CN109986997B - A power battery SOC prediction device, vehicle and method - Google Patents
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Abstract
本发明涉及动力电池健康状态领域，公开一种基于模糊多模型卡尔曼滤波的动力电池SOC预测装置，该动力电池SOC预测装置包括连接于动力电池的以下部件记录动力电池充放电时间的微控制器、测量动力电池温度的温度传感器、测量动力电池充电电流的电流传感器、测量充电电池放电电压的电压传感器，其中，微控制器分别连接于温度传感器、电流传感器和电压传感器，微控制器在建立多个预测动力电池的荷电状态的数学模型和得到每个荷电状态的权重之后，通过加权求和算法得到动力电池的荷电状态值。本发明提高了描述动力电池的充放电特性的精度。
The invention relates to the field of power battery state of health, and discloses a power battery SOC prediction device based on fuzzy multi-model Kalman filtering. The power battery SOC prediction device includes a microcontroller connected to the following components of the power battery for recording the charging and discharging time of the power battery , a temperature sensor to measure the temperature of the power battery, a current sensor to measure the charging current of the power battery, and a voltage sensor to measure the discharge voltage of the rechargeable battery, wherein the microcontroller is connected to the temperature sensor, the current sensor and the voltage sensor respectively. After a mathematical model for predicting the state of charge of the power battery and the weight of each state of charge are obtained, the state of charge value of the power battery is obtained through a weighted summation algorithm. The present invention improves the accuracy of describing the charging and discharging characteristics of the power battery.
Description
技术领域technical field
本发明涉及动力电池健康状态领域，具体地，涉及动力电池SOC预测装置、汽车及方法。The present invention relates to the field of power battery state of health, and in particular, to a power battery SOC prediction device, vehicle and method.
背景技术Background technique
精确测量动力电池的荷电状态对动力电池的能量管理与电池保护具有重要意义。但是电池随着电池使用时间的增长其内阻会增大，电池的容量也会随之降低。且电池的荷电状态与环境温度以及充放电电流之间存在复杂的非线性关系，这使得难以建立精确的数学模型对动力电池的充放电特性加以描述。也正是由于数学模型的精度问题，限制了传统的卡尔曼滤波在工程中应用。Accurately measuring the state of charge of the power battery is of great significance to the energy management and battery protection of the power battery. However, as the battery life increases, its internal resistance will increase, and the battery capacity will also decrease. In addition, there is a complex nonlinear relationship between the state of charge of the battery, the ambient temperature and the charging and discharging current, which makes it difficult to establish an accurate mathematical model to describe the charging and discharging characteristics of the power battery. It is precisely because of the accuracy of the mathematical model that the application of the traditional Kalman filter in engineering is limited.
发明内容SUMMARY OF THE INVENTION
本发明的目的是提供一种动力电池SOC预测装置、汽车及方法，该动力电池SOC预测装置克服了现有技术中的无法建立精确的数学模型对动力电池的充放电特性加以描述的问题，提高了描述动力电池的充放电特性的精度。The purpose of the present invention is to provide a power battery SOC prediction device, vehicle and method, the power battery SOC prediction device overcomes the problem in the prior art that an accurate mathematical model cannot be established to describe the charging and discharging characteristics of the power battery, and improves the The accuracy of describing the charging and discharging characteristics of the power battery.
为了实现上述目的，本发明提供一种基于模糊多模型卡尔曼滤波的动力电池SOC预测装置，该动力电池SOC预测装置包括连接于动力电池的以下部件记录动力电池充放电时间的微控制器、测量动力电池温度的温度传感器、测量动力电池充电电流的电流传感器、测量充电电池放电电压的电压传感器，其中，所述微控制器分别连接于所述温度传感器、电流传感器和电压传感器，所述微控制器在建立多个预测动力电池的荷电状态的数学模型和得到每个荷电状态的权重之后，通过加权求和算法得到动力电池的荷电状态值。In order to achieve the above object, the present invention provides a power battery SOC prediction device based on fuzzy multi-model Kalman filtering, the power battery SOC prediction device includes the following components connected to the power battery to record the power battery charge and discharge time The microcontroller, the measurement A temperature sensor for the temperature of the power battery, a current sensor for measuring the charging current of the power battery, and a voltage sensor for measuring the discharge voltage of the rechargeable battery, wherein the microcontroller is respectively connected to the temperature sensor, the current sensor and the voltage sensor, and the microcontroller After establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge, the device obtains the state of charge value of the power battery through a weighted summation algorithm.
优选地，该动力电池SOC预测装置包括：所述控制器能够根据所测量的充电电流和放电电压建立多个数学模型，并通过扩展卡尔曼滤波法针对多个数学模型预测动力电池的荷电状态；所述微控制器能够根据动力电池充放电时间和动力电池温度模糊推理后得到每个数学模型预测动力电池的荷电状态的权重；通过多个数学模型预测动力电池的荷电状态及其权重的加权求和算法得到动力电池的荷电状态值。Preferably, the power battery SOC prediction device includes: the controller can establish a plurality of mathematical models according to the measured charging current and discharge voltage, and predict the state of charge of the power battery for the plurality of mathematical models through the extended Kalman filtering method The microcontroller can obtain the weight of each mathematical model to predict the state of charge of the power battery according to the charging and discharging time of the power battery and the temperature of the power battery after fuzzy inference; predict the state of charge of the power battery and its weight through multiple mathematical models The weighted summation algorithm of the power battery obtains the state of charge value of the power battery.
优选地，所述微控制器还连接有带电可擦可编程只读存储器，在所述带电可擦可编程只读存储器中记录有动力电池的放电总时间。Preferably, the microcontroller is further connected with an electrified erasable programmable read-only memory, and the electrified erasable and programmable read-only memory records the total discharge time of the power battery.
优选地，该动力电池SOC预测装置还包括电源，所述电源连接于所述微控制器、温度传感器、电流传感器和电压传感器，以提供工作电压。Preferably, the power battery SOC prediction device further includes a power source, and the power source is connected to the microcontroller, the temperature sensor, the current sensor and the voltage sensor to provide a working voltage.
优选地，该动力电池SOC预测装置还包括输入端连接于所述微控制器的CAN控制器，所述CAN控制器的输出端连接于车载ECU。Preferably, the power battery SOC prediction device further includes a CAN controller whose input end is connected to the microcontroller, and an output end of the CAN controller is connected to the vehicle-mounted ECU.
本发明还提供一种汽车，该汽车包括上述基于模糊多模型卡尔曼滤波的动力电池SOC预测装置。The present invention also provides an automobile, which includes the above-mentioned device for predicting the SOC of a power battery based on the fuzzy multi-model Kalman filter.
本发明还提供一种基于模糊多模型卡尔曼滤波的动力电池SOC预测方法，该动力电池SOC预测方法包括在建立多个预测动力电池的荷电状态的数学模型和得到每个荷电状态的权重之后，通过加权求和算法得到动力电池的荷电状态值。The present invention also provides a power battery SOC prediction method based on fuzzy multi-model Kalman filtering, the power battery SOC prediction method includes establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge After that, the state of charge value of the power battery is obtained through a weighted summation algorithm.
优选地，在建立多个预测动力电池的荷电状态的数学模型和得到每个荷电状态的权重之后，包括：Preferably, after establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge, the method includes:
根据测量的动力电池的充电电流和放电电压建立多个数学模型，并通过扩展卡尔曼滤波法针对多个数学模型预测动力电池的荷电状态；According to the measured charging current and discharging voltage of the power battery, multiple mathematical models are established, and the state of charge of the power battery is predicted for the multiple mathematical models by the extended Kalman filter method;
根据动力电池充放电时间和动力电池温度模糊推理后得到每个数学模型预测动力电池的荷电状态的权重。The weight of each mathematical model to predict the state of charge of the power battery is obtained after fuzzy inference according to the charging and discharging time of the power battery and the temperature of the power battery.
优选地，通过加权求和算法得到动力电池的荷电状态值，包括：Preferably, the state of charge value of the power battery is obtained through a weighted summation algorithm, including:
通过多个数学模型预测动力电池的荷电状态及其权重的加权求和算法得到动力电池的荷电状态值。The state of charge value of the power battery is obtained through a weighted summation algorithm that predicts the state of charge of the power battery and its weight through multiple mathematical models.
通过上述技术方案，本发明针对不同阶段、不同环境下的电池分别建立数学模型描述其状态。并通过卡尔曼滤波方法针对每个数学模型，估计电池的荷电状态。然后通过模糊推理确定每个模型预测结果的权重，对每个预测结果进行加权求和，提高动力电池荷电状态的最终预测精度。Through the above technical solutions, the present invention establishes mathematical models to describe the states of batteries in different stages and in different environments. And for each mathematical model, the state of charge of the battery is estimated by the Kalman filter method. Then, the weight of each model prediction result is determined by fuzzy reasoning, and each prediction result is weighted and summed to improve the final prediction accuracy of the power battery state of charge.
本发明的其它特征和优点将在随后的具体实施方式部分予以详细说明。Other features and advantages of the present invention will be described in detail in the detailed description that follows.
附图说明Description of drawings
附图是用来提供对本发明的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本发明，但并不构成对本发明的限制。在附图中：The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and together with the following specific embodiments, are used to explain the present invention, but do not constitute a limitation to the present invention. In the attached image:
图1是本发明的一种优选实施方式的基于模糊多模型卡尔曼滤波的动力电池SOC预测装置的结构流程图；1 is a structural flowchart of a power battery SOC prediction device based on fuzzy multi-model Kalman filtering according to a preferred embodiment of the present invention;
图2是本发明的一种优选实施方式的基于模糊多模型卡尔曼滤波的动力电池SOC预测方法的流程图。FIG. 2 is a flowchart of a method for predicting the SOC of a power battery based on a fuzzy multi-model Kalman filter according to a preferred embodiment of the present invention.
具体实施方式Detailed ways
以下结合附图对本发明的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本发明，并不用于限制本发明。The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, but not to limit the present invention.
本发明提供一种基于模糊多模型卡尔曼滤波的动力电池SOC预测装置，如图1所示，该动力电池SOC预测装置包括连接于动力电池的以下部件记录动力电池充放电时间的微控制器、测量动力电池温度的温度传感器、测量动力电池充电电流的电流传感器、测量充电电池放电电压的电压传感器，其中，所述微控制器分别连接于所述温度传感器、电流传感器和电压传感器，所述微控制器在建立多个预测动力电池的荷电状态的数学模型和得到每个荷电状态的权重之后，通过加权求和算法得到动力电池的荷电状态值。The present invention provides a power battery SOC prediction device based on fuzzy multi-model Kalman filtering. As shown in FIG. 1 , the power battery SOC prediction device includes a microcontroller connected to the power battery for recording the charging and discharging time of the power battery, A temperature sensor for measuring the temperature of the power battery, a current sensor for measuring the charging current of the power battery, and a voltage sensor for measuring the discharge voltage of the rechargeable battery, wherein the microcontroller is respectively connected to the temperature sensor, the current sensor and the voltage sensor, and the microcontroller is connected to the temperature sensor, the current sensor and the voltage sensor respectively. After establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge, the controller obtains the state of charge value of the power battery through a weighted summation algorithm.
通过上述技术方案，本发明针对不同阶段、不同环境下的电池分别建立数学模型描述其状态。并通过卡尔曼滤波方法针对每个数学模型，估计电池的荷电状态。然后通过模糊推理确定每个模型预测结果的权重，对每个预测结果进行加权求和，提高动力电池荷电状态的最终预测精度。所述的微处理器可以使用stm32c8t6。Through the above technical solutions, the present invention establishes mathematical models to describe the states of batteries in different stages and in different environments. And for each mathematical model, the state of charge of the battery is estimated by the Kalman filter method. Then, the weight of each model prediction result is determined by fuzzy reasoning, and each prediction result is weighted and summed to improve the final prediction accuracy of the power battery state of charge. Said microprocessor can use stm32c8t6.
其中，温度传感器、电流传感器和电压传感器分别连接于动力电池，以将所测得的动力电池值分别反馈至微控制器，以方便微控制器执行下一步的操作，最终可以得到较为精确的荷电状态值。Among them, the temperature sensor, the current sensor and the voltage sensor are respectively connected to the power battery, so as to feed back the measured power battery value to the microcontroller respectively, so as to facilitate the microcontroller to perform the next operation, and finally a more accurate load can be obtained. electrical state value.
在本发明的一种具体实施方式中，该动力电池SOC预测装置可以包括：所述控制器能够根据所测量的充电电流和放电电压建立多个数学模型，并通过扩展卡尔曼滤波法针对多个数学模型预测动力电池的荷电状态；所述微控制器能够根据动力电池充放电时间和动力电池温度模糊推理后得到每个数学模型预测动力电池的荷电状态的权重；通过多个数学模型预测动力电池的荷电状态及其权重的加权求和算法得到动力电池的荷电状态值。In a specific embodiment of the present invention, the power battery SOC prediction device may include: the controller can establish a plurality of mathematical models according to the measured charging current and discharge voltage, and use the extended Kalman filtering method to target the plurality of mathematical models. The mathematical model predicts the state of charge of the power battery; the microcontroller can obtain the weight of each mathematical model to predict the state of charge of the power battery after fuzzy inference according to the charging and discharging time of the power battery and the temperature of the power battery; predicting through multiple mathematical models The weighted summation algorithm of the state of charge of the power battery and its weights obtains the state of charge value of the power battery.
通过上述的实施方式，微控制器中需要集成大量的运算模块，该集成的运算模块可以实现建立模型，加权运算等功能，最终可以实现对于动力电池SOC预测。Through the above-mentioned embodiments, a large number of operation modules need to be integrated in the microcontroller, and the integrated operation modules can realize functions such as model building, weighting operation, etc., and finally can realize the SOC prediction of the power battery.
在本发明的一种具体实施方式中，为了实现总放电时间的存储，所述微控制器还连接有带电可擦可编程只读存储器，在所述带电可擦可编程只读存储器中记录有动力电池的放电总时间。In a specific embodiment of the present invention, in order to realize the storage of the total discharge time, the microcontroller is further connected with a charged erasable programmable read-only memory, and recorded in the charged erasable programmable read-only memory The total discharge time of the power battery.
其中，带电可擦可编程只读存储器为最优选的存储器，其目的在于可以在断电情况下，继续对放电总时间进行存储。Among them, the electrified erasable programmable read-only memory is the most preferred memory, and its purpose is to continue to store the total discharge time in the event of a power failure.
在本发明的一种具体实施方式中，该动力电池SOC预测装置还可以包括电源，所述电源连接于所述微控制器、温度传感器、电流传感器和电压传感器，以提供工作电压。In a specific embodiment of the present invention, the power battery SOC prediction apparatus may further include a power source, and the power source is connected to the microcontroller, the temperature sensor, the current sensor and the voltage sensor to provide an operating voltage.
通过上述的实施方式，可以实现微控制器、温度传感器、电流传感器和电压传感器的供电，可以实现电量的供给，可以实现工作电压的提供。Through the above-mentioned embodiments, the power supply of the microcontroller, the temperature sensor, the current sensor and the voltage sensor can be realized, the supply of electric power can be realized, and the supply of the working voltage can be realized.
在本发明的一种具体实施方式中，该动力电池SOC预测装置还可以包括输入端连接于所述微控制器的CAN控制器，所述CAN控制器的输出端连接于车载ECU。In a specific embodiment of the present invention, the power battery SOC prediction device may further include a CAN controller whose input end is connected to the microcontroller, and an output end of the CAN controller is connected to the vehicle-mounted ECU.
通过上述的实施方式，可以将本发明用在汽车上，从而可以使得车载ECU读取电池的电量，确保检测的可视化效果。Through the above-mentioned embodiments, the present invention can be used in automobiles, so that the vehicle-mounted ECU can read the power of the battery and ensure the visual effect of detection.
在本发明的一种最优选的实施方式中，针对不同使用寿命的电池，装置中电流传感器采集动力电池的电流为输入量，电压传感器采集电池的端电压为输出量，在微处理器中分别建立n个数学模型描述电池充放电特性。In a most preferred embodiment of the present invention, for batteries with different service lives, the current sensor in the device collects the current of the power battery as the input quantity, and the voltage sensor collects the terminal voltage of the battery as the output quantity. Establish n mathematical models to describe the battery charge and discharge characteristics.
Xi(k)＝fi(X(k-1))+Wi(k)；X i (k)=f i (X(k-1))+W i (k);
Yi(k)＝Hi(k)X(k)+Vi(k)；Y i (k)=H i (k)X(k)+V i (k);
其中(i＝1,2,…n)。where (i=1,2,...n).
然后分别使用扩展卡尔曼滤波法针对n个电池模型预测电池的荷电状态，得到n个计算结果Xi(k|k)。Then, the extended Kalman filter method is used to predict the state of charge of the battery for n battery models, and n calculation results X i (k|k) are obtained.
装置中通过温度传感器采集温度T。装置通过微处理器记录下电池的放电总时间t，并记录在EEPROM中，使其断电信息不丢失。装置中电源给微处理器及传感器供电。The temperature T is collected by a temperature sensor in the device. The device records the total discharge time t of the battery through the microprocessor, and records it in the EEPROM, so that the power-off information is not lost. The power supply in the device supplies power to the microprocessor and the sensor.
然后根据当前电池的放电总时间t、环境温度T进行模糊推理，获得每个模型的预测结果的权重αi。最终结果为
测量完成后，装置通过CAN控制器将最终测量结果通过CAN控制器发送到网络，供车载ECU使用。After the measurement is completed, the device sends the final measurement result to the network through the CAN controller for use by the on-board ECU.
本发明还提供一种汽车，该汽车包括根据上述基于模糊多模型卡尔曼滤波的动力电池SOC预测装置。The present invention also provides an automobile, which includes the power battery SOC prediction device based on the above-mentioned fuzzy multi-model Kalman filter.
其中，汽车可以是新能源汽车又或者是混合动力汽车，只要能实现动力电池的电能供给就行。Among them, the vehicle can be a new energy vehicle or a hybrid vehicle, as long as the power supply of the power battery can be realized.
除上述所述之外，本发明还提供一种基于模糊多模型卡尔曼滤波的动力电池SOC预测方法，该动力电池SOC预测方法可以包括在建立多个预测动力电池的荷电状态的数学模型和得到每个荷电状态的权重之后，通过加权求和算法得到动力电池的荷电状态值。In addition to the above, the present invention also provides a power battery SOC prediction method based on fuzzy multi-model Kalman filtering, the power battery SOC prediction method may include establishing a plurality of mathematical models for predicting the state of charge of the power battery and After the weight of each state of charge is obtained, the state of charge value of the power battery is obtained through the weighted summation algorithm.
通过上述的实施方式，可以实现荷电状态的预测，可以最终得到动力电池额荷电状态，该方法可以提高动力电池荷电状态的最终预测精度。Through the above-mentioned embodiments, the prediction of the state of charge can be realized, and the rated state of charge of the power battery can be finally obtained, and the method can improve the final prediction accuracy of the state of charge of the power battery.
在该种实施方式中，如上所述，在建立多个预测动力电池的荷电状态的数学模型和得到每个荷电状态的权重之后，包括：In this embodiment, as described above, after establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge, the method includes:
根据测量的动力电池的充电电流和放电电压建立多个数学模型，并通过扩展卡尔曼滤波法针对多个数学模型预测动力电池的荷电状态；According to the measured charging current and discharging voltage of the power battery, multiple mathematical models are established, and the state of charge of the power battery is predicted for the multiple mathematical models by the extended Kalman filter method;
根据动力电池充放电时间和动力电池温度模糊推理后得到每个数学模型预测动力电池的荷电状态的权重。The weight of each mathematical model to predict the state of charge of the power battery is obtained after fuzzy inference according to the charging and discharging time of the power battery and the temperature of the power battery.
在该种实施方式中，通过加权求和算法得到动力电池的荷电状态值，包括：In this embodiment, the state of charge value of the power battery is obtained through a weighted summation algorithm, including:
通过多个数学模型预测动力电池的荷电状态及其权重的加权求和算法得到动力电池的荷电状态值。The state of charge value of the power battery is obtained through a weighted summation algorithm that predicts the state of charge of the power battery and its weight through multiple mathematical models.
通过上述的方法，可以实现加权求和的方式，得到荷电状态值，最终可以确保后续的使用。Through the above method, the method of weighted summation can be implemented to obtain the state of charge value, which can finally ensure subsequent use.
以上结合附图详细描述了本发明的优选实施方式，但是，本发明并不限于上述实施方式中的具体细节，在本发明的技术构思范围内，可以对本发明的技术方案进行多种简单变型，这些简单变型均属于本发明的保护范围。The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above-mentioned embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, These simple modifications all belong to the protection scope of the present invention.
另外需要说明的是，在上述具体实施方式中所描述的各个具体技术特征，在不矛盾的情况下，可以通过任何合适的方式进行组合，为了避免不必要的重复，本发明对各种可能的组合方式不再另行说明。In addition, it should be noted that the specific technical features described in the above-mentioned specific embodiments can be combined in any suitable manner unless they are inconsistent. In order to avoid unnecessary repetition, the present invention provides The combination method will not be specified otherwise.
此外，本发明的各种不同的实施方式之间也可以进行任意组合，只要其不违背本发明的思想，其同样应当视为本发明所公开的内容。In addition, the various embodiments of the present invention can also be combined arbitrarily, as long as they do not violate the spirit of the present invention, they should also be regarded as the contents disclosed in the present invention.
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