CN112613128A - Prediction method, device, equipment, vehicle and storage medium of endurance mileage - Google Patents
Prediction method, device, equipment, vehicle and storage medium of endurance mileage Download PDFInfo
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Abstract
The application discloses a prediction method, a prediction device, equipment and a storage medium of endurance mileage, wherein the method comprises the following steps: calculating to obtain a speed curve of the vehicle based on road information, wherein the speed curve is a curve of speeds of the vehicle in N travel sections from the current moment, the road information is information used for indicating the environmental conditions of the road on each travel section, N is a natural number, and N is not less than 1; calculating the driving power of the vehicle according to the road information and the speed curve, wherein the driving power is the power of equipment and/or devices which are arranged on the vehicle and participate in driving the vehicle to run; calculating accessory power of the vehicle, wherein the accessory power is power of accessories equipped on the vehicle, and the accessories are devices and/or devices which do not participate in driving the vehicle to run; calculating a historical energy consumption rate of the vehicle; calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power; and calculating the driving range of the vehicle according to the energy consumption rate.
Description
Technical Field
The application relates to the technical field of vehicle control, in particular to a method, a device, equipment, a vehicle and a storage medium for predicting endurance mileage.
Background
The new energy vehicle is a vehicle which adopts unconventional vehicle fuel as a power source (or uses conventional vehicle fuel and adopts a novel vehicle-mounted power device), integrates advanced technologies in the aspects of power control and driving of the vehicle, forms an advanced technical principle, has a new technology and a new structure, and comprises a Hybrid Electric Vehicle (HEV), a pure electric vehicle (BEV), a Fuel Cell Electric Vehicle (FCEV), and the like.
For a new energy vehicle, the prediction capability of the endurance mileage is an important index for evaluating the new energy vehicle, and the prediction capability is the total mileage of the new energy vehicle which can continuously run under the maximum energy reserve. In the related art, the method for predicting the range of the new energy vehicle is based on the evaluation of driving of a driver according to a fixed driving cycle, and the accuracy of prediction is low due to few factors (especially, the influence of the environment on energy consumption is not considered).
Disclosure of Invention
The application provides a prediction method, a prediction device, equipment, a vehicle and a storage medium of endurance mileage, which can solve the problem of low prediction accuracy of a prediction method of vehicle mileage provided in the related technology.
In one aspect, an embodiment of the present application provides a method for predicting a driving mileage, including:
calculating a speed curve of a vehicle based on road information, wherein the speed curve is a curve of speeds of the vehicle in N travel sections from the current moment, the road information is information used for indicating the environmental conditions of roads on each travel section, N is a natural number, and N is more than or equal to 1;
calculating the driving power of the vehicle according to the road information and the speed curve, wherein the driving power is the power of equipment and/or devices which are arranged on the vehicle and participate in driving the vehicle to run;
calculating an accessory power of the vehicle, the accessory power being a power of an accessory equipped on the vehicle, the accessory being a device and/or a device that does not participate in driving the vehicle;
calculating a historical energy consumption rate of the vehicle, the historical energy consumption rate being an energy consumption rate calculated from historical energy consumption of the vehicle;
calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power;
and calculating the driving mileage of the vehicle according to the energy consumption rate.
Optionally, the road information includes a traffic flow speed on each of the travel sections.
Optionally, the calculating a speed curve of the vehicle based on the road information includes:
and calling a speed prediction model to process the current speed of the vehicle and the traffic flow speed to obtain the speed curve, wherein the speed prediction model is a machine learning model obtained by training according to at least one first training set, and the first training set comprises a sample current speed, a sample traffic flow speed and an actual speed curve corresponding to the sample current speed and the sample traffic flow speed.
Optionally, the road information includes gradient information on each of the travel sections.
Optionally, the calculating the driving power of the vehicle according to the road information and the speed curve includes:
and calculating the driving power according to the speed curve and the gradient information.
Optionally, the calculating accessory power of the vehicle includes:
calculating a switch curve of the accessory and a target temperature of a cab of the vehicle according to the ambient temperature of the environment where the vehicle is located, wherein the switch curve is a curve of the accessory which is turned on and off within the preset time from the current moment, and the target temperature is the temperature in the cab set by the driver;
and calculating the accessory power according to the switching curve, the target temperature and the environment temperature.
Optionally, the calculating the accessory power according to the switching curve, the target temperature, and the ambient temperature includes:
calculating the transient power of the accessory according to the switch curve, the target temperature and the environment temperature, wherein the transient power is the power consumed by the accessory to enable the temperature of the cab to reach the target temperature;
calculating the steady-state power of the accessory according to the environment temperature, wherein the steady-state power is the power consumed by the accessory for maintaining the temperature of the cab at the target temperature;
and calculating the accessory power according to the transient power and the steady-state power.
Optionally, the calculating the switch curve of the accessory and the target temperature of the cab of the vehicle according to the ambient temperature includes:
and calling an accessory using habit model to process the environment temperature to obtain the switch curve and the target temperature, wherein the accessory using habit model is a machine learning model obtained by training according to at least one group of second training group, and the second training group comprises the sample environment temperature and an actual switch curve and an actual target temperature of the accessory corresponding to the sample environment temperature.
Optionally, the calculating the driving range of the vehicle according to the energy consumption rate includes:
and calling a battery model to process the energy consumption rate to obtain the endurance mileage, wherein the battery model is a physical model obtained based on resistance-capacitance (RC) model modeling.
Optionally, the historical energy consumption rate includes historical driving power and historical accessory power of the vehicle.
On the other hand, the embodiment of the present application provides a device for predicting a driving range, including:
the speed prediction module is used for calculating to obtain a speed curve of the vehicle based on road information, the speed curve is a curve of speeds of the vehicle in N travel sections from the current moment, the road information is information used for indicating the environmental conditions of roads on each travel section, N is a natural number, and N is more than or equal to 1;
the energy consumption prediction module is used for calculating the driving power of the vehicle according to the road information and the speed curve, wherein the driving power is the power of equipment and/or devices which are arranged on the vehicle and participate in driving the vehicle to run; calculating an accessory power of the vehicle, the accessory power being a power of an accessory equipped on the vehicle, the accessory being a device and/or a device that does not participate in driving the vehicle; calculating a historical energy consumption rate of the vehicle, the historical energy consumption rate being an energy consumption rate calculated from historical energy consumption of the vehicle; calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power;
and the endurance prediction module is used for calculating the endurance mileage of the vehicle according to the energy consumption rate.
In another aspect, the present application provides a computer device, where the computer device includes a processor and a memory, where the memory stores at least one instruction or program, and the instruction or program is loaded and executed by the processor to implement the method for predicting driving range as described in any one of the above.
Optionally, the apparatus is provided in a vehicle.
In another aspect, an embodiment of the present application provides a vehicle, which includes the computer device described above.
In another aspect, an embodiment of the present application provides a computer-readable storage medium, where at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the method for predicting driving range as described in any one of the above.
The technical scheme at least comprises the following advantages:
the method comprises the steps of obtaining a speed curve of a vehicle through road information calculation, obtaining driving power of the vehicle through the road information calculation and the speed curve calculation, obtaining an energy consumption rate through the driving power, accessory power and historical energy loss rate calculation, and obtaining the endurance mileage of the vehicle through the energy consumption rate calculation.
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In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic illustration of an implementation environment of a method for predicting driving range according to an exemplary embodiment of the present disclosure;
FIG. 2 is a flow chart of a method for predicting range provided by an exemplary embodiment of the present application;
FIG. 3 is a flow chart of a method for predicting range provided by an exemplary embodiment of the present application;
FIG. 4 is a modeling diagram of a battery model provided by an exemplary embodiment of the present application;
FIG. 5 is a block diagram of a mileage predicting apparatus provided in an exemplary embodiment of the present application;
FIG. 6 is a block diagram of a computer device provided in an exemplary embodiment of the present application.
Detailed Description
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; the connection can be mechanical connection or electrical connection; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In addition, the technical features mentioned in the different embodiments of the present application described below may be combined with each other as long as they do not conflict with each other.
Referring to fig. 1, which is a schematic diagram illustrating an implementation environment of a method for predicting driving range according to an exemplary embodiment of the present application, as shown in fig. 1, the implementation environment includes a control device 111 in a vehicle 110 and a server 120, and the control device 111 may establish a communication connection with the server 120 through a wireless network through a communication device 112 equipped in the vehicle 110.
The wireless network may be a communication network based on a Zigbee protocol (Zigbee), a wireless fidelity (Wi-Fi), or a mobile network (e.g., a third generation mobile network (3G) network, a Long Term Evolution (LTE) network, or a fifth generation mobile network (5G) network).
A control device 111 provided in the vehicle 110 acquires vehicle information, and transmits the vehicle information to the server 120 through a communication device 112; the server 120 calculates the driving range of the vehicle 110 according to the vehicle information and the road information by using any one of the following method embodiments, transmits the information of the driving range to the communication device 112, and the communication device 112 transmits the information of the driving range to the control device 111. Where the road information is information indicating an environmental condition of the road on each of N travel sections of the vehicle from the present time, which may include, for example, a traffic flow speed on each travel section, or a traffic flow speed and gradient information on each travel section, N being a natural number, N ≧ 1.
Among them, the control device 111 may be an Electronic Control Unit (ECU) equipped in the vehicle 110, and the communication device 112 may be a Tbox, a smart gateway, or other off-board communication device equipped in the vehicle 110. For example, the control device 111 may be a Vehicle Control Unit (VCU) in the vehicle 110.
Illustratively, an Application (APP) for predicting the driving range is integrated in the control device 111, an operation platform of the application is integrated in the server 120, the control device 111 transmits vehicle information to the server 120 through the communication device 112 by running the application, the server 120 calculates the driving range based on an algorithm model in the operation platform according to the vehicle information and the road information, and transmits the information of the driving range to the communication device 112, the communication device 112 transmits the information of the driving range to the control device 111, and the control device 111 can display the driving range to a driver in the vehicle 110 through a user interface in the application and/or prompt the driving range to the driver in the vehicle 110 through voice.
In an alternative embodiment, as shown in fig. 1, the control device 111 receives the road information sent by the server 120 through the communication device 112, calculates the driving range of the vehicle 110 according to the acquired vehicle information and the road information by using any one of the following method embodiments, displays the driving range to the driver in the vehicle 110, and/or prompts the driving range to the driver in the vehicle 110 through voice.
Among them, the control device 111 may be an electronic controller equipped in the vehicle 110, and the communication device 112 may be a Tbox, a smart gateway, or other off-board communication device equipped in the vehicle 110. For example, control device 111 may be a vehicle control unit in vehicle 110.
Referring to fig. 2, which shows a flowchart of a method for predicting driving range provided by an exemplary embodiment of the present application, the method may be performed by the server 120 or the control device 111 in the embodiment of fig. 1, and the method includes:
in step 201, a speed profile of the vehicle is calculated based on road information, the speed profile being a profile of the speed of the vehicle within N travel sections from the current time, and the road information being information indicating an environmental condition of a road on each travel section.
If the method is executed by the control device 111 in the embodiment of fig. 1, the road information is sent to the communication device 112 by the server 120, and then forwarded to the control device 111 by the communication device 112.
Wherein the road information includes gradient information on each trip section of the vehicle, or traffic speed and gradient information on each trip section; optionally, the traffic flow speed comprises an average traffic flow speed for each travel segment. The driving route of the vehicle may be divided into N travel links, and the driving route may be a driving route set by a driver or a predicted driving route, which is not limited herein.
For example, when the gradient of the travel section 1 is large, the speed of the vehicle traveling on the travel section 1 can be predicted to be small according to the gradient information of the travel section 1, and when the travel section 2 has a congestion phenomenon, the speed of the vehicle traveling on the travel section 2 can be predicted to be small together with the traffic flow speed of the travel section 2. By taking into account the road information in predicting the speed profile, the speed profile can be calculated more accurately.
And step 202, calculating the driving power of the vehicle according to the road information and the speed curve, wherein the driving power is the power of equipment and/or devices which are arranged on the vehicle and participate in driving the vehicle to run.
The driving power is the power contributed by the devices and/or means involved in driving the vehicle, for example, at least one of the engine, generator and motor of the vehicle, and the devices and/or means associated with the at least one device. Since the driving power is related to the speed of the vehicle, and the speed profile used in calculating the driving power is predicted in consideration of the road information, the accuracy of the calculated driving power is high.
Optionally, in step 202, the driving power of the vehicle may be calculated by the gradient information in the road information and the speed curve.
In step 203, accessory power of the vehicle is calculated, wherein the accessory power is the power of an accessory equipped on the vehicle, and the accessory is a device and/or a device which does not participate in driving the vehicle to run.
If the method is executed by the server 120 in the embodiment of fig. 1, the vehicle data required for calculating the accessory power is acquired by the control device 111 in the embodiment of fig. 1 and then sent to the server 120 through the communication device 112, and the server calculates the accessory power of the vehicle according to the vehicle data.
Wherein the accessory may comprise at least one of: air conditioning apparatus, entertainment apparatus, devices associated with air conditioning apparatus, and devices associated with entertainment apparatus.
Optionally, the accessory power includes a transient power and a steady-state power, the transient power is a power consumed by the accessory to make the temperature of the cabin reach a target temperature, the steady-state power is a power consumed by the accessory to maintain the temperature of the cabin at the target temperature, and the target temperature is a predicted temperature in the cabin set by the driver within a predetermined time from the current time.
Optionally, step 203 includes but is not limited to: calculating a switch curve of the accessory and a target temperature of a cab of the vehicle according to the ambient temperature of the vehicle, wherein the switch curve is a curve of the accessory which is turned on and off within a preset time from the current moment; and calculating the accessory power according to the switching curve, the target temperature and the ambient temperature.
In step 204, a historical energy consumption rate of the vehicle is calculated, the historical energy consumption rate being an energy consumption rate calculated from the historical consumption of the vehicle.
If the method is executed by the server 120 in the embodiment of fig. 1, the vehicle data required for calculating the historical energy consumption rate is acquired by the control device 111 in the embodiment of fig. 1, and then is sent to the server 120 through the communication device 112; alternatively, the control device 111 acquires vehicle data required for obtaining the historical energy consumption rate, calculates the historical energy consumption rate from the vehicle data, and transmits the calculated historical energy consumption rate to the server 120 through the communication device 112.
Wherein the historical energy consumption rate may include historical drive power of the vehicle and historical accessory power of the vehicle.
And step 205, calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power.
Because the accessory power is considered in the calculation of the energy consumption rate of the vehicle, the calculated energy consumption rate is closer to the actual energy consumption rate of the vehicle, and the accuracy of the calculation of the energy consumption rate of the vehicle is improved; meanwhile, historical energy consumption rate is considered in the calculation of the energy consumption of the vehicle, so that the energy consumption rate can be complemented with the calculated accessory power and driving power, and the calculation accuracy of the energy consumption rate is further improved.
And step 206, calculating the endurance mileage of the vehicle according to the energy consumption rate.
For example, the mileage can be calculated in various ways, for example, the relationship between the mileage and the energy consumption rate can be fitted by a least square method, and the mileage is calculated according to the energy consumption rate; or, a battery model is established, and the endurance mileage is obtained by calling the battery model to process the capacity consumption rate, which is not limited herein.
In summary, in the embodiment of the application, the speed curve of the vehicle is obtained through the road information calculation, the driving power of the vehicle is obtained through the road information calculation and the speed curve calculation, the energy consumption rate is obtained through the driving power, the accessory power and the historical energy consumption rate calculation, and therefore the driving range of the vehicle is obtained through the energy consumption rate calculation.
Referring to fig. 3, which shows a flowchart of a method for predicting driving range provided in an exemplary embodiment of the present application, the method may be executed by the server 120 or the control device 111 in the embodiment of fig. 1, and the method may be an optional implementation manner of the embodiment of fig. 2, and the method includes:
If the method is executed by the control device 111 in the embodiment of fig. 1, the road information sent by the server 120 may be received through the communication device 112, where the road information carries a traffic flow speed, and a current speed may be measured by a speed sensor equipped in the vehicle 110; if the method is performed by the server 120 in the embodiment of fig. 1, the current speed transmitted by the control device 111 may be received by the communication device 112, and the current speed may be measured by the control device 111 through a speed sensor equipped in the vehicle 110.
The speed prediction model is a machine learning model obtained by training according to at least one first training set, and the first training set comprises a sample current speed, a sample traffic flow speed, and an actual speed curve corresponding to the sample current speed and the sample traffic flow speed. Optionally, the velocity prediction model comprises a neural network (e.g., a chain neural network) model.
Optionally, the training method of the speed prediction model includes, but is not limited to: inputting the first training set into an original speed prediction model to obtain a first training result; for each first training group, comparing the first training result with a calibrated speed curve (namely an actual speed curve corresponding to the current speed of the sample and the traffic flow speed of the sample) to obtain a first calculation loss, wherein the first calculation loss is used for indicating an error between the first training result and the calibrated speed curve; and training by adopting an error back propagation algorithm according to the first calculation loss corresponding to each of at least one group of first training groups to obtain a speed prediction model.
And step 302, calculating the driving power of the vehicle according to the speed curve and the gradient information.
For example, the driving power may be obtained by processing the speed curve and the gradient information by calling a driving power prediction model, which may be a physical model.
And step 303, calculating to obtain a switching curve of the accessory and a target temperature of a cab of the vehicle according to the environment temperature of the environment where the vehicle is located.
If the method is executed by the control device 111 in the embodiment of fig. 1, the ambient temperature may be measured by a temperature sensor equipped in the vehicle 110; if the method is performed by the server 120 in the embodiment of fig. 1, the ambient temperature sent by the control device 111 may be received by the communication device 112, and may be measured by the control device 111 through a temperature sensor equipped in the vehicle 110.
Optionally, step 303 includes, but is not limited to: and calling an accessory using habit model to process the environment temperature to obtain a switch curve and a target temperature, wherein the accessory using habit model is a machine learning model obtained by training according to at least one group of second training group, and the second training group comprises an actual switch curve and an actual target temperature of the accessory corresponding to the sample environment temperature and the sample environment temperature.
Optionally, the training method for the accessory using habit model includes but is not limited to: inputting the second training set into an original accessory use habit model to obtain a second training result; for each second training group, comparing the second training result with a calibrated target temperature (namely an actual switching curve of the accessory corresponding to the sample environment temperature and the actual target temperature) to obtain a second calculation loss, wherein the second calculation loss is used for indicating an error between the second training result and the calibrated target temperature; and training by adopting an error back propagation algorithm according to the second calculation loss corresponding to each of at least one group of second training groups to obtain an accessory use habit model.
And step 304, calculating the accessory power according to the switch curve, the target temperature and the environment temperature of the accessory.
Wherein the switch profile is a profile of turning on and off of the accessories for a predetermined time from the present time, the target temperature is a temperature in the cabin set by the driver predicted for the predetermined time from the present time, and the target temperature is normally set by the driver in the central control of the vehicle.
Optionally, step 304 includes, but is not limited to: calculating the transient power of the accessory according to the switching curve, the target temperature and the environment temperature, wherein the transient power is the power consumed by the accessory to enable the temperature of the cockpit to reach the target temperature; calculating the steady-state power of the accessory according to the ambient temperature, wherein the steady-state power is the power consumed by the accessory for maintaining the temperature of the cockpit as the target temperature; and calculating the accessory power according to the transient power and the steady-state power. The following methods of calculating transient power and steady state power are provided by way of example:
(1)transient power：
A whole vehicle thermal policy model can be established according to a vehicle cockpit physical model and a thermal management control strategy model provided by a whole vehicle factory, and a query map (map)1 of transient power related to environmental temperature and target temperature and a query map 2 of temperature reduction/temperature rise related to environmental temperature, target temperature and cooling/heating time (which can be obtained according to a switch curve) are calibrated by a simulation means.
In this embodiment, the method for calculating the transient power includes, but is not limited to: step S1, taking the ambient temperature as the initial temperature of the cockpit; step S2, obtaining the current required power through inquiring map 1 according to the environment temperature and the target temperature; step S3, obtaining the temperature of the next travel section through inquiring map 2 according to the environment temperature, the target temperature and the cooling/heating time of the next travel section; and (4) superposing the temperature of the temperature reduction/rise calculated in the step S3 on the temperature of the cockpit in the step S1 to obtain a new temperature of the cockpit, entering the step S2, repeatedly executing the steps until the required power of each route section in the N route sections is obtained, and obtaining a power curve which is the transient power according to the required power in each route section.
(2)Steady state power：
A whole vehicle thermal policy model can be established according to a vehicle cockpit physical model and a thermal management control strategy model provided by a whole vehicle factory. Through a simulation means, a query map 3 of the power with respect to the ambient temperature is calibrated, and according to the ambient temperature, a power curve obtained by obtaining the required power of each travel section through query of the query map 3 is the steady-state power.
At step 305, a historical energy consumption rate for the vehicle is calculated, the historical energy consumption rate including historical drive power and historical accessory power for the vehicle.
And step 306, calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power.
For example, the energy consumption rate of the vehicle may be derived by processing the historical energy consumption rate, the accessory power, and the drive power by means of weighted average filtering.
And 307, calling a battery model to process the energy consumption rate to obtain the endurance mileage, wherein the battery model is a physical model obtained based on RC model modeling.
In the following, the calculation of the driving range is explained by an exemplary embodiment:
referring to FIG. 4, a modeling diagram of a cell model including a battery and including a series resistance R according to an exemplary embodiment of the present application is shown1Capacitor C1And a parallel resistor R0The series-parallel RC circuit of (1).
The spatial state equation in the battery model shown in fig. 4 is:
v(k)＝OCV[z(k)]-R1iR1(k)-R0i(k)
wherein, it is made
In the above formula, ocv (open circuit voltage) is the open circuit voltage of the battery, R0Is a parallel resistor R0Resistance value of R1Is a series resistance R1Resistance value of C1Is a capacitor C1K is a natural number (k is more than or equal to 1), delta t is a discrete time step length, z is the SoC of the storage battery, eta is the coulombic efficiency of the storage battery, Q is the capacity (ampere) of the storage battery, i is the discharge current of the storage batteryR1To flow through a series resistance R1The discharge current of (1).
Based on this state space model, and in order to reduce the amount of computation, the applicant has designed an iterative calculation logic of the endurance mileage of the battery:
knowing that the state of charge (SoC) of the battery at the present time is z (0), the power consumption predicted for N future travel routes from the present time is P (1), P (2), … …, P (N) in this order, there are:
OCV＝OCV[z(n-1)]
R0＝R0[z(n-1)]
P＝P(n)
wherein, C0Is constant (e.g., it may be 3600).
If N is a natural number (N is less than or equal to N) and z (N) is less than or equal to the lower limit threshold of the SoC of the storage battery after the calculation of the steps, the displacement at the nth stroke section is the endurance mileage; otherwise, taking z (n) as the initial SoC value again, repeating the calculation of the steps until z (n ') is less than or equal to the lower limit threshold of the SoC of the storage battery, and the displacement corresponding to the nth' travel section is the endurance mileage.
If the method is executed by the server 120, the machine model and the physical model are stored in the server 120, and if the method is executed by the control device 111, the machine model may be stored in the control device 111 after training is completed.
Referring to fig. 5, a block diagram of a mileage predicting apparatus provided in an exemplary embodiment of the present application is shown, and the apparatus may be implemented as the server 120 or the control device 111 in the embodiment of fig. 1 through software, hardware, or a combination of the two. The device includes:
the speed prediction module 510 is configured to calculate a speed curve of the vehicle based on road information, where the speed curve is a curve of speeds of the vehicle within N travel sections from a current time, the road information is information indicating an environmental condition of a road on each travel section, N is a natural number, and N is greater than or equal to 1.
The energy consumption prediction module 520 is used for calculating the driving power of the vehicle according to the road information and the speed curve, wherein the driving power is the power of equipment and/or devices which are arranged on the vehicle and participate in driving the vehicle to run; calculating accessory power of the vehicle, wherein the accessory power is power of accessories equipped on the vehicle, and the accessories are devices and/or devices which do not participate in driving the vehicle to run; calculating a historical energy consumption rate of the vehicle, the historical energy consumption rate being an energy consumption rate calculated from the historical energy consumption of the vehicle; and calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power.
And the endurance prediction module 530 is used for calculating the endurance mileage of the vehicle according to the energy consumption rate.
Optionally, the road information includes a traffic flow speed on each travel segment.
Optionally, the speed prediction module 510 is further configured to invoke a speed prediction model to process the current speed and the traffic flow speed of the vehicle to obtain a speed curve, where the speed prediction model is a machine learning model obtained by training according to at least one first training set, and the first training set includes a sample current speed, a sample traffic flow speed, and an actual speed curve corresponding to the sample current speed and the sample traffic flow speed.
Optionally, the road information includes grade information on each travel segment.
Optionally, the energy consumption prediction module 520 is further configured to calculate the driving power according to the speed curve and the gradient information.
Optionally, the energy consumption predicting module 520 is further configured to calculate a switching curve of an accessory and a target temperature of the cabin of the vehicle according to an ambient temperature of an environment where the vehicle is located, where the switching curve of the accessory is turned on and off within a predetermined time from a current time, and the target temperature is a temperature in the cabin set by the driver; and calculating the accessory power according to the switching curve, the target temperature and the ambient temperature.
Optionally, the energy consumption predicting module 520 is further configured to calculate a transient power of the accessory according to the switching curve, the target temperature, and the ambient temperature, where the transient power is a power consumed by the accessory to enable the temperature of the cabin to reach the target temperature; calculating the steady-state power of the accessory according to the ambient temperature, wherein the steady-state power is the power consumed by the accessory for maintaining the temperature of the cockpit as the target temperature; and calculating the accessory power according to the transient power and the steady-state power.
Optionally, the energy consumption predicting module 520 is further configured to invoke an accessory using habit model to process the environment temperature to obtain a switching curve and a target temperature, where the accessory using habit model is a machine learning model obtained by training according to at least one second training set, and the second training set includes an actual switching curve and an actual target temperature of the accessory corresponding to the sample environment temperature and the environment temperature.
Optionally, the endurance prediction module 530 is further configured to invoke a battery model to process the energy consumption rate, so as to obtain endurance mileage, where the battery model is a physical model obtained based on an RC model.
Optionally, the historical energy consumption rate includes historical drive power and historical accessory power of the vehicle.
Referring to FIG. 6, a block diagram of a computer device provided by an exemplary embodiment of the present application is shown. The control device may be the server 120 or the control device 111 in the embodiment of fig. 1, which includes: a processor 610, and a memory 620.
The processor 610 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP. The processor 610 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
The memory 620 is connected to the processor 610 via a bus or other means, and the memory 620 has stored therein at least one instruction, at least one program, set of codes, or set of instructions that are loaded and executed by the processor 610 to implement any of the above method embodiments. The memory 620 may be a volatile memory (or a nonvolatile memory), a non-volatile memory (or a combination thereof). The volatile memory may be a random-access memory (RAM), such as a static random-access memory (SRAM) or a dynamic random-access memory (DRAM). The nonvolatile memory may be a Read Only Memory (ROM), such as a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), and an Electrically Erasable Programmable Read Only Memory (EEPROM). The non-volatile memory may also be a flash memory, a magnetic memory, such as a magnetic tape, a floppy disk, or a hard disk. The non-volatile memory may also be an optical disc.
The present application further provides a computer-readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by the processor to implement the method of predicting driving range as in any of the above embodiments.
The present application also provides a computer program product, which when run on a computer, causes the computer to execute the method for predicting driving range provided by the above method embodiments.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of this invention are intended to be covered by the scope of the invention as expressed herein.
Claims (15)
1. A method for predicting driving mileage, comprising:
calculating a speed curve of the vehicle based on road information, wherein the speed curve is a curve of speeds of the vehicle in N travel sections from the current moment, the road information is information used for indicating the environmental conditions of the road on each travel section, N is a natural number, and N is more than or equal to 1;
calculating the driving power of the vehicle according to the road information and the speed curve, wherein the driving power is the power of equipment and/or devices which are arranged on the vehicle and participate in driving the vehicle to run;
calculating an accessory power of the vehicle, the accessory power being a power of an accessory equipped on the vehicle, the accessory being a device and/or a device that does not participate in driving the vehicle;
calculating a historical energy consumption rate of the vehicle, the historical energy consumption rate being an energy consumption rate calculated from historical energy consumption of the vehicle;
calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power;
and calculating the driving mileage of the vehicle according to the energy consumption rate.
2. The method of claim 1, wherein the road information comprises a traffic flow velocity on the each travel segment.
3. The method of claim 2, wherein the calculating a speed profile of the vehicle based on the road information comprises:
and calling a speed prediction model to process the current speed of the vehicle and the traffic flow speed to obtain the speed curve, wherein the speed prediction model is a machine learning model obtained by training according to at least one first training set, and the first training set comprises a sample current speed, a sample traffic flow speed and an actual speed curve corresponding to the sample current speed and the sample traffic flow speed.
4. The method of claim 1, wherein the road information comprises grade information on the each travel segment.
5. The method of claim 4, wherein the calculating the driving power of the vehicle from the road information and the speed profile comprises:
and calculating the driving power according to the speed curve and the gradient information.
6. The method of claim 1, wherein the calculating accessory power for the vehicle comprises:
calculating a switch curve of the accessory and a target temperature of a cab of the vehicle according to the ambient temperature of the environment where the vehicle is located, wherein the switch curve is a curve of the accessory which is turned on and off within the preset time from the current moment, and the target temperature is the temperature in the cab set by the driver;
and calculating the accessory power according to the switching curve, the target temperature and the environment temperature.
7. The method of claim 6, wherein said calculating the accessory power from the switching curve, the target temperature, and the ambient temperature comprises:
calculating the transient power of the accessory according to the switch curve, the target temperature and the environment temperature, wherein the transient power is the power consumed by the accessory to enable the temperature of the cab to reach the target temperature;
calculating the steady-state power of the accessory according to the environment temperature, wherein the steady-state power is the power consumed by the accessory for maintaining the temperature of the cab at the target temperature;
and calculating the accessory power according to the transient power and the steady-state power.
8. The method of claim 7, wherein said calculating a switching curve of the accessory and a target temperature of a cabin of the vehicle from the ambient temperature comprises:
and calling an accessory using habit model to process the environment temperature to obtain the switch curve and the target temperature, wherein the accessory using habit model is a machine learning model obtained by training according to at least one group of second training group, and the second training group comprises the sample environment temperature and an actual switch curve and an actual target temperature of the accessory corresponding to the sample environment temperature.
9. The method of claim 1, wherein said calculating a range of the vehicle from the energy consumption rate comprises:
and calling a battery model to process the energy consumption rate to obtain the endurance mileage, wherein the battery model is a physical model obtained based on RC model modeling.
10. The method of any of claims 1 to 9, wherein the historical energy consumption rate comprises historical drive power and historical accessory power of the vehicle.
11. An apparatus for predicting driving range, comprising:
the speed prediction module is used for calculating to obtain a speed curve of the vehicle based on road information, the speed curve is a curve of speeds of the vehicle in N travel sections from the current moment, the road information is information used for indicating the environmental conditions of roads on each travel section, N is a natural number, and N is more than or equal to 1;
the energy consumption prediction module is used for calculating the driving power of the vehicle according to the road information and the speed curve, wherein the driving power is the power of equipment and/or devices which are arranged on the vehicle and participate in driving the vehicle to run; calculating an accessory power of the vehicle, the accessory power being a power of an accessory equipped on the vehicle, the accessory being a device and/or a device that does not participate in driving the vehicle; calculating a historical energy consumption rate of the vehicle, the historical energy consumption rate being an energy consumption rate calculated from historical energy consumption of the vehicle; calculating the energy consumption rate of the vehicle according to the historical energy consumption rate of the vehicle, the accessory power and the driving power;
and the endurance prediction module is used for calculating the endurance mileage of the vehicle according to the energy consumption rate.
12. A computer device, characterized in that it comprises a processor and a memory, in which at least one instruction or program is stored, which is loaded and executed by the processor to implement a method of prediction of range as claimed in any one of claims 1 to 10.
13. The apparatus of claim 12, wherein the apparatus is provided in a vehicle.
14. A vehicle characterized in that it comprises a computer device according to claim 13.
15. A computer-readable storage medium having stored thereon at least one instruction which is loaded and executed by a processor to implement a method of predicting range as claimed in any one of claims 1 to 10.
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