain first charge amount information, and determine a charging prediction time based on the obtained first charge amount information. , when the charging prediction time is reached, input battery state information at a second point in time into the first model to obtain second charge amount information, and determine whether to continue charging the battery based on the second charge amount information. You can.

Registered patent 10-2302760 provides an artificial intelligence deceptive power management system.

The present invention can provide a battery management system for controlling charging and discharging of a battery.

1 is a block diagram of a user device for implementing a battery management system according to an embodiment of the present disclosure.
Figure 2 is a schematic diagram showing an artificial neural network of an embodiment of the present disclosure.
3 is a flowchart of a battery management method according to an embodiment of the present disclosure.
Figure 4 is an example of learning data according to an embodiment of the present disclosure.
Figure 5 is a schematic diagram of a first model of one embodiment of the present disclosure.
6 is an example diagram of charging information displayed on the display of a user device according to an embodiment of the present disclosure.
7 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “if it contains only A,” “if it contains only B,” or “if it is a combination of A and B.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In this disclosure, network function, artificial neural network, and neural network may be used interchangeably.

1 is a block diagram of a user device for implementing a battery management system according to an embodiment of the present disclosure.

The configuration of the user device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the user device 100 may include different configurations for performing a computer environment, and only some of the disclosed configurations may configure the user device 100. The user device 100 of the present disclosure may perform learning of the first model 400 for predicting battery charge information, or may perform a battery management method by receiving the first model learned from another computer device. there is.

The user device 100 may include a processor 110, a memory 130, a network unit 150, a display 170, and a battery (not shown).

The processor 110 may be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit) may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for learning neural networks, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. Calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, CPU and GPGPU can work together to process learning of network functions and data classification using network functions. Additionally, in one embodiment of the present disclosure, the processors of a plurality of computing devices can be used together to process learning of network functions and data classification using network functions. Additionally, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150.

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, hard disk type, multimedia card micro type, or card type memory (e.g. (e.g. SD or -Only Memory), and may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk. The user device 100 may operate in relation to web storage that performs a storage function of the memory 130 on the Internet. The description of the memory described above is merely an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( A variety of wired communication systems can be used, such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN).

In addition, the network unit 150 presented in this specification includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and SC-FDMA ( A variety of wireless communication systems can be used, such as Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may be configured regardless of the communication mode, such as wired or wireless, and may be composed of various communication networks such as a personal area network (PAN) and a wide area network (WAN). You can. Additionally, the network may be the well-known World Wide Web (WWW), or may use wireless transmission technology used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth.

The display 170 is a display device of the user device 100 and may include any visual expression means for displaying information desired to be displayed visually on the user device 100.

Figure 2 is a schematic diagram showing an artificial neural network of an embodiment of the present disclosure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node. Nodes (or neurons) that make up neural networks may be interconnected by one or more links.

Within a neural network, one or more nodes connected through a link may form a relative input node and output node relationship. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the data of the output node may be determined based on the data input to the input node. Here, the link connecting the input node and the output node may have a weight. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network can be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if the same number of nodes and links exist and two neural networks with different weight values of the links exist, the two neural networks may be recognized as different from each other.

A neural network may consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer. Some of the nodes constituting the neural network may form one layer based on the distances from the first input node. For example, a set of nodes with a distance n from the initial input node may constitute n layers. The distance from the initial input node can be defined by the minimum number of links that must be passed to reach the node from the initial input node. However, this definition of a layer is arbitrary for explanation purposes, and the order of a layer within a neural network may be defined in a different way than described above. For example, a layer of nodes may be defined by distance from the final output node.

The initial input node may refer to one or more nodes in the neural network through which data is directly input without going through links in relationships with other nodes. Alternatively, in a neural network network, in the relationship between nodes based on links, it may mean nodes that do not have other input nodes connected by links. Similarly, the final output node may refer to one or more nodes that do not have an output node in their relationship with other nodes among the nodes in the neural network. Additionally, hidden nodes may refer to nodes constituting a neural network other than the first input node and the last output node.

The neural network according to an embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as it progresses from the input layer to the hidden layer. You can. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. You can. A neural network according to another embodiment of the present disclosure may be a neural network that is a combination of the above-described neural networks.

A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to the input layer and output layer. Deep neural networks allow you to identify latent structures in data. In other words, it is possible to identify the potential structure of a photo, text, video, voice, or music (e.g., what object is in the photo, what the content and emotion of the text are, what the content and emotion of the voice are, etc.) . Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBM). machine), deep belief network (DBN), Q network, U network, Siamese network, Generative Adversarial Network (GAN), etc. The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network to output output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be placed between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically and reduced from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform nonlinear dimensionality reduction. The number of input layers and output layers can be corresponded to the dimension after preprocessing of the input data. In an auto-encoder structure, the number of nodes in the hidden layer included in the encoder may have a structure that decreases as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, not enough information may be conveyed, so if it is higher than a certain number (e.g., more than half of the input layers, etc.) ) may be maintained.

A neural network may be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Learning of a neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

Neural networks can be trained to minimize output errors. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (i.e., labeled learning data), and in the case of non-teacher learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of teacher learning regarding data classification, the learning data may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of non-teachable learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the reverse direction (i.e., from the output layer to the input layer) in the neural network, and the connection weight of each node in each layer of the neural network can be updated according to backpropagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network training, a high learning rate can be used to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and in the later stages of training, a low learning rate can be used to increase accuracy.

In the learning of neural networks, the training data can generally be a subset of real data (i.e., the data to be processed using the learned neural network), and thus the error for the training data is reduced, but the error for the real data is reduced. There may be an incremental learning cycle. Overfitting is a phenomenon in which errors in actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that learned a cat by showing a yellow cat fails to recognize that it is a cat when it sees a non-yellow cat may be a type of overfitting. Overfitting can cause errors in machine learning algorithms to increase. To prevent such overfitting, various optimization methods can be used. To prevent overfitting, methods such as increasing the learning data, regularization, dropout to disable some of the network nodes during the learning process, and use of a batch normalization layer can be applied. You can.

3 is a flowchart of a battery management method according to an embodiment of the present disclosure.

The battery management method of one embodiment of the present disclosure may be performed in the user device 100.

The user device 100 may obtain first battery charge amount information based on battery state information at a first point in time. The user device 100 may obtain battery state information at any time and obtain battery charge amount information based on the battery state information. The first time point refers to the time point at which charging of the battery begins. Battery charge amount information can be estimated by calculating battery state information with the first model 400. Battery status information is monitoring information about the current state of the battery that can be obtained from the battery and may include voltage, current, and temperature information of the battery.

The first model 400 is a model learned to infer charge amount information from battery state information, and may be trained using learning data 300 including battery state information labeled with charge amount information. The first model 400 may be a model learned to output charge amount information based on the battery state information 10. The learning data 300 may be data labeled with battery state information at an arbitrary point in time and charge amount information according to each battery state information. The first model 400 can be trained using labeled learning data 300. The first model 400 may be composed of an input layer, an output layer, a hidden layer, an activation function, etc. For example, in one embodiment of the present invention, the hidden layer of the first model 400 may include two layers and 32 nodes in each layer. The description of the hyper parameters described above is only an example and the present invention is not limited thereto.

The battery state information 10 may be input to the first model 400 as a measured value, or may be input to the first model 400 by normalizing the measured value. Normalization minimizes the performance impact due to differences in scale of each component of the measurement (e.g. temperature and voltage).

The learning data 300 includes a first learning data subset 310 in which battery state information during charging and charge amount information according to the battery state information are labeled, and battery state information when discharging and charge amount information according to the battery state information are labeled. It may include a second learning data subset 320. Figure 4 is an example of learning data according to an embodiment of the present disclosure. Even under the same battery status information conditions, the charge amount information of the battery may be different depending on whether the battery is charging or discharging. Therefore, so that the model can learn both cases, the learning data 300 is a first learning data subset 310 consisting of battery state information collected during charging and charge amount information, which is a label for each battery state information, and discharge. It may include a second learning data subset 320 consisting of battery state information collected during the process and charge amount information, which is a label for each battery state information.

The first learning data subset 310 and the second learning data subset 320 will be composed of learning data 311 and 321, verification data 313 and 323, and testing data 315 and 325, respectively. You can. The training data can be used for learning the first model 400, and the first model 400 infers the charge information according to the battery state information of the learning data, and the inferred charge information and the charge amount information labeled in the battery state information are used for learning. It can be learned by back-propagating the error from to the first model 400. In learning the first model 400, the learning rate is set high at the beginning of the learning epoch to prevent the first model 400 from overfitting and consume the computing resources required for learning. can be reduced, and if a certain standard of performance is met through a performance test using test data at regular intervals during the learning repetition process, the learning rate can be adjusted low. This is to increase the final prediction accuracy by finely adjusting the weights of the first model 400 through a small learning rate in the late stage of learning.

The first model 400 is trained with the training data 311 of the first learning data subset 310 and then verified with the verification data 323 of the second learning data subset for general prediction performance. It may be tested with test data 315 of the first learning data subset and test data 325 of the second learning data subset.

In addition, the first model 400 is learned with the training data 321 of the second learning data subset and then verified with the verification data 313 of the first learning data subset 310, and the first learning data sub-set 310 It may be tested with the test data 315 of the set 310 and the test data 325 of the second learning data subset 320.

As described above, even under the same battery status information conditions, there may be differences between the charge amount information when the battery is being charged and the charge amount information when the battery is being discharged. In light of this, in order to generalize the prediction performance of the charge amount information of the first model 400, as described above, the first model 400 can be learned with data at the time of charging and tested with data at the time of discharging, It can be learned from data when discharging and tested with data when charging. In this case, the method of evaluating the accuracy of the model is different when learning the first model 400 with data at the time of charging and testing it with the data at the time of charging and in the case of learning the first model 400 with the data at the time of charging and testing it with the data at the time of discharging. can do. For example, when learning the first model 400 with data at the time of charging and testing it with the data at the time of charging, the accuracy of the first model 400 can be evaluated as high when inferring a value close to the label of the test data. When testing the first model 400 with data at the time of discharging, accuracy can be evaluated by applying an error for the difference between the data at the time of charging and discharging in the label of the test data.

Additionally, the first model 400 can be learned with data during charging (the first learning data subset 310) and tested with data during charging (the first learning data subset 310), and can be tested with data during charging (the first learning data subset 310). It may be learned with data (second learning data subset 320) and tested with data at the time of discharge (second learning data subset 320).

In addition, the training data for learning the first model 400 includes input including battery state information and meta information indicating whether the battery state information is data during charging or discharging, and a label for charge amount information for the input. It may be included, and the first model 400 may be trained to infer charge amount information from battery state information and meta information indicating when charging or discharging using such learning data.

Additionally, the learning data 300 may include subsets corresponding to each battery classified by battery information. For example, the learning data 300 may include subsets organized by battery size, material, manufacturer, and specification. That is, for example, charge amount information according to the battery status information of an 18650 lithium-ion battery, charge amount information according to the battery status information of a 4680 lithium-ion battery, and charge amount information according to the battery status information of an LFP battery may be different from each other. Accordingly, the learning data 300 may include subsets classified according to the type of battery.

The first model 400 may include one or more submodels 400-1, 400-2, and 400-n configured to correspond to each battery classified by battery information. Figure 5 is a schematic diagram of a first model of one embodiment of the present disclosure. As shown in FIG. 5 , the first model 400 may include a plurality of submodels learned with training data specialized for each battery. As described above, each battery may not be of one type, and the correlation between battery state information and charge amount information may be different depending on the type of battery. Accordingly, the first model 400 may include one or more submodels corresponding to each battery information. For example, the first sub-model 400-1 may be a model learned to infer charge amount information according to the battery state information of an 18650 lithium-ion battery, and the second sub-model 400-2 may be a model learned to infer charge amount information according to the battery state information of an 18650 lithium-ion battery. It may be a model learned to infer charge amount information according to battery state information. The above-mentioned battery information is only an example, and the description of the matching relationship between the battery information and the matched sub-models is only an example, and the present disclosure is not limited thereto.

The user device 100 inputs battery state information into the first model and selects a submodel for processing battery state information among one or more submodels of the first model 400 based on the battery information prior to obtaining charge information. You can select . As described above, there may be submodels for inferring charge information for each battery information, and the user device 100 identifies the type of battery based on the battery information included in the input data and creates a first model optimized for the battery. You can infer battery charge information by selecting a submodel of .

The user device 100 may determine the charging prediction time based on the obtained charging amount information. The predicted charging time may include prediction information about the time it will take for the battery to be charged to a predetermined charging target.

The charging prediction time may be determined based on a first map configured to output the charging prediction time based on charge amount information, battery state information, battery health information, and charging goal. The first map is a map that maps the expected charging time for each charge amount information, battery status information, battery health information, and charging goal. When the charge amount information, battery status information, battery health information, and charging target are input, the predicted charging time is output. It is configured to do so. Additionally, the first map may additionally include battery information and provide a charging prediction time according to the battery information.

The charging target corresponds to the conditions for completing charging of the battery and transitioning to a discharged state, and may include elapsed charging time and target voltage value.

Battery health information (SOH: state of health) is information related to the life of the battery. As the battery is used, its internal resistance increases and may deteriorate. Battery health information is a value that indicates the amount of deterioration and ensures that the battery is fully charged. It can include information about the level at which it can be achieved. Battery health information may be updated according to a predetermined cycle based on battery cycle information. Battery cycle information is information about the total amount of charge and total amount of discharge applied to the battery, and includes information about how repeatedly the battery is charged and discharged. Battery health information may be inferred and updated based on a predetermined model based on battery cycle information, or may be updated by measuring discharge capacity and comparing the ratio with process capacity.

When the predicted charging time is reached, the user device 100 may input battery state information at a second time point into the first model 400 to obtain second charge amount information. The second time point may be a point in time that has elapsed from the first time point to the estimated charging time. The user device 100 may input battery state information at the point in time at which charging progresses after charging for the required time to the first model 400 to check whether the charge amount information matches the prediction made before charging.

The user device 100 may determine whether to continue charging the battery based on the second charge amount information. When the second charge amount information is greater than or equal to a predetermined charge amount, the user device 100 may stop charging the battery and switch the battery to a discharged state. If the battery is overcharged, the lifespan of the battery may be negatively affected and safety issues may arise in addition to damage to the battery. Accordingly, when the battery is charged more than expected, the user device 100 stops charging and switches to a discharge state to bring the battery to an appropriate level of charge, thereby preventing battery deterioration and safety issues. The user device 100 may obtain third charge amount information based on battery state information at a third time after the battery transitions to a discharged state. The user device 100 may determine charging or discharging of the battery based on the third charge amount information. If the third charge amount information falls short of the charging target, the user device 100 may charge the battery again, and if the third charge amount information exceeds the charge target, the user device 100 may again maintain the discharged state.

The user device 100 may display charging information 500 including battery status information, charge amount information, battery health information, and estimated charging time. 6 is an example diagram of charging information displayed on the display of a user device according to an embodiment of the present disclosure.

When charging for the user device begins, charging prediction time information may be displayed in a pop-up form on the display () of the user device (). Charging information () may be displayed on the display () when receiving a user-selected input for charging prediction time information.

The charging information 500 includes a temperature information visual object 510 expressed in a color determined based on the temperature information of the battery state information. The temperature information visual object 510 may be expressed in color according to the temperature of the battery so that the user can intuitively understand the temperature of the battery. For example, when the battery's temperature is within the normal range, the temperature information visual object 510 is colored green; when the battery's temperature is below the normal range, the temperature information visual object 510 is colored blue; when the battery's temperature is above the normal range, the temperature information visual object 510 is colored blue. In this case, the temperature information visual object 510 may be displayed in red, and as the temperature increases, it may be displayed in stronger red. The temperature information visual object 510 may further display a visual highlight when the temperature information of the battery status information is outside the normal range. Visual emphasis expressions for the temperature information visual object 510 may include, for example, blinking, trembling, enlarging and shrinking, etc.

The charging information 500 includes a battery health information visual object 520 and a charge amount information visual object 530. The battery health information visual object 520 and the charge amount information visual object 530 may be expressed as overlapping with respect to one battery shape visual object. The battery health information visual object 520, the charge amount information visual object 530, and the temperature information visual object 510 may be expressed as overlapping with respect to one battery shape visual object, and only the temperature information visual object 510 is different from the other battery. Shape visual objects may be expressed separately.

The battery health information visual object 520 may be configured to visually identify a deteriorated portion of the battery and a rechargeable portion. For example, the battery health information visual object 520 may be expressed in an achromatic color with low brightness for the portion where the battery is deteriorated and cannot be charged, and may be expressed in an achromatic color with high brightness for the portion that can be charged.

The battery charge amount information visual object 530 is composed of achromatic colors and can display information about how much the battery is currently charged. That is, in the overall battery size, the battery health information visual object 520 displays the unusable portion and the usable portion separately, and the battery charge amount information visual object 530 displays the charged portion and the portion that requires charging among the usable portion. can be displayed separately.

If the battery health information is below a predetermined level, the battery health information visual object 520 and the charge amount information visual object 530 may be configured in colored colors to warn of this, and in this case, the temperature information visual object 510 may be configured in achromatic colors. The battery health information visual object 520 and the charge amount information visual object 530 may be displayed larger than the temperature information visual object 510.

If the battery health information is below a predetermined level and the temperature range of the battery status information is outside the normal range, the temperature information visual object 510 is displayed with other visual objects (e.g., the battery health information visual object 520 and the charge amount It may be displayed larger than the size of the information visual object 530, and battery replacement notification information (not shown) may be additionally displayed. The battery replacement notification information is information recommending battery replacement and may additionally include the location of a nearby service center where battery replacement is possible, a reservation schedule, and price information based on the location information of the user device.

As mentioned above, batteries contain active materials for storing energy, and the temperature of the battery may not only deteriorate the battery but also cause safety-related problems. Accordingly, the user device 100 can visually express battery charging information so that the user can better recognize and respond to problems that may occur during charging. Additionally, if a problem occurs during charging, the charging information can express visual or tactile information in addition to visual information, which allows the user to better recognize and respond to problems occurring in their device.

7 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has generally been described above as being capable of being implemented by a computing device, those skilled in the art will understand that the present disclosure can be implemented in combination with computer-executable instructions and/or other program modules that can be executed on one or more computers and/or in hardware and software. It will be well known that it can be implemented as a combination.

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure are applicable to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. It will be appreciated that each of these may be implemented in other computer system configurations, including those capable of operating in conjunction with one or more associated devices.

The described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media. Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes - a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM for reading the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1102, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those skilled in the art will also recognize removable optical media such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other types of computer-readable media, such as the like, may also be used in the example operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also connected to system bus 1108 through an interface, such as a video adapter 1146. In addition to monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102. For simplicity, only memory storage device 1150 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 1102 is connected to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed thereon for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158 or be connected to a communicating computing device on the WAN 1154 or to establish communications over the WAN 1154, such as over the Internet. Have other means. Modem 1158, which may be internal or external and a wired or wireless device, is coupled to system bus 1108 via serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.

Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) enables connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a base station. Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, the Internet, and wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (23)

As a battery management method,
Inputting battery state information at a first point in time into a first model to obtain first state of charge (SOC) information;
determining a charging prediction time based on the obtained first charging amount information;
When the predicted charging time is reached, inputting battery state information at a second point in time into the first model to obtain second charge amount information; and
determining whether to continue charging the battery based on the second charge amount information;
Including,
The first model is,
It is a model that is trained with learning data containing labeled battery state information and is trained to output charge amount information based on the battery state information,
The learning data is,
It includes a first learning data subset consisting of battery state information when charging and charge amount information according to the battery state information, and a second learning data subset consisting of battery status information when discharging and charge amount information according to the battery state information,
The first learning data subset and the second learning data subset are composed of training data, verification data, and test data, respectively,
The first model is,
After learning with the training data of the first learning data subset, it is verified with the verification data of the second learning data subset, and the test data of the first learning data subset and the test data of the second learning data subset A model tested with data,
How to care for your battery. According to claim 1,
The battery status information is,
Containing at least one of battery voltage, current, and temperature information,
How to care for your battery. delete delete delete delete According to claim 1,
The first model is,
After learning with the training data of the second learning data subset, it is verified with the verification data of the first learning data subset, and the test data of the first learning data subset and the test data of the second learning data subset are used. A model tested with data,
How to care for your battery. According to claim 1,
The first model is,
Containing one or more submodels configured to correspond to each battery classified by battery information,
How to care for your battery. According to claim 8,
The battery information is,
Contains information to distinguish the battery from other types of batteries, and includes information related to the physical specifications of the battery, battery manufacturer information, and battery physical property information.
How to care for your battery.
According to claim 8,
selecting a submodel for processing battery state information from among one or more submodels of the first model based on the battery information;
Containing more,
How to care for your battery.
According to claim 1,
The predicted charging time is,
Determined based on a first map configured to output a charging prediction time based on the charge amount information, battery state information, battery health information, and charging goal,
How to care for your battery. According to claim 11,
The charging goal is,
Including charging elapsed time and target voltage value,
How to care for your battery. According to claim 11,
The battery health information is,
Updated according to a predetermined cycle based on battery information and battery cycle information,
How to care for your battery. According to claim 1,
If the second charge amount information is less than a predetermined charge amount, maintaining the charging state and re-determining the charging prediction time based on the second charge amount information;
Containing more,
How to care for your battery. According to claim 1,
stopping battery charging and converting the battery to a discharged state when the second charge amount information is greater than or equal to a predetermined charge amount;
Containing more,
How to care for your battery. According to claim 15,
Obtaining third charge amount information based on battery state information at a third time after the battery is converted to a discharged state;
Containing more,
How to care for your battery.
According to claim 1,
Displaying charging information including battery status information, charge amount information, battery health information, and estimated charging time;
Containing more,
How to care for your battery. According to claim 17,
The charging information includes a temperature information visual object expressed in a color determined based on the temperature information of the battery state information,
How to care for your battery.
According to claim 18,
The temperature information visual object further displays a visual highlight when the temperature information of the battery status information is outside a normal range,
How to care for your battery. According to claim 19,
The charging information includes a battery health information visual object and a charge amount information visual object, and the battery health information visual object is configured to visually identify a deteriorated portion of the battery and a rechargeable portion,
The battery health information visual object and the charge amount information visual object are composed of achromatic colors, and the temperature information visual object is composed of chromatic colors,
How to care for your battery. According to claim 20,
If battery health information is below a predetermined level,
The battery health information visual object and the charge amount information visual object are composed of chromatic colors, and the temperature information visual object is composed of achromatic colors, and the battery health information visual object and the charge amount information visual object are larger than the size of the temperature information visual object. displayed large,
How to care for your battery. According to claim 21,
If the battery health information is below a predetermined level and the temperature information of the battery status information is outside the normal range,
The temperature information visual object is displayed larger than the size of other visual objects, and battery replacement notification information is additionally displayed.
How to care for your battery. delete