Vehicle endurance mileage determination method and device, equipment, storage medium and product

Abstract

The invention discloses a vehicle endurance mileage determination method and device, equipment, a storage medium and a product, and belongs to the technical field of artificial intelligence. The method comprises the steps that the current first charging and discharging times of a battery included in the vehicle are determined; determining a first charge-discharge frequency interval to which the first charge-discharge frequency belongs based on the first charge-discharge frequency; determining a first mileage prediction model corresponding to the first charge-discharge frequency interval, wherein the first mileage prediction model is used for predicting the endurance mileage of the battery with the charge-discharge frequency in the first charge-discharge frequency interval; obtaining a current first voltage, a first current, a first temperature, a first state of charge (SOC) and a first battery state (SOH) of the battery and a current first driving mode of the vehicle; and based on the current first voltage, the first current, the first temperature, the first SOC and the first SOH of the battery and the current first driving mode of the vehicle, the endurance mileage of the vehicle is determined through the first mileage prediction model.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device, equipment, storage medium and product for determining a vehicle's cruising range. Background Art

[0002] For electric vehicles, the vehicle's cruising range is a data that drivers are very concerned about. Accurate cruising range can help drivers accurately determine when to charge, thereby greatly alleviating the driver's anxiety about the cruising range. In addition, accurate cruising range will not only increase the driver's trust in the vehicle, but also increase the service life of the vehicle's battery. Therefore, how to accurately calculate the vehicle's cruising range is an issue that urgently needs to be solved. Summary of the invention

[0003] The embodiments of the present application provide a method, device, equipment, storage medium and product for determining the cruising range of a vehicle. The technical solution is as follows:

[0004] In one aspect, a method for determining a cruising range of a vehicle is provided, the method comprising:

[0005] Determining a first current charge and discharge number of a battery included in the vehicle;

[0006] Based on the first charge and discharge times, determining a first charge and discharge times interval to which the first charge and discharge times belongs;

[0007] Determine a first mileage prediction model corresponding to the first charge and discharge frequency interval, the first mileage prediction model being used to predict the cruising range of a battery with charge and discharge frequencies in the first charge and discharge frequency interval;

[0008] Acquire a current first voltage, a first current, a first temperature, a first state of charge SOC, a first state of battery SOH of the battery, and a current first driving mode of the vehicle;

[0009] Based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle, the cruising range of the vehicle is determined by using the first mileage prediction model.

[0010] In a possible implementation, the method further includes:

[0011] Based on the first charge and discharge frequency interval, a first sample data set is determined, the first sample data set includes first sample data of a first sample vehicle and label information of the first sample data, the charge and discharge frequency of the first sample vehicle is within the first charge and discharge frequency interval, and the first sample data of the first sample vehicle includes a voltage, a current, a temperature, a SOC, a SOH of a battery included in the first sample vehicle and a driving mode of the first sample vehicle, and the label information of the first sample data is an actual cruising range of the first sample vehicle;

[0012] Based on the first sample data set, determining the predicted cruising range of the first sample vehicle by using an initial first mileage prediction model, wherein the first mileage prediction model includes an input layer, a plurality of hidden layers, and an output layer connected in sequence;

[0013] Determine a loss function value of the first mileage prediction model based on the actual cruising range and the predicted cruising range of the first sample vehicle, wherein the loss function value is used to represent the difference between the actual cruising range and the predicted cruising range of the first sample vehicle;

[0014] When the loss function value is less than the loss function threshold, the training is completed to obtain the first mileage prediction model;

[0015] In the case where the loss function value is not less than the loss function threshold, based on the loss function value, the feature weights and biases of the multiple hidden layers are updated, and then based on the first sample data set, the predicted cruising range of the first sample vehicle is determined by the first mileage prediction model, until the loss function value of the first mileage prediction model is less than the loss function threshold, at which time the training is completed and the first mileage prediction model is obtained.

[0016] In another possible implementation,

[0017] The determining the loss function value of the first mileage prediction model based on the actual cruising range and the predicted cruising range of the first sample vehicle includes:

[0018] Determining a first sample number of the first sample data set;

[0019] Based on the first sample number, the actual cruising range and the predicted cruising range of the first sample vehicle, the loss function value of the first mileage prediction model is determined by the following formula 1;

[0020] Formula 1:

[0021] Wherein, L represents the loss function value, N represents the first sample number, i represents the serial number of the first sample vehicle, represents the predicted cruising range of the i-th first sample vehicle, y i represents the actual cruising range of the i-th first sample vehicle.

[0022] In another possible implementation, updating the feature weights and biases of the multiple hidden layers based on the loss function value includes:

[0023] For the j-th hidden layer, determining the partial derivative of the loss function with respect to the feature weight of the j-th hidden layer to obtain a first partial derivative;

[0024] Determine the partial derivative of the loss function with respect to the bias of the j-th hidden layer to obtain a second partial derivative;

[0025] Based on the first partial derivative and the second partial derivative, the feature weight and bias of the j-th hidden layer are updated by the following formula 2;

[0026] Formula 2:

[0027] Among them, j represents the number of the hidden layer, represents the updated feature weight of the jth hidden layer, represents the feature weight before the jth hidden layer is updated, α represents the learning rate, represents the first partial derivative, represents the updated bias value of the jth hidden layer, represents the bias value before the jth hidden layer is updated, represents the second partial derivative.

[0028] In another possible implementation, the method further includes:

[0029] When the current second charge and discharge number of the battery is greater than the maximum value of the first charge and discharge number interval, determining the second charge and discharge number interval to which the second charge and discharge number belongs;

[0030] Based on the second charge and discharge frequency interval, determining a second sample data set, the second sample data set including second sample data of a second sample vehicle and label information of the second sample data, the charge and discharge frequency of the second sample vehicle is within the second charge and discharge frequency interval, and the second sample data of the second sample vehicle includes a voltage, a current, a temperature, a SOC, a SOH of a battery included in the second sample vehicle and a driving mode of the second sample vehicle, and the label information of the second sample data is an actual cruising range of the second sample vehicle;

[0031] Based on the second sample data set, training a second mileage prediction model, the second mileage prediction model is used to predict the cruising range of a battery with a charge and discharge number of times within the second charge and discharge number range;

[0032] Acquire a second current, a second temperature, a second SOC, a second SOH of the battery and a second driving mode of the vehicle;

[0033] Based on the current second voltage, second current, second temperature, second SOC, second SOH of the battery and the current second driving mode of the vehicle, the cruising range of the vehicle is determined by the second mileage prediction model.

[0034] In another possible implementation, determining the cruising range of the vehicle through the first mileage prediction model based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle includes:

[0035] Inputting the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle into the first mileage prediction model to output the first predicted SOC of the vehicle;

[0036] Based on the first predicted SOC and the theoretical cruising range of the vehicle, the cruising range of the vehicle is determined.

[0037] In another aspect, a device for determining a cruising range of a vehicle is provided, the device comprising:

[0038] A first determination module, used to determine a current first charge and discharge number of a battery included in the vehicle;

[0039] A second determining module, configured to determine, based on the first charge and discharge times, a first charge and discharge times interval to which the first charge and discharge times belongs;

[0040] A third determination module, used to determine a first mileage prediction model corresponding to the first charge and discharge frequency interval, wherein the first mileage prediction model is used to predict the cruising range of a battery with charge and discharge times in the first charge and discharge frequency interval;

[0041] A first acquisition module is used to acquire a current first voltage, a first current, a first temperature, a first state of charge SOC, a first state of battery SOH of the battery and a current first driving mode of the vehicle;

[0042] The fourth determination module is used to determine the cruising range of the vehicle through the first mileage prediction model based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle.

[0043] In a possible implementation manner, the device further includes:

[0044] a fifth determination module, configured to determine a first sample data set based on the first charge and discharge frequency interval, wherein the first sample data set includes first sample data of a first sample vehicle and label information of the first sample data, the charge and discharge frequency of the first sample vehicle is within the first charge and discharge frequency interval, and the first sample data of the first sample vehicle includes a voltage, a current, a temperature, a SOC, a SOH of a battery included in the first sample vehicle, and a driving mode of the first sample vehicle, and the label information of the first sample data is an actual cruising range of the first sample vehicle;

[0045] a sixth determination module, configured to determine the predicted cruising range of the first sample vehicle by using an initial first mileage prediction model based on the first sample data set, wherein the first mileage prediction model includes an input layer, a plurality of hidden layers, and an output layer connected in sequence;

[0046] a seventh determination module, configured to determine a loss function value of the first mileage prediction model based on the actual cruising range and the predicted cruising range of the first sample vehicle, wherein the loss function value is used to represent a difference between the actual cruising range and the predicted cruising range of the first sample vehicle;

[0047] A first training module, configured to, when the loss function value is less than a loss function threshold, terminate the training to obtain the first mileage prediction model;

[0048] An updating module is used to update the feature weights and biases of the multiple hidden layers based on the loss function value when the loss function value is not less than the loss function threshold, and then determine the predicted cruising range of the first sample vehicle through the first mileage prediction model based on the first sample data set, until the loss function value of the first mileage prediction model is less than the loss function threshold, at which time the training is completed and the first mileage prediction model is obtained.

[0049] In another possible implementation, the seventh determination module is used to determine a first sample number of the first sample data set; based on the first sample number, the actual cruising range and the predicted cruising range of the first sample vehicle, determine the loss function value of the first mileage prediction model by the following formula 1;

[0050] Formula 1:

[0051] Wherein, L represents the loss function value, N represents the first sample number, i represents the serial number of the first sample vehicle, represents the predicted cruising range of the i-th first sample vehicle, y i represents the actual cruising range of the i-th first sample vehicle.

[0052] In another possible implementation, the update module is used to determine, for the jth hidden layer, the partial derivative of the loss function with respect to the feature weight of the jth hidden layer to obtain a first partial derivative; determine the partial derivative of the loss function with respect to the bias of the jth hidden layer to obtain a second partial derivative; based on the first partial derivative and the second partial derivative, update the feature weight and bias of the jth hidden layer by the following formula 2;

[0053] Formula 2:

[0054] Among them, j represents the number of the hidden layer, represents the updated feature weight of the jth hidden layer, represents the feature weight before the jth hidden layer is updated, α represents the learning rate, represents the first partial derivative, represents the updated bias value of the jth hidden layer, represents the bias value before the jth hidden layer is updated, represents the second partial derivative.

[0055] In another possible implementation, the device further includes:

[0056] an eighth determination module, configured to determine, when the current second charge and discharge number of times of the battery is greater than the maximum value of the first charge and discharge number interval, a second charge and discharge number interval to which the second charge and discharge number belongs;

[0057] a ninth determination module, configured to determine a second sample data set based on the second charge and discharge frequency interval, the second sample data set including second sample data of a second sample vehicle and label information of the second sample data, the charge and discharge frequency of the second sample vehicle being within the second charge and discharge frequency interval, and the second sample data of the second sample vehicle including a voltage, a current, a temperature, a SOC, a SOH of a battery included in the second sample vehicle and a driving mode of the second sample vehicle, and the label information of the second sample data being an actual cruising range of the second sample vehicle;

[0058] A second training module, used for training a second mileage prediction model based on the second sample data set, wherein the second mileage prediction model is used for predicting the cruising range of a battery with a charge and discharge frequency within the second charge and discharge frequency range;

[0059] A second acquisition module, used for acquiring a second voltage, a second current, a second temperature, a second SOC, a second SOH of the battery and a second driving mode of the vehicle;

[0060] The tenth determination module is used to determine the cruising range of the vehicle through the second mileage prediction model based on the current second voltage, second current, second temperature, second SOC, second SOH of the battery and the current second driving mode of the vehicle.

[0061] In another possible implementation, the fourth determination module is used to input the voltage, current, temperature, SOC, SOH of the battery and the current driving mode of the vehicle into the first mileage prediction model to output the predicted SOC of the vehicle; and determine the cruising range of the vehicle based on the predicted SOC and the theoretical cruising range of the vehicle.

[0062] On the other hand, a computer device is provided, which includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the above-mentioned method for determining the cruising range of a vehicle.

[0063] On the other hand, a computer-readable storage medium is provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the above-mentioned method for determining the cruising range of a vehicle.

[0064] On the other hand, a computer program product is provided, wherein the product stores at least one program code, and the at least one program code is used to be executed by a processor to implement the above-mentioned method for determining the cruising range of a vehicle.

[0065] In the embodiment of the present application, on the one hand, the number of times the battery is charged and discharged will affect the service life of the battery. As the number of times the battery is charged and discharged increases, the health of the battery will gradually decrease; therefore, when determining the cruising range of the vehicle, the SOH value of the battery is added as one of the input features, thereby improving the accuracy of the determined cruising range of the vehicle. On the other hand, different mileage prediction models are set for different charge and discharge frequency intervals, thereby improving the accuracy of the determined cruising range of the vehicle. In summary, the embodiment of the present application can improve the accuracy of the determined cruising range of the vehicle.

[0066] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a schematic diagram of an implementation environment of a method for determining a cruising range of a vehicle shown in an exemplary embodiment of the present application;

[0068] Figure 2 is a flow chart of a method for determining a vehicle's cruising range shown in an exemplary embodiment of the present application;

[0069] Figure 3 is a flow chart of a method for determining a vehicle's cruising range shown in an exemplary embodiment of the present application;

[0070] Figure 4 is a schematic diagram of a first mileage prediction model of a vehicle shown in an exemplary embodiment of the present application;

[0071] Figure 5 is a flow chart of a method for determining a vehicle's cruising range shown in an exemplary embodiment of the present application;

[0072] Figure 6 is a flow chart of a method for determining a vehicle's cruising range shown in an exemplary embodiment of the present application;

[0073] Figure 7 is a block diagram of a device for determining a cruising range of a vehicle shown in an exemplary embodiment of the present application;

[0074] Figure 8 is a block diagram of a computer device shown in an exemplary embodiment of the present application;

[0075] Fig. 9 It is a block diagram of a vehicle controller shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0076] In order to make the technical solutions and advantages of the present application clearer, the implementation methods of the present application are described in further detail below.

[0077] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0078] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions. For example, the number of battery charge and discharge times (including the first charge and discharge times and the second charge and discharge times), the battery voltage (including the first voltage and the second voltage), the battery current (including the first current and the second current), the battery temperature (including the first temperature and the second temperature), the battery SOC (including the first SOC and the second SOC), the battery SOH (including the first SOH and the second SOH) and the current driving mode of the vehicle (including the first driving mode and the second driving mode) involved in this application are all obtained with full authorization.

[0079] Please refer to Figure 1 , which shows a schematic diagram of an implementation environment of a method for determining a vehicle's cruising range according to an exemplary embodiment of the present application. The implementation environment includes a computer device 101 and a vehicle 102. The computer device 101 is used to train a mileage prediction model and predict the cruising range (also referred to as the remaining cruising range) of the vehicle 102 through the mileage prediction model.

[0080] The vehicle 102 is a new energy vehicle, such as a pure electric vehicle, a plug-in hybrid electric vehicle, or an extended-range electric vehicle, etc. The vehicle 102 is equipped with a vehicle controller, a battery, an onboard charger for charging the battery, and a battery management system for managing the battery.

[0081] The battery management system is used to obtain the voltage, current, temperature, SOC and SOH of the battery; the vehicle controller is used to determine the current driving mode of the vehicle 102. In the embodiment of the present application, the cruising range of the vehicle 102 is determined based on the voltage, current, temperature, SOC and SOH of the battery and the current driving mode of the vehicle 102.

[0082] In one possible implementation, after the battery management system obtains the battery voltage, current, temperature, SOC and SOH, it sends the battery voltage, current, temperature, SOC and SOH to the vehicle controller. After the vehicle controller receives the battery voltage, current, temperature, SOC and SOH sent by the battery management system and obtains the current driving mode of the vehicle 102, it sends the battery voltage, current, temperature, SOC and SOH and the current driving mode of the vehicle 102 to the computer device 101. The computer device 101 determines the cruising range of the vehicle 102 through a mileage prediction model based on the battery voltage, current, temperature, SOC and SOH and the current driving mode of the vehicle 102, and then sends the cruising range of the vehicle 102 to the vehicle controller. Alternatively, after the battery management system obtains the battery voltage, current, temperature, SOC and SOH, it sends the battery voltage, current, temperature, SOC and SOH directly to the computer device 101. After the vehicle controller obtains the current driving mode of the vehicle 102, it sends the current driving mode of the vehicle 102 to the computer device 101. The computer device 101 determines the cruising range of the vehicle 102 based on the battery voltage, current, temperature, SOC and SOH and the current driving mode of the vehicle 102 through a mileage prediction model, and then sends the cruising range of the vehicle 102 to the vehicle controller.

[0083] In another possible implementation, the computer device 101 deploys the trained mileage prediction model to the vehicle controller. After the vehicle controller receives the battery voltage, current, temperature, SOC and SOH sent by the battery management system and obtains the current driving mode of the vehicle 102, the vehicle controller determines the cruising range of the vehicle 102 through the locally deployed mileage prediction model.

[0084] In another possible implementation, the mileage prediction model is trained by the vehicle controller, and after the vehicle controller receives the battery voltage, current, temperature, SOC and SOH sent by the battery management system and obtains the current driving mode of the vehicle 102, the vehicle controller determines the cruising range of the vehicle 102 through the trained mileage prediction model.

[0085] Please refer to Figure 2 , which shows a flow chart of a method for determining a vehicle's cruising range according to an exemplary embodiment of the present application. Figure 2 , the method comprising:

[0086] Step 201: A computer device determines a current first charge and discharge number of a battery included in a vehicle.

[0087] The current first charge and discharge number of the battery refers to the number of times the battery currently completes a complete charge and discharge process. The vehicle is equipped with a battery, a battery management system for managing the battery, and a vehicle controller; the vehicle controller and the battery management system provide a CAN bus electrical connection. The battery management system obtains the current first charge and discharge number of the battery, and then sends the first charge and discharge number to the vehicle controller. The vehicle controller receives the first charge and discharge number, and sends the first charge and discharge number to the computer device, and the computer receives the first charge and discharge number. Alternatively, after the battery management system obtains the first charge and discharge number, it directly sends the first charge and discharge number to the computer device.

[0088] Step 202: The computer device determines a first charge and discharge frequency interval to which the first charge and discharge frequency belongs based on the first charge and discharge frequency.

[0089] In the embodiment of the present application, different mileage prediction models are set for different charge and discharge frequency intervals; therefore, after the computer device obtains the first charge and discharge frequency, it determines the first charge and discharge frequency interval to which the first charge and discharge frequency belongs, and then executes step 203.

[0090] Step 203: The computer device determines a first mileage prediction model corresponding to a first charge and discharge frequency interval, where the first mileage prediction model is used to predict the cruising range of a battery with a charge and discharge frequency in the first charge and discharge frequency interval.

[0091] In the embodiment of the present application, different mileage prediction models are set for different charge and discharge frequency intervals, so as to improve the accuracy of the endurance mileage predicted by the mileage prediction model. Before this step, the computer device pre-trains the first mileage prediction model corresponding to the first charge and discharge frequency interval and stores the first mileage prediction model; in this step, the computer device directly obtains the stored first mileage prediction model.

[0092] Step 204: The computer device obtains the current first voltage, first current, first temperature, first state of charge SOC, first state of battery SOH and the current first driving mode of the vehicle.

[0093] The current first driving mode of the vehicle can be economic mode, normal mode, sports mode, and long-range mode. The first SOC refers to the charging state of the battery, that is, the remaining power, and the first SOC represents the ratio of the remaining available power to the power in the fully charged state after the battery has been used or stored for a period of time. This ratio is usually expressed as a percentage, ranging from 0% to 100%. When the first SOC is 0%, it means that the battery is fully discharged; when the first SOC is 100%, it means that the battery is fully charged.

[0094] First SOH refers to the battery's state of health, which represents the battery's actual capacity percentage relative to a new battery. First SOH is used to describe the health of the battery's current state compared to its original state. When the battery ages or becomes damaged, the first SOH will decrease, indicating that the battery's capacity and performance have been reduced.

[0095] In the embodiment of the present application, the first SOH is an important indicator for evaluating the current state of the battery. As the number of charge and discharge cycles of the battery increases, the active materials and lithium ions inside the battery are continuously lost, which will cause the battery capacity, power, and life to continue to decrease. Therefore, estimating the remaining mileage of the vehicle through the first SOH state of the battery can more accurately reflect the changes in the mileage.

[0096] Step 205: The computer device determines the cruising range of the vehicle through a first mileage prediction model based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle.

[0097] The first mileage prediction model is used to predict the first predicted SOC of the vehicle based on the battery's current first voltage, first current, first temperature, first SOC, first SOH and the vehicle's current first driving mode; accordingly, this step may be: the computer device inputs the battery's current first voltage, first current, first temperature, first SOC, first SOH and the vehicle's current first driving mode into the first mileage prediction model to output the vehicle's first predicted SOC; based on the first predicted SOC and the vehicle's theoretical cruising range, determine the vehicle's cruising range.

[0098] In a possible implementation, the step of determining the cruising range of the vehicle based on the first predicted SOC and the theoretical cruising range of the vehicle may be: the computer device determines the product of the first predicted SOC and the theoretical cruising range of the vehicle to obtain the cruising range of the vehicle. The theoretical cruising range of the vehicle may be the total mileage of the vehicle.

[0099] In the embodiments of the present application, as the computing power of the neural network algorithm continues to improve, its advantages such as fast computing speed and high accuracy are well known. Compared with ordinary algorithms, the advantage of neural networks is that they can select simple input features to reduce the difficulty of data collection; the algorithm model is easy to build, and only some time is needed to train the model in the early stage to meet the actual needs in the later stage.

[0100] It should be noted that the embodiment of the present application can also be executed by the vehicle controller of the vehicle; accordingly, the method includes: the vehicle controller determines the current first charge and discharge times of the battery included in the vehicle, determines the first charge and discharge times interval to which the first charge and discharge times belong based on the first charge and discharge times, obtains the first mileage prediction model corresponding to the first charge and discharge times from the computer device based on the first charge and discharge times interval, and then stores the first mileage prediction model. When the vehicle controller determines the cruising range of the vehicle, the vehicle controller takes the current first voltage, first current, first temperature, first state of charge SOC, first battery state SOH of the battery and the current first driving mode of the vehicle, and determines the cruising range of the vehicle through the first mileage prediction model based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle.

[0101] In the embodiment of the present application, on the one hand, the number of times the battery is charged and discharged will affect the service life of the battery. As the number of charge and discharge cycles increases, the health of the battery will gradually decrease; therefore, when determining the vehicle's cruising range, the battery's SOH value is added as one of the input features, thereby improving the accuracy of the determined vehicle's cruising range. On the other hand, different mileage prediction models are set for different charge and discharge frequency intervals, thereby improving the accuracy of the determined vehicle's cruising range. In summary, the embodiment of the present application can improve the accuracy of the determined vehicle's cruising range.

[0102] Please refer to Figure 3 , which shows a flow chart of a method for determining the cruising range of a vehicle shown in an exemplary embodiment of the present application. In the embodiment of the present application, the training mileage prediction model is used as an example for explanation. Figure 3 , the method comprising:

[0103] Step 301: The computer device determines a first sample data set based on a first charge and discharge frequency interval, the first sample data set including first sample data of a first sample vehicle and label information of the first sample data, the charge and discharge frequency of the first sample vehicle is within the first charge and discharge frequency interval, and the first sample data of the first sample vehicle includes the voltage, current, temperature, SOC, SOH of a battery included in the first sample vehicle and a driving mode of the first sample vehicle, and the label information of the first sample data is the actual cruising range of the first sample vehicle.

[0104] It should be noted that before the computer device executes step 302, the computer device needs to construct an initial first mileage prediction model; for example, please refer to Figure 4The first mileage prediction model includes an input layer, multiple hidden layers and an output layer; the input layer includes 6 nodes, that is, the input layer includes a first node, a second node, a third node, a fourth node, a fifth node and a sixth node, the first node is used to input voltage, the second node is used to input current, the third node is used to input temperature, the fourth node is used to input SOH, the fifth node is used to input driving mode, and the sixth node is used to input SOC. The output layer includes one node, and the nodes included in the output layer are used to output the predicted SOC of the vehicle.

[0105] In a possible implementation, the number of hidden layers included in the first mileage prediction model can be set and changed as needed; for example, the number of hidden layers can be 3 or 4, etc.; in the embodiment of the present application, the number of hidden layers is 3, that is, the hidden layers included in the first mileage prediction model are respectively the first hidden layer, the second hidden layer and the third hidden layer, the input end of the first hidden layer is connected to the output end of the input layer, the output end of the first hidden layer is connected to the input end of the second hidden layer, the output end of the second hidden layer is connected to the input end of the third hidden layer, and the output end of the third hidden layer is connected to the output layer.

[0106] After the computer device determines the number of hidden layers, it also needs to determine the number of features of each hidden layer, that is, the computer device also needs to determine the number of features of the first hidden layer, the number of features of the second hidden layer, and the number of features of the third hidden layer. The computer device subtracts a preset value from the number of features of the first hidden layer to obtain the number of features of the second hidden layer, and subtracts a preset value from the number of features of the second hidden layer to obtain the number of features of the third hidden layer. The preset value can be set and changed as needed. In the embodiment of the present application, the preset value is not specifically limited; for example, the preset value can be 2; therefore, the core of this step is to determine the number of features of the first hidden layer.

[0107] Among them, the step of the computer device determining the number of features of the first hidden layer can be: the computer device determines the number of input features, the number of output features and the neuron constants of the hidden layer, and determines the number of features of the first hidden layer based on the number of input features, the number of output features and the neuron constants of the hidden layer.

[0108] Among them, the step of the computer device determining the neuron constants of the hidden layer may be: the computer device determines the value range of the neuron constants of the hidden layer, determines the precision requirement information and the accuracy requirement information, the precision requirement information is used to represent the precision requirement of the first mileage prediction model, and the accuracy requirement information is used to represent the accuracy requirement of the first mileage prediction model; the computer device determines the neuron constants included in the hidden layer based on the precision requirement information and the accuracy requirement information; determines the neuron constants based on the value range of the neuron constants and the neuron constants included in the hidden layer.

[0109] For example, the value range of the neuron constant of the hidden layer is 1-10; when the neuron constant of the hidden layer is very small, the prediction accuracy of the first mileage prediction model is very low and does not meet the requirements; as the number of neurons in the hidden layer increases, the accuracy of the first mileage prediction model training becomes higher and higher, and the number of training times becomes less and less; but when the neuron constant increases to more than 15, the accuracy of the first mileage prediction model training hardly increases, but the training time of the first mileage prediction model will become longer; therefore, based on the above content, the computer device determines that the neuron constant of the hidden layer is 5.

[0110] Among them, the step of determining the number of features of the first hidden layer based on the number of input features, the number of output features and the neuron constants of the hidden layer by the computer device can be: the computer device determines the first value based on the number of input features, the number of output features and the neuron constants of the hidden layer through the following formula three, and rounds the first value to obtain the number of features of the first hidden layer.

[0111] Formula 3:

[0112] Wherein, i represents a first numerical value, x represents the number of input features, y represents the number of output features, and z represents a neuron constant of a hidden layer; in the embodiment of the present application, the value of x is 6, the value of y is 1, and the value of z is 5; the computer device rounds up i to obtain a feature number of 8 for the first hidden layer, then the computer device subtracts a preset value (2) from the feature number of the first hidden layer to obtain a feature number (6) for the second hidden layer, and subtracts the preset value (2) from the feature number of the second hidden layer to obtain a feature number (4) for the third hidden layer.

[0113] Step 302: The computer device determines the predicted cruising range of the first sample vehicle based on the first sample data set through an initial first mileage prediction model, where the first mileage prediction model includes an input layer, multiple hidden layers, and an output layer connected in sequence.

[0114] The first sample data set includes first sample data of multiple first sample vehicles. For the first sample data of any first sample vehicle, the computer device inputs the first sample data of the first sample vehicle into an initial first mileage prediction model, outputs the predicted SOC of the first sample vehicle, and determines the predicted cruising range of the first sample vehicle based on the predicted SOC of the first sample vehicle and the theoretical cruising range of the first sample vehicle.

[0115] Step 303: The computer device determines a loss function value of the first mileage prediction model based on the actual mileage and the predicted mileage of the first sample vehicle, where the loss function value is used to represent the difference between the actual mileage and the predicted mileage of the first sample vehicle.

[0116] This step can be implemented by the following steps 3031 and 3032, including:

[0117] Step 3031: The computer device determines the first sample number of the first sample data set.

[0118] Step 3032: The computer device determines the loss function value of the first mileage prediction model based on the first sample number, the actual mileage and the predicted mileage of the first sample vehicle by using the following formula 1;

[0119] Formula 1:

[0120] Where L represents the loss function value, N represents the number of first samples, and i represents the serial number of the first sample vehicle. represents the predicted cruising range of the i-th first sample vehicle, y i Represents the actual cruising range of the i-th first sample vehicle.

[0121] Step 304: When the loss function value is less than the loss function threshold, the computer device training is completed to obtain the first mileage prediction model.

[0122] The loss function threshold can be set and changed as needed. In the embodiment of the present application, the loss function threshold is not specifically limited; for example, the loss function threshold can be 0.01. In the embodiment of the present application, after the computer device performs multiple rounds of training on the first mileage prediction model, the loss function value of the feature weights of multiple hidden layers included in the first mileage prediction model will gradually decrease, and over-training will lead to overfitting, so it is necessary to set the loss function threshold. When it is reduced to the loss function threshold (set as 0.01 in this article), the training is terminated, and the first mileage prediction model is deemed to have completed the current stage of training.

[0123] Step 305: When the loss function value is not less than the loss function threshold, the computer device updates the feature weights and biases of multiple hidden layers based on the loss function value, and then determines the predicted cruising range of the first sample vehicle through the first mileage prediction model based on the first sample data set, until the loss function value of the first mileage prediction model is less than the loss function threshold, at which time the training is completed and the first mileage prediction model is obtained.

[0124] The steps of updating the feature weights and biases of the plurality of hidden layers by the computer device based on the loss function value may be:

[0125] For the jth hidden layer, determine the partial derivative of the loss function with respect to the feature weight of the jth hidden layer to obtain the first partial derivative;

[0126] Determine the partial derivative of the loss function with respect to the bias of the j-th hidden layer to obtain a second partial derivative;

[0127] Based on the first partial derivative and the second partial derivative, the feature weight and bias of the jth hidden layer are updated by the following formula 2;

[0128] Formula 2:

[0129] Among them, j represents the number of the hidden layer, represents the updated feature weight of the jth hidden layer, represents the feature weight before the jth hidden layer is updated, α represents the learning rate, represents the first partial derivative, represents the updated bias value of the jth hidden layer, represents the bias value before the jth hidden layer is updated, represents the second partial derivative.

[0130] The learning rate can be set and changed as needed. In the embodiment of the present application, the learning rate is not specifically limited; for example, the learning rate can be 0.01.

[0131] After the computer device updates the feature weights of multiple hidden layers, based on the first sample data set, the predicted cruising range of the first sample vehicle is re-determined through the updated first mileage prediction model, and then based on the actual cruising range of the first sample vehicle and the re-determined predicted cruising range, the loss function value of the first mileage prediction model is re-determined, and then it is determined whether the re-determined loss function value is less than the loss function threshold. When the re-determined loss function values ​​are all less than the loss function threshold, the training is completed to obtain the first mileage prediction model; when the re-determined loss function value is not less than the loss function threshold, the feature weights and biases of the multiple hidden layers are updated again, and so on.

[0132] It should be noted that the first mileage prediction model can also be trained by the vehicle controller; when the first mileage prediction model is trained by the vehicle controller, the execution subject of steps 301-305 is replaced by the vehicle controller.

[0133] Please refer to Figure 5 , which shows a flow chart of a method for determining the cruising range of a vehicle shown in an exemplary embodiment of the present application. In the embodiment of the present application, an example is given in which a computer device updates a mileage prediction model and determines the cruising range of a vehicle by the computer device. Figure 5 , the method comprising:

[0134] Step 501: When the current second charge and discharge times of the battery is greater than the maximum value of the first charge and discharge times interval, the computer device determines the second charge and discharge times interval to which the second charge and discharge times belongs.

[0135] For the sake of distinction, the number of charge and discharge times determined at different times are referred to as different charge and discharge times; for example, the number of charge and discharge times determined when the vehicle's cruising range was determined last time is referred to as the first charge and discharge times (step 201), and the number of charge and discharge times determined when the vehicle's cruising range is determined this time is referred to as the second charge and discharge times. The SOH of a battery is highly correlated with the number of charge and discharge times of the battery; when the second charge and discharge times is greater than the maximum value of the first charge and discharge times interval, it means that the vehicle's cruising range predicted by the first mileage prediction model at this time is no longer accurate, and the latest battery data needs to be introduced to retrain the mileage prediction model to predict the vehicle's cruising range, thereby improving the accuracy of the determined vehicle's cruising range.

[0136] Step 502: The computer device determines a second sample data set based on a second charge and discharge frequency interval, the second sample data set including second sample data of a second sample vehicle and label information of the second sample data, the charge and discharge frequency of the second sample vehicle is within the second charge and discharge frequency interval, and the second sample data of the second sample vehicle includes the voltage, current, temperature, SOC, SOH of a battery included in the second sample vehicle and the driving mode of the second sample vehicle, and the label information of the second sample data is the actual cruising range of the second sample vehicle.

[0137] In some embodiments, this step is similar to step 301 and will not be repeated again.

[0138] Step 503: The computer device trains a second mileage prediction model based on the second sample data set, where the second mileage prediction model is used to predict the cruising range of a battery with a charge and discharge frequency within a second charge and discharge frequency range.

[0139] The computer device determines the predicted cruising range of the second sample vehicle through an initial second mileage prediction model based on the second sample data set, the second mileage prediction model includes an input layer, multiple hidden layers and an output layer connected in sequence; based on the actual cruising range and the predicted cruising range of the second sample vehicle, the loss function value of the second mileage prediction model is determined; when the loss function value is less than the loss function threshold, the training is completed to obtain the second mileage prediction model, and when the loss function value is not less than the loss function threshold, the computer device updates the bias of the feature weights of the multiple hidden layers based on the loss function value, and then determines the predicted cruising range of the second sample vehicle through the second mileage prediction model based on the second sample data set, until the loss function values ​​of the determined second mileage prediction model are all less than the loss function threshold, at which time the training is completed to obtain the second mileage prediction model.

[0140] Step 504: The computer device obtains the current second voltage, second current, second temperature, second SOC, second SOH of the battery and the current second driving mode of the vehicle.

[0141] For the sake of distinction, the voltage, current, temperature, SOC and SOH of the battery and the driving mode of the vehicle determined at different times are referred to as different voltages, currents, temperatures, SOC and SOH and the driving mode of the vehicle; for example, the voltage, current, temperature, SOC and SOH and the driving mode of the vehicle determined when the vehicle's cruising range was determined last time are referred to as the first voltage, the first current, the first temperature, the first SOC and the first SOH and the first driving mode of the vehicle (step 204), and the voltage, current, temperature, SOC and SOH and the driving mode of the vehicle determined when the vehicle's cruising range is determined this time are referred to as the second voltage, the second current, the second temperature, the second SOC and the second SOH and the second driving mode of the vehicle.

[0142] Step 505: The computer device determines the vehicle's cruising range through a second mileage prediction model based on the battery's current second voltage, second current, second temperature, second SOC, second SOH and the vehicle's current second driving mode.

[0143] The computer device inputs the battery's current second voltage, second current, second temperature, second SOC, second SOH and the vehicle's current second driving mode into a second mileage prediction model and outputs the vehicle's second predicted SOC; based on the second predicted SOC and the vehicle's theoretical cruising range, the vehicle's cruising range is determined.

[0144] In an embodiment of the present application, the battery SOH value is highly correlated with the number of battery charge and discharge times. When the number of vehicle charge and discharge times reaches a certain limit, the latest battery data will be introduced to retrain the mileage prediction model (for ease of distinction, the retrained mileage prediction model will be referred to as the second mileage prediction model) The various weight values ​​​​are used to determine the vehicle's cruising range through the second mileage prediction model, thereby improving the accuracy of the determined vehicle's cruising range.

[0145] Please refer to Figure 6 , which shows a flow chart of a method for determining the cruising range of a vehicle shown in an exemplary embodiment of the present application. In the embodiment of the present application, an example is given in which a computer device updates a mileage prediction model and a vehicle controller determines the cruising range of the vehicle. Figure 6 , the method comprising:

[0146] Step 601: When the current second charge and discharge times of the battery is greater than the maximum value of the first charge and discharge times interval, the vehicle controller sends an acquisition request to the computer device, and the acquisition request carries the second charge and discharge times.

[0147] In an embodiment of the present application, the mileage prediction model is updated by a computer device; therefore, when the current second charge and discharge number of the battery is greater than the maximum value of the first charge and discharge number interval, the vehicle controller sends an acquisition request to the computer device to request the computer device to update the mileage prediction model, and the updated mileage prediction model is called the second mileage prediction model.

[0148] Step 602: The computer device receives the acquisition request sent by the vehicle controller, and determines the second charge and discharge number interval to which the second charge and discharge number belongs.

[0149] Step 603: The computer device determines a second sample data set based on the second charge and discharge frequency interval, the second sample data set includes second sample data of a second sample vehicle and label information of the second sample data, the charge and discharge frequency of the second sample vehicle is in the second charge and discharge frequency interval, and the second sample data of the second sample vehicle includes the voltage, current, temperature, SOC, SOH of the battery included in the second sample vehicle and the driving mode of the second sample vehicle, and the label information of the second sample data is the actual cruising range of the second sample vehicle.

[0150] In some embodiments, this step is similar to step 301 and will not be repeated here.

[0151] Step 604: The computer device trains a second mileage prediction model based on the second sample data set, where the second mileage prediction model is used to predict the cruising range of a battery with a charge and discharge frequency within a second charge and discharge frequency range.

[0152] In some embodiments, this step is the same as step 503 and will not be repeated here.

[0153] Step 605: The computer device sends the second mileage prediction model to the vehicle controller.

[0154] Step 606: The vehicle controller receives the second mileage prediction model and stores the second mileage prediction model.

[0155] The vehicle controller stores the second mileage prediction model to facilitate subsequent prediction of the vehicle's cruising range through the second mileage prediction model.

[0156] Step 607: When the vehicle controller determines the cruising range of the vehicle, the vehicle controller obtains the battery's current second voltage, second current, second temperature, second SOC, second SOH and the vehicle's current second driving mode.

[0157] In some embodiments, the steps for the vehicle controller to obtain the battery's current second voltage, second current, second temperature, second SOC, second SOH and the vehicle's current second driving mode may be: the vehicle's battery management system obtains the battery's current second voltage, second current, second temperature, second SOC, and second SOH, and sends the second voltage, second current, second temperature, second SOC, and second SOH to the vehicle controller; the vehicle controller receives the second voltage, second current, second temperature, second SOC, and second SOH, and obtains the vehicle's current second driving mode.

[0158] Step 608: The vehicle controller determines the vehicle's cruising range through a second mileage prediction model based on the battery's current second voltage, second current, second temperature, second SOC, second SOH and the vehicle's current second driving mode.

[0159] The vehicle controller inputs the battery's current second voltage, second current, second temperature, second SOC, second SOH and the vehicle's current second driving mode into a second mileage prediction model to output the vehicle's second predicted SOC; and determines the vehicle's cruising range based on the second predicted SOC and the vehicle's theoretical cruising range.

[0160] In an embodiment of the present application, the second mileage prediction model is deployed in the vehicle controller, so that the vehicle controller can predict the vehicle's cruising range through the second mileage prediction model even when it is not connected to a wireless network, thereby increasing the scope of application.

[0161] Please refer to Figure 7 , which shows a block diagram of a vehicle's cruising range determination device shown in an exemplary embodiment of the present application. The device includes:

[0162] A first determination module 701 is used to determine a first current charge and discharge number of a battery included in the vehicle;

[0163] A second determining module 702 is used to determine a first charge and discharge number interval to which the first charge and discharge number belongs based on the first charge and discharge number;

[0164] A third determination module 703 is used to determine a first mileage prediction model corresponding to the first charge and discharge frequency interval, where the first mileage prediction model is used to predict the cruising range of a battery with charge and discharge times in the first charge and discharge frequency interval;

[0165] A first acquisition module 704 is used to acquire a current first voltage, a first current, a first temperature, a first state of charge SOC, a first state of battery SOH of the battery and a current first driving mode of the vehicle;

[0166] The fourth determination module 705 is used to determine the cruising range of the vehicle through the first mileage prediction model based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle.

[0167] In a possible implementation manner, the device further includes:

[0168] a fifth determination module, configured to determine a first sample data set based on the first charge and discharge frequency interval, wherein the first sample data set includes first sample data of a first sample vehicle and label information of the first sample data, the charge and discharge frequency of the first sample vehicle is within the first charge and discharge frequency interval, and the first sample data of the first sample vehicle includes a voltage, a current, a temperature, a SOC, a SOH of a battery included in the first sample vehicle, and a driving mode of the first sample vehicle, and the label information of the first sample data is an actual cruising range of the first sample vehicle;

[0169] a sixth determination module, configured to determine the predicted cruising range of the first sample vehicle by using an initial first mileage prediction model based on the first sample data set, wherein the first mileage prediction model includes an input layer, a plurality of hidden layers, and an output layer connected in sequence;

[0170] a seventh determination module, configured to determine a loss function value of the first mileage prediction model based on the actual cruising range and the predicted cruising range of the first sample vehicle, wherein the loss function value is used to represent a difference between the actual cruising range and the predicted cruising range of the first sample vehicle;

[0171] A first training module, configured to, when the loss function value is less than a loss function threshold, terminate the training to obtain the first mileage prediction model;

[0172] An updating module is used to update the feature weights and biases of the multiple hidden layers based on the loss function value when the loss function value is not less than the loss function threshold, and then determine the predicted cruising range of the first sample vehicle through the first mileage prediction model based on the first sample data set, until the loss function value of the first mileage prediction model is less than the loss function threshold, at which time the training is completed and the first mileage prediction model is obtained.

[0173] In another possible implementation, the seventh determination module is used to determine a first sample number of the first sample data set; based on the first sample number, the actual cruising range and the predicted cruising range of the first sample vehicle, determine the loss function value of the first mileage prediction model by the following formula 1;

[0174] Formula 1:

[0175] Wherein, L represents the loss function value, N represents the first sample number, i represents the serial number of the first sample vehicle, represents the predicted cruising range of the i-th first sample vehicle, y i represents the actual cruising range of the i-th first sample vehicle.

[0176] In another possible implementation, the update module is used to determine, for the jth hidden layer, the partial derivative of the loss function with respect to the feature weight of the jth hidden layer to obtain a first partial derivative; determine the partial derivative of the loss function with respect to the bias of the jth hidden layer to obtain a second partial derivative; based on the first partial derivative and the second partial derivative, update the feature weight and bias of the jth hidden layer by the following formula 2;

[0177] Formula 2:

[0178] Among them, j represents the number of the hidden layer, represents the updated feature weight of the jth hidden layer, represents the feature weight before the jth hidden layer is updated, α represents the learning rate, represents the first partial derivative, represents the updated bias value of the jth hidden layer, represents the bias value before the jth hidden layer is updated, represents the second partial derivative.

[0179] In another possible implementation, the device further includes:

[0180] an eighth determination module, configured to determine, when the current second charge and discharge number of times of the battery is greater than the maximum value of the first charge and discharge number interval, a second charge and discharge number interval to which the second charge and discharge number belongs;

[0181] a ninth determination module, configured to determine a second sample data set based on the second charge and discharge frequency interval, the second sample data set including second sample data of a second sample vehicle and label information of the second sample data, the charge and discharge frequency of the second sample vehicle being within the second charge and discharge frequency interval, and the second sample data of the second sample vehicle including a voltage, a current, a temperature, a SOC, a SOH of a battery included in the second sample vehicle and a driving mode of the second sample vehicle, and the label information of the second sample data being an actual cruising range of the second sample vehicle;

[0182] A second training module, used for training a second mileage prediction model based on the second sample data set, wherein the second mileage prediction model is used for predicting the cruising range of a battery with a charge and discharge frequency within the second charge and discharge frequency range;

[0183] A second acquisition module, used for acquiring a second voltage, a second current, a second temperature, a second SOC, a second SOH of the battery and a second driving mode of the vehicle;

[0184] The tenth determination module is used to determine the cruising range of the vehicle through the second mileage prediction model based on the current second voltage, second current, second temperature, second SOC, second SOH of the battery and the current second driving mode of the vehicle.

[0185] In another possible implementation, the fourth determination module 705 is used to input the voltage, current, temperature, SOC, SOH of the battery and the current driving mode of the vehicle into the first mileage prediction model to output the predicted SOC of the vehicle; and determine the cruising range of the vehicle based on the predicted SOC and the theoretical cruising range of the vehicle.

[0186] In the embodiment of the present application, on the one hand, the number of times the battery is charged and discharged will affect the service life of the battery. As the number of times the battery is charged and discharged increases, the health of the battery will gradually decrease; therefore, when determining the cruising range of the vehicle, the SOH value of the battery is added as one of the input features, thereby improving the accuracy of the determined cruising range of the vehicle. On the other hand, different mileage prediction models are set for different charge and discharge frequency intervals, thereby improving the accuracy of the determined cruising range of the vehicle. In summary, the embodiment of the present application can improve the accuracy of the determined cruising range of the vehicle.

[0187] It should be noted that the vehicle range determination device provided in the above embodiment only uses the division of the above functional modules as an example when determining the vehicle range. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle range determination device provided in the above embodiment and the vehicle range determination method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0188] Please refer to Figure 8 , Figure 8The block diagram of the structure of a computer device 800 provided by an exemplary embodiment of the present application is shown. The computer device 800 includes a processor (central processing unit, CPU) 801 and a memory 802, wherein the memory 802 stores at least one program code, and the at least one program code is loaded and executed by the processor 801 to implement the vehicle range determination method provided by the above-mentioned various method embodiments. Of course, the computer device 800 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The computer device 800 may also include other components for realizing the functions of the device, which will not be repeated here.

[0189] Those skilled in the art will understand that Figure 8 The structure shown in the figure does not constitute a limitation on the computer device 800, and the computer device 800 may include more or less components than those shown in the figure, or combine some components, or adopt a different arrangement of components.

[0190] Fig. 9 It is a structural schematic diagram of a vehicle controller provided according to an embodiment of the present application.

[0191] Typically, the vehicle controller 900 includes: a main control module 901, a CAN interface 902, a hard-line input interface 903, and a hard-line output interface 904. The main control module 901 is connected to the CAN interface 902, the hard-line input interface 903, and the hard-line output interface 904, respectively.

[0192] The main control module 901 generally includes a processor and a memory. Among them, the processor may include one or more processing cores, such as a 4-core processor, a 9-core processor, etc. The processor may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the vehicle display screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning. The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one computer program, which is used to be executed by the processor to implement the vehicle range determination method provided in the method embodiment of the present application.

[0193] The CAN interface 902 may include a power CAN interface, a motor CAN interface, and a diagnostic CAN interface, wherein the power CAN interface is used to communicate with a power system module of the vehicle, the motor CAN interface is used to communicate with a motor controller of the vehicle, and the diagnostic CAN interface is used to communicate with a diagnostic device.

[0194] The hard-wire input interface 903 is used to receive hard-wire control signals. The hard-wire output interface 904 is used to send control instructions to the electronic control components of the vehicle so that the electronic control components of the vehicle perform corresponding actions. The electronic control components of the vehicle include a power management system, a motor controller, an on-board charger, a body control system, etc.

[0195] The main control module 901 can communicate with the vehicle's power system module, motor controller and diagnostic equipment through the CAN interface 902, and generate control instructions based on the hard-wired control signal received by the hard-wired input interface 903 to send control instructions to the vehicle's electronic control components through the hard-wired output interface 904.

[0196] Those skilled in the art will understand that Fig. 9 The structure shown in the figure does not constitute a limitation on the vehicle controller 900, and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0197] The embodiment of the present application also provides a computer-readable storage medium, in which at least one program code is stored, and the at least one program code is loaded and executed by a processor to implement the vehicle range determination method described in any of the above implementations. Optionally, the storage medium can be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium can be a ROM (Read-Only Memory), a RAM (Random Access Memory), a CD-ROM (Compact Disc Read-Only Memory), a magnetic tape, a floppy disk, and an optical data storage device.

[0198] An embodiment of the present application also provides a computer program product, which stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the method for determining the cruising range of a vehicle as shown in the above embodiments.

[0199] In some embodiments, the computer program product involved in the embodiments of the present application may be deployed and executed on a computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed at multiple locations and interconnected by a communication network. Multiple computer devices distributed at multiple locations and interconnected by a communication network may constitute a blockchain system.

[0200] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0201] The above description is only for the purpose of facilitating those skilled in the art to understand the technical solution of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a vehicle's cruising range, characterized in that: The method comprises: determining a current first charge and discharge number of a battery included in the vehicle; Based on the first charge and discharge times, determining a first charge and discharge times interval to which the first charge and discharge times belong; Determining a first mileage prediction model corresponding to the first charge and discharge frequency interval, the first mileage prediction model being used to predict the cruising range of a battery with charge and discharge frequencies within the first charge and discharge frequency interval; Obtaining a current first voltage, a first current, a first temperature, a first state of charge (SOC), a first state of battery (SOH), and a current first driving mode of the vehicle; Based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle, the cruising range of the vehicle is determined by the first mileage prediction model.

2. The method according to claim 1, characterized in that The method further comprises: Determining a first sample data set based on the first charge and discharge frequency interval, the first sample data set including first sample data of a first sample vehicle and label information of the first sample data, the charge and discharge frequency of the first sample vehicle being within the first charge and discharge frequency interval, and the first sample data of the first sample vehicle including voltage, current, temperature, SOC, SOH of a battery included in the first sample vehicle and a driving mode of the first sample vehicle, and the label information of the first sample data being an actual cruising range of the first sample vehicle; Determining a predicted cruising range of the first sample vehicle using an initial first mileage prediction model based on the first sample data set, the first mileage prediction model comprising an input layer, a plurality of hidden layers, and an output layer connected in sequence; Determining a loss function value of the first mileage prediction model based on the actual cruising range and the predicted cruising range of the first sample vehicle, the loss function value being used to represent the difference between the actual cruising range and the predicted cruising range of the first sample vehicle; When the loss function value is less than the loss function threshold, the training is completed to obtain the first mileage prediction model; When the loss function value is not less than the loss function threshold, the feature weights and biases of the multiple hidden layers are updated based on the loss function value, and then the predicted cruising range of the first sample vehicle is determined by the first mileage prediction model based on the first sample data set, until the loss function value of the first mileage prediction model is less than the loss function threshold. At this time, the training is completed and the first mileage prediction model is obtained.

3. The method according to claim 2, characterized in that The determining the loss function value of the first mileage prediction model based on the actual cruising range and the predicted cruising range of the first sample vehicle includes: determining a first sample number of the first sample data set; Based on the first sample number, the actual cruising range and the predicted cruising range of the first sample vehicle, a loss function value of the first mileage prediction model is determined by the following formula 1; Formula 1: Wherein, L represents the loss function value, N represents the number of the first samples, i represents the serial number of the first sample vehicle, represents the predicted cruising range of the i-th first sample vehicle, y i represents the actual cruising range of the i-th first sample vehicle.

4. The method according to claim 2 or 3, characterized in that The updating of the feature weights and biases of the plurality of hidden layers based on the loss function value includes: For the j-th hidden layer, determining the partial derivative of the loss function with respect to the feature weight of the j-th hidden layer to obtain a first partial derivative; Determining a partial derivative of the loss function with respect to a bias of the j-th hidden layer to obtain a second partial derivative; Based on the first partial derivative and the second partial derivative, update the feature weight and bias of the j-th hidden layer using the following formula 2; Formula 2: Among them, j represents the sequence number of the hidden layer, represents the updated feature weight of the jth hidden layer, represents the feature weight before the jth hidden layer is updated, α represents the learning rate, represents the first partial derivative, represents the updated bias value of the jth hidden layer, represents the bias value before the jth hidden layer is updated, represents the second partial derivative.

5. The method according to claim 1, wherein The method further comprises: When the current second charge and discharge number of the battery is greater than the maximum value of the first charge and discharge number interval, determining the second charge and discharge number interval to which the second charge and discharge number belongs; Determining a second sample data set based on the second charge and discharge frequency interval, the second sample data set including second sample data of a second sample vehicle and label information of the second sample data, the charge and discharge frequency of the second sample vehicle being within the second charge and discharge frequency interval, and the second sample data of the second sample vehicle including voltage, current, temperature, SOC, SOH of a battery included in the second sample vehicle and a driving mode of the second sample vehicle, and the label information of the second sample data being an actual cruising range of the second sample vehicle; Training a second mileage prediction model based on the second sample data set, where the second mileage prediction model is used to predict the cruising range of a battery with charge and discharge times within the second charge and discharge time interval; Obtaining a second current, a second temperature, a second SOC, a second SOH, and a second driving mode of the vehicle; Based on the current second voltage, second current, second temperature, second SOC, second SOH of the battery and the current second driving mode of the vehicle, the cruising range of the vehicle is determined by the second mileage prediction model.

6. The method according to claim 1, characterized in that The determining the cruising range of the vehicle using the first mileage prediction model based on the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle includes: Inputting the current first voltage, first current, first temperature, first SOC, first SOH of the battery and the current first driving mode of the vehicle into the first mileage prediction model to output the first predicted SOC of the vehicle; The cruising range of the vehicle is determined based on the first predicted SOC and the theoretical cruising range of the vehicle.

7. A device for determining a vehicle's cruising range, characterized in that: The device comprises: A first determining module, configured to determine a first current charge and discharge number of a battery included in the vehicle; a second determining module, configured to determine, based on the first charge and discharge times, a first charge and discharge times interval to which the first charge and discharge times belong; a third determining module, configured to determine a first mileage prediction model corresponding to the first charge and discharge frequency interval, wherein the first mileage prediction model is configured to predict the cruising range of a battery with charge and discharge frequencies within the first charge and discharge frequency interval; a first acquisition module, configured to acquire a current first voltage, a first current, a first temperature, a first state of charge (SOC), a first state of battery (SOH), and a current first driving mode of the vehicle; A fourth determination module is used to determine the vehicle's cruising range through the first mileage prediction model based on the battery's current first voltage, first current, first temperature, first SOC, first SOH and the vehicle's current first driving mode.

8. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the method for determining the cruising range of a vehicle as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the method for determining the cruising range of a vehicle as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The product stores at least one program code, and the at least one program code is used to be executed by a processor to implement the method for determining the cruising range of a vehicle as described in any one of claims 1 to 6.

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