Battery charging control method and related devices

Abstract

This application discloses a battery charging control method and related apparatus, belonging to the field of vehicles. The method includes: acquiring the state information of a battery in a vehicle, the state information including at least the current state of charge (SOC), current, voltage, and temperature; determining the target output voltage of the vehicle's DC-DC converter (DCDC) based on the battery state information using a neural network model; and controlling the DCDC to charge the battery according to the target output voltage. That is, by determining the target output voltage of the DCDC using a neural network model, the charging control of the battery is improved to better maintain the battery's SOC at a suitable level.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a battery charging control method and related device. Background Technology

[0002] With the development of society and the economy, and considering the increasing depletion of petroleum energy and the environmental pollution caused by vehicle exhaust emissions, the industry has begun to seek new energy sources for vehicle power. Among these, electric vehicles using batteries (such as lead-acid batteries and lithium batteries) as the primary energy source are currently an important research direction. A battery is a container for storing electrical energy. Batteries can be charged and discharged to provide the electrical energy required by the vehicle. However, overcharging and over-discharging will affect the battery's lifespan. Therefore, it is necessary to maintain the battery's SOC (state of charge) at a suitable level, and maintaining SOC mainly relies on controlling the output voltage of the vehicle's DC-DC (Direct Current to DC converter). How to control the DC-DC output voltage has become an important research direction in the industry. Summary of the Invention

[0003] This application provides a battery charging control method and related apparatus, which can better control the charging of the battery, thereby maintaining the battery's State of Charge (SOC) at a suitable level. The technical solution is as follows:

[0004] On one hand, a battery charging control method is provided, the method comprising:

[0005] Obtain the status information of the battery in the vehicle, the status information including at least the current SOC, current, voltage and temperature;

[0006] Based on the state information of the battery, the target output voltage of the vehicle's DC-DC converter is determined by a neural network model;

[0007] The DC-DC converter is controlled to charge the battery according to the target output voltage.

[0008] In one possible implementation, the neural network model includes a first neural network model;

[0009] The step of determining the target output voltage of the vehicle's DC-DC converter using a neural network model based on the battery's state information includes:

[0010] The state information of the battery is input into the first neural network model to obtain the reference SOC output by the first neural network model. The reference SOC is the SOC that the battery needs to reach during this charging.

[0011] The target output voltage is determined based on the battery's state information and the reference SOC.

[0012] In one possible implementation, the status information further includes activation indication information, which indicates whether the battery is turned on;

[0013] The step of inputting the state information of the battery into the first neural network model to obtain the reference SOC output by the first neural network model includes:

[0014] When the activation indication information indicates that the battery is activated, the battery status information is input into the first neural network model to obtain the reference SOC output by the first neural network model.

[0015] In one possible implementation, the neural network model includes a second neural network model;

[0016] The step of determining the target output voltage of the vehicle's DC-DC converter (DCDC) using a neural network model based on the battery's state information includes:

[0017] Based on the state information of the battery, a reference SOC is determined, which is the SOC that the battery is to reach during this charge.

[0018] The battery's state information and the reference SOC are input into the second neural network model to obtain the target output voltage output by the second neural network model.

[0019] In one possible implementation, the status information further includes at least one of the current SOC accuracy, fault information, SOH (State of Health), and power-on indication information, wherein the power-on indication information indicates whether the battery is powered on, and the fault information indicates whether the battery is faulty.

[0020] On the other hand, a battery charging control device is provided, which includes an acquisition module, a determination module and a control module;

[0021] The acquisition module is used to acquire the status information of the battery in the vehicle, the status information including at least the current SOC, current, voltage and temperature;

[0022] The determination module is used to determine the target output voltage of the vehicle's DC-DC converter based on the state information of the battery through a neural network model;

[0023] The control module is used to control the DC-DC converter to charge the battery according to the target output voltage.

[0024] In one possible implementation, the neural network model includes a first neural network model;

[0025] The determining module includes:

[0026] The first determining submodule is used to input the state information of the battery into the first neural network model to obtain the reference SOC output by the first neural network model. The reference SOC is the SOC that the battery needs to reach during this charging.

[0027] The second determining submodule is used to determine the target output voltage based on the state information of the battery and the reference SOC.

[0028] In one possible implementation, the status information further includes activation indication information, which indicates whether the battery is turned on;

[0029] The first determining submodule is specifically used for:

[0030] When the activation indication information indicates that the battery is activated, the battery status information is input into the first neural network model to obtain the reference SOC output by the first neural network model.

[0031] In one possible implementation, the neural network model includes a second neural network model;

[0032] The determining module includes:

[0033] The third determining submodule is used to determine a reference SOC based on the state information of the battery, wherein the reference SOC is the SOC that the battery is to reach during this charging.

[0034] The fourth determining submodule is used to input the state information of the battery and the reference SOC into the second neural network model to obtain the target output voltage output by the second neural network model.

[0035] In one possible implementation, the status information further includes at least one of the current SOC accuracy, fault information, battery health status (SOH), and power-on indication information, wherein the power-on indication information indicates whether the battery is powered on, and the fault information indicates whether the battery is faulty.

[0036] On the other hand, a battery charging control device is provided, the device comprising:

[0037] processor;

[0038] Memory used to store processor-executable instructions;

[0039] The processor is configured to execute some or all of the steps in the above-described battery charging control method.

[0040] On the other hand, a vehicle is provided, the vehicle including a memory and a controller, the memory for storing a computer program, and the controller for executing the computer program stored in the memory to implement the battery charging control method described above.

[0041] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, the above-described battery charging control method is implemented.

[0042] On the other hand, a computer program product is provided, which stores computer instructions that, when executed by a processor, implement the above-described battery charging control method.

[0043] The technical solution provided in this application can bring at least the following beneficial effects:

[0044] After acquiring information on the battery's current SOC, current, voltage, and temperature, a neural network model is used to determine the target output voltage of the DC-DC converter. Since the neural network model is trained, and the accuracy and prediction efficiency of the trained neural network model in analyzing and predicting the target output voltage of the DC-DC converter are recognized, the target output voltage determined by this neural network model is more consistent with the battery's current actual power supply situation. By controlling the battery charging according to this target output voltage, the battery's SOC can be better maintained at an appropriate level. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application;

[0047] Figure 2 This is a flowchart of a battery charging control method provided in an embodiment of this application;

[0048] Figure 3This is a flowchart of another battery charging control method provided in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the structure of a battery charging control device provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0052] Before providing a detailed explanation of the battery charging control method provided in the embodiments of this application, the application scenarios involved in the embodiments of this application will be introduced first.

[0053] With the rapid development of energy technology, lithium batteries, lead-acid batteries, and other types of batteries have been widely used in vehicles. However, the operating conditions of these batteries are complex, and the charging efficiency of lithium batteries is greatly affected by factors such as the battery's own charge level and temperature. Changes in these charging parameters can lead to variations in the required charging voltage. Significant voltage fluctuations can accelerate battery lifespan degradation, reduce structural stability, and ultimately pose safety hazards during vehicle operation. Therefore, current battery charging stability is relatively poor.

[0054] Based on this, the present application provides a charging control method for a storage battery, which can improve the charging stability of the storage battery and slow down the rate of battery wear.

[0055] Before providing a detailed explanation of the charging control method provided in the embodiments of this application, the implementation environment involved in the embodiments of this application will be introduced first.

[0056] Please refer to Figure 1 , Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application. The implementation environment includes a battery 101, a battery sensor 102, a DC-DC controller 103, and a DC-DC controller 104.

[0057] The battery 101 is used to provide electrical energy to the vehicle. For example, the battery 101 can be connected to one or more electrical loads to provide electrical energy to the connected electrical loads.

[0058] Vehicles typically also include a power battery (not shown in the diagram). The power battery is used to charge the storage battery 101. In electric and hybrid vehicles, the power battery also typically provides the vehicle with power.

[0059] In some embodiments, the current output by the power battery is a high-voltage current, while the current required for charging the storage battery 101 is a low-voltage current. Based on this, a DC-DC converter 104 can also be connected between the power battery and the storage battery 101. The DC-DC converter 104 is used to adjust the voltage output by the power battery to replenish the storage battery 101 with energy based on the current required by the charging voltage of the storage battery 101.

[0060] The battery sensor 102 is connected to the battery 101 and is used to obtain the status information of the battery 101 and send it to the DC-DC controller 103.

[0061] The DC-DC controller 103 can be connected to the battery sensor 102 and the DC-DC controller 104. By adjusting the output voltage of the DC-DC controller 104, the charging voltage of the battery 101 can be controlled. For example, the DC-DC controller 103 can acquire the status information of the battery 101 collected by the battery sensor 102, and based on the status information of the battery 101, determine the voltage required for charging the battery 101, and then adjust the output voltage of the DC-DC controller 104 so that the output voltage of the DC-DC controller 104 can meet the charging requirements of the battery 101.

[0062] In this embodiment of the application, the implementation environment also includes a battery management system (hereinafter referred to as the management system). Figure 1 (Not shown in the image) The battery management system can be connected to the battery sensor 102 and the DC-DC controller 103. The battery management system is used to obtain the status information of the battery 101 collected by the battery sensor 102 and send the status information to the DC-DC controller 103.

[0063] The battery management system can be the vehicle's LBMS (Low Voltage Battery Management System), used to detect and monitor parameters such as voltage, current, temperature, SOC, and SOH of the battery 101.

[0064] It should be noted that the application scenarios and implementation environments described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. That is, those skilled in the art should understand that the above-mentioned battery 101, battery sensor 102, DC-DC controller 103, DC-DC controller 104, power battery, and battery management system are merely examples. Other existing or future power batteries, controllers, batteries, battery management systems, etc., if applicable to the embodiments of this application, should also be included within the protection scope of the embodiments of this application, and are hereby incorporated by reference. In other words, those skilled in the art will know that, with the evolution of application scenarios and vehicle system architecture, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0065] The battery charging control method provided in the embodiments of this application will now be explained in detail.

[0066] The battery charging control method provided in this application embodiment is executed by a controller (also called a processor). The controller can be, for example, the DC-DC controller described above, or other controllers in a vehicle, such as a ZCU (Zone Control Unit), VCU (Vehicle Control Unit), etc. The controller can be a general-purpose CPU (Central Processing Unit), NP (Network Processor), microprocessor, or one or more integrated circuits used to implement the solution of this application, such as an ASIC (Application-Specific Integrated Circuit), a PLD (Programmable Logic Device), or a combination thereof. The PLD can be a CPLD (Complex Programmable Logic Device), FPGA (Field-Programmable Gate Array), GAL (Generic Array Logic), or any combination thereof.

[0067] Figure 2 This is a flowchart illustrating a battery charging control method provided in an embodiment of this application. The method is applied to a controller, which can be a controller in a vehicle or a controller in other devices or equipment; this embodiment does not limit this. Please refer to... Figure 2 The method includes the following steps.

[0068] Step 201: Obtain the status information of the battery in the vehicle. The status information includes at least the current state of charge (SOC), current, voltage, and temperature.

[0069] SOC, or State of Charge, indicates the percentage of the battery's remaining charge relative to its rated capacity, i.e., the current remaining capacity of the battery.

[0070] In some embodiments, the acquired status information further includes at least one of the following: current SOC accuracy, fault information, SOH, and power-on indication information. The power-on indication information indicates whether the battery is powered on, i.e., whether it is supplying power, while the fault information indicates whether the battery is faulty.

[0071] Since the embodiments of this application are mainly used to implement charging control of the battery, in one possible implementation, step 201 is executed only when the battery needs to be charged. Based on this, in some scenarios, the current SOC and / or discharge voltage of the battery can be periodically acquired. If the current SOC of the battery is less than the SOC threshold and / or the discharge voltage of the battery is less than the voltage threshold, it is considered that the battery currently has insufficient power and needs to be charged.

[0072] In this embodiment, the battery status information can be acquired by the battery management system and sent to the controller. Specifically, the battery management system acquires the battery status information through battery sensors; that is, the battery sensors collect the battery status information and send it to the battery management system.

[0073] In some embodiments, the controller also acquires the upper current limit, lower current limit, upper voltage limit, and lower voltage limit of the battery to constrain the output voltage and output current of the DC-DC converter. The upper current limit, lower current limit, upper voltage limit, and lower voltage limit of the battery can also be included in the battery's state information.

[0074] Table 1 shows examples of information that the controller can acquire in the embodiments of this application. Referring to Table 1, the controller can acquire the battery's current SOC, the accuracy of the current SOC, current, temperature, voltage, upper current limit, lower current limit, upper voltage limit, and lower voltage limit. Here, the sender refers to the party acquiring the information, and the receiver refers to the party receiving the information. In the embodiments of this application, the sender can be an LBMS or a battery sensor, etc., and the receiver can be a controller, such as a ZCU.

[0075] Table 1

[0076]

[0077]

[0078] It should be understood that Table 1 is an example and is not intended to limit the embodiments of this application. In specific implementations, the senders of each piece of information may be the same or different, and the accuracy and range of each piece of information may also be selected according to actual needs. The embodiments of this application do not limit these aspects.

[0079] Step 202: Based on the battery status information, determine the target output voltage of the vehicle's DC-DC converter (DCDC) using a neural network model.

[0080] In other words, the controller uses a neural network model to determine the target output voltage of the DC-DC converter.

[0081] In this embodiment of the application, the neural network model includes a first neural network model; based on the state information of the battery, the target output voltage of the vehicle's DC-DC converter (DCDC) is determined by the neural network model, including: inputting the state information of the battery into the first neural network model to obtain a reference SOC output by the first neural network model, wherein the reference SOC is the SOC that the battery is to reach during this charging; and determining the target output voltage based on the state information of the battery and the reference SOC.

[0082] In one possible implementation, if the status information also includes activation indication information, and the activation indication information indicates that the battery is on, then the battery status information is input into the first neural network model to obtain the reference SOC output by the first neural network model. That is, when the vehicle is running (at which time the battery is on), the reference SOC is dynamically determined by the first neural network model.

[0083] In one possible implementation, if the battery indicator message indicates that the battery is not powered on, then a preset static reference SOC is determined as the reference SOC. That is, when the vehicle is parked (and the battery is not powered on), the static reference SOC is determined as the reference SOC. The static reference SOC can be, for example, 100% or another large value.

[0084] For example, “OFF” or “Comfortable” indicates that the battery is not turned on, and “ON” indicates that the battery is turned on. When the battery’s on-state information is “OFF” or “Comfortable”, the reference SOC is a static reference SOC. When the battery’s on-state information changes from “OFF” or “Comfortable” to “ON”, the reference SOC is dynamically determined by the first neural network model.

[0085] In some other embodiments, the controller determines a reference SOC based on the battery temperature and the current SOC of the power battery. In one possible implementation, the controller determines the reference SOC according to a first mapping relationship based on the battery temperature and the current SOC of the power battery. The first mapping relationship represents a mapping function or mapping table between the battery temperature, the power battery SOC, and the reference SOC, and can be flexibly selected based on actual usage requirements; this application does not limit this specific choice.

[0086] In one possible implementation, the first mapping relationship can be determined based on experimental statistics and expert experience.

[0087] After determining the reference SOC, the controller determines the target output voltage of the DC-DC converter based on the battery's state information and the reference SOC.

[0088] In some embodiments, the neural network model includes a second neural network model. The controller inputs the battery's state information and a reference SOC into the second neural network model to obtain the target output voltage output by the second neural network model. That is, the controller determines the target output voltage through the second neural network model.

[0089] In other embodiments, the controller determines the target output voltage based on the battery's state information and a reference SOC, according to a second mapping relationship. This second mapping relationship represents a mapping function or table between the battery's state, the reference SOC, and the DC-DC output voltage. The specific mapping relationship can be flexibly selected based on actual usage requirements, and this application does not limit its specific choice.

[0090] As an example, the second mapping relationship represents a mapping table between the battery temperature, reference SOC, and the DC-DC output voltage. The controller determines the target output voltage based on the battery temperature and reference SOC according to the second mapping relationship.

[0091] Table 2 is a mapping table between the temperature, reference SOC, and output voltage of a battery provided in an embodiment of this application. In Table 2, the first row represents the battery temperature, the first column represents the reference SOC, and the remaining data represent the output voltage of the DC-DC converter.

[0092] Table 2

[0093]

[0094] It should be understood that Table 2 is an example and is not intended to limit the embodiments of this application. In specific implementations, the output voltage of the DC-DC converter corresponding to different temperatures and reference SOCs can be determined according to actual needs, and the embodiments of this application do not limit this. In one possible implementation, the second mapping relationship can be determined based on experimental statistical data and expert experience, etc.

[0095] Taking Table 2 as an example, after obtaining the battery temperature and reference SOC, the controller can determine the DC-DC output voltage corresponding to the battery temperature and reference SOC by interpolation based on Table 2, and obtain the target output voltage.

[0096] In other embodiments, the controller can determine the SOC difference between the current SOC of the battery and a reference SOC, determine the target charging current corresponding to the SOC difference, acquire the battery current, determine the current difference between the battery current and the target charging current, determine the target charging voltage corresponding to the current difference, and set the target charging voltage as the target output voltage of the DC-DC converter. This implementation method can be called a closed-loop compensation algorithm.

[0097] Specifically, the controller can determine the target charging current corresponding to the SOC difference based on a third mapping relationship, which characterizes the mapping relationship between the SOC difference and the charging current. The controller can also determine the target charging voltage corresponding to the current difference based on a fourth mapping relationship, which characterizes the mapping relationship between the current difference and the target charging voltage. In one possible implementation, the third and fourth mapping relationships can be determined based on experimental statistical data and expert experience.

[0098] In some other embodiments, the controller may also determine the target charging current corresponding to the SOC difference based on the current SOC and reference SOC of the battery using proportional, integral, and derivative control algorithms; and determine the target output voltage based on the current difference between the battery current and the target charging current using proportional, integral, and derivative control algorithms.

[0099] Among them, proportional, integral, and derivative control algorithms are simply referred to as PID control algorithms, where P stands for proportional control, I stands for integral control, and D stands for derivative control.

[0100] In some embodiments, the controller also acquires the output configuration word of the DC-DC converter. The output configuration word is either a first value or a second value, with different output configuration words representing different configurations of the DC-DC converter, which are related to the DC-DC converter's model and specifications. Specifically, when the output configuration word is the first value, the controller determines the DC-DC converter's output voltage according to a second mapping relationship. When the output configuration word is the second value, the controller determines the DC-DC converter's output voltage according to the aforementioned closed-loop compensation algorithm.

[0101] The first and second values ​​can be 0 and 1 respectively, or other values.

[0102] In some other embodiments, the controller determines whether a battery fault exists based on fault information. If a battery fault exists, the backup voltage is used to determine the DC-DC output voltage. If the battery is not faulty, the controller then determines the method for determining the DC-DC output voltage based on the DC-DC output configuration word.

[0103] Figure 3 This is a flowchart of another battery charging control method provided in an embodiment of this application. See also... Figure 3 First, the vehicle status is determined (based on the battery's on / off indicator). If the vehicle is stationary, the static reference SOC is set as the reference SOC. If the vehicle is moving, the reference SOC is dynamically determined (e.g., using a first neural network model). Next, the battery is checked for faults. If a fault exists, the backup voltage is used to determine the DC-DC output voltage. If no fault exists, the DC-DC output configuration word is checked. If the output configuration word is the first value (e.g., 1), the DC-DC output voltage is determined according to the MAP table (i.e., the second mapping relationship). If the output configuration word is the second value, the DC-DC output voltage is determined according to the closed-loop compensation algorithm described above.

[0104] Based on the above description, the controller first determines the reference SOC, and then determines the target output voltage based on the reference SOC. The reference SOC can be determined using the first neural network model, or not. After obtaining the reference SOC, the target output voltage can be determined using the second neural network model, or not.

[0105] Based on this, another way for the controller to determine the target output voltage of the vehicle's DC-DC converter (DCDC) based on the battery's state information and through a neural network model is as follows: Based on the battery's state information, a reference SOC is determined, and the battery's state information and the reference SOC are input into a second neural network model to obtain the target output voltage output by the second neural network model.

[0106] The first neural network model and the second neural network model in the embodiments of this application can be models with different architectures or models with the same architecture; this application does not limit this. The structures of the first neural network model and the second neural network model can also be the same or different; this application does not limit this either. In one possible implementation, the first neural network model is a convolutional neural network model, a recurrent convolutional neural network model, or other deep learning models, and the second neural network model is a convolutional neural network model, a recurrent convolutional neural network model, or other deep learning models.

[0107] Furthermore, the embodiments of this application do not limit the training method, number of model parameters, number of layers, etc., of the first and second neural network models. The first neural network model can be trained using a first sample set, which includes multiple state information samples and a reference SOC corresponding to each state information sample. The second neural network model can be trained using a second sample set, which includes multiple state information samples, a reference SOC corresponding to each state information sample, and the output voltage of the DC-DC converter.

[0108] In some other embodiments, the controller inputs the battery's state information into a neural network model to obtain the target output voltage of the DC-DC converter from the neural network model. That is, the target output voltage is directly obtained after analyzing the battery's state information using a neural network model. This application does not limit the architecture, structure, training method, number of model parameters, or number of layers of the neural network model. In one possible implementation, the neural network model is a convolutional neural network model, a recurrent convolutional neural network model, or other deep learning models. The neural network model can be trained using a third sample set, which includes multiple state information samples and the output voltage of the DC-DC converter corresponding to each state information sample.

[0109] Step 203: Control the DC-DC converter to charge the battery according to the target output voltage.

[0110] After determining the target output voltage of the DC-DC converter, the controller controls the DC-DC converter to charge the battery according to the target output voltage, that is, to control the output voltage of the DC-DC converter to be the target output voltage.

[0111] In summary, in this embodiment, after obtaining information on the current SOC, current, voltage, and temperature of the battery, the target output voltage of the DC-DC converter is determined through a neural network model. Since the neural network model is trained, and the accuracy and prediction efficiency of the trained neural network model in analyzing and predicting the target output voltage of the DC-DC converter are recognized, the target output voltage determined by the neural network model is more consistent with the actual power supply situation of the battery. By controlling the charging of the battery according to the target output voltage, the SOC of the battery can be better maintained at a suitable level.

[0112] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and the embodiments of this application will not be described in detail one by one.

[0113] Figure 4 This is a schematic diagram of the structure of a battery charging control device provided in an embodiment of this application. The battery charging control device 400 can be implemented as part or all of a controller by software, hardware, or a combination of both. This controller can be a controller for a vehicle or other equipment. Please refer to... Figure 4 The battery charging control device 400 includes: an acquisition module 401, a determination module 402, and a control module 403.

[0114] The acquisition module 401 is used to acquire the status information of the battery in the vehicle. The status information includes at least the current SOC, current, voltage and temperature.

[0115] The determination module 402 is used to determine the target output voltage of the vehicle's DC-DC converter based on the battery's state information and through a neural network model.

[0116] The control module 403 is used to control the DC-DC converter to charge the battery according to the target output voltage.

[0117] In one possible implementation, the neural network model includes a first neural network model;

[0118] Module 402 is defined, including:

[0119] The first determining submodule is used to input the state information of the battery into the first neural network model to obtain the reference SOC output by the first neural network model. The reference SOC is the SOC that the battery needs to achieve during this charging.

[0120] The second determination submodule is used to determine the target output voltage based on the battery's state information and the reference SOC.

[0121] In one possible implementation, the status information also includes an activation indication, which indicates whether the battery is on.

[0122] The first determining submodule is specifically used for:

[0123] When the battery is activated as indicated by the activation indicator, the battery status information is input into the first neural network model to obtain the reference SOC output by the first neural network model.

[0124] In one possible implementation, the neural network model includes a second neural network model;

[0125] Module 402 is defined, including:

[0126] The third determination submodule is used to determine the reference SOC based on the battery's state information. The reference SOC is the SOC that the battery is expected to reach during this charge.

[0127] The fourth determination submodule is used to input the battery's state information and reference SOC into the second neural network model to obtain the target output voltage output by the second neural network model.

[0128] In one possible implementation, the status information also includes at least one of the following: the accuracy of the current SOC, fault information, battery health status (SOH), and power-on indication information, wherein the power-on indication information indicates whether the battery is powered on, and the fault information indicates whether the battery is faulty.

[0129] In this embodiment, after obtaining information on the current SOC, current, voltage, and temperature of the battery, the target output voltage of the DC-DC converter is determined through a neural network model. Since the neural network model must be trained, and the performance of the trained neural network model in terms of accuracy and prediction efficiency in analyzing and predicting the target output voltage of the DC-DC converter must be recognized, the target output voltage determined by the neural network model is more in line with the actual power supply situation of the battery. By controlling the charging of the battery according to the target output voltage, the SOC of the battery can be better maintained at a suitable level.

[0130] It should be noted that the battery charging control device provided in the above embodiments is only illustrated by the division of the above functional modules when controlling the charging of the battery. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the battery charging control device and the battery charging control method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0131] Figure 5This is a structural block diagram of a vehicle 500 provided in an embodiment of this application. Typically, the vehicle 500 includes a controller 501 and a memory 502.

[0132] Controller 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. Controller 501 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA, and PLA (Programmable Logic Array). Controller 501 may also include a main processor and a coprocessor. The main processor, also known as a CPU, is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, controller 501 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, controller 501 may also include an AI (Artificial Intelligence) processor, such as an NPU (Neural Network Processing Unit), which is used to handle computational operations related to machine learning.

[0133] The memory 502 may include one or more non-transitory computer-readable storage media. The memory 502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 502 are used to store at least one instruction, which is executed by the controller 501 to implement the battery charging control method provided in the method embodiments of this application.

[0134] This application also provides a computer-readable storage medium storing a computer program. When executed by the controller described above, the computer program implements the steps of the battery charging control method described above. For example, the computer-readable storage medium may be ROM (read-only memory), RAM (random access memory), CD-ROM (compact disc read-only memory), magnetic tape, floppy disk, and optical data storage device, etc.

[0135] It is worth noting that the computer-readable storage medium mentioned in the embodiments of this application can be a non-volatile storage medium, in other words, it can be a non-transient storage medium.

[0136] It should be understood that all or part of the steps of the above embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-described computer-readable storage medium.

[0137] That is, in some embodiments, a computer program product is also provided, which stores computer instructions that, when executed by the controller, implement the steps of the battery charging control method described above. Alternatively, a computer program is provided that, when executed by the controller, implements the battery charging control method described above.

[0138] It should be understood that "at least one" as mentioned herein refers to one or more, and "multiple" refers to two or more. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In addition, in order to clearly describe the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., are not necessarily different.

[0139] 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 used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the status information involved in the embodiments of this application was obtained under full authorization.

[0140] The above descriptions are embodiments provided in this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A battery charging control method, characterized in that, The method includes: Obtain the status information of the battery in the vehicle, the status information including at least the current state of charge (SOC), current, voltage, and temperature; Based on the state information of the battery, the target output voltage of the vehicle's DC-DC converter is determined by a neural network model; The DC-DC converter is controlled to charge the battery according to the target output voltage; The neural network model includes a first neural network model; determining the target output voltage of the vehicle's DC-DC converter (DCDC) based on the battery's state information using the neural network model includes: inputting the battery's state information into the first neural network model to obtain a reference SOC output by the first neural network model, where the reference SOC is the SOC the battery is expected to achieve during this charging cycle; and determining the target output voltage based on the battery's state information and the reference SOC; or... The neural network model includes a second neural network model; the step of determining the target output voltage of the vehicle's DC-DC converter (DCDC) based on the battery's state information through the neural network model includes: determining a reference SOC based on the battery's state information; inputting the battery's state information and the reference SOC into the second neural network model to obtain the target output voltage output by the second neural network model.

2. The method as described in claim 1, characterized in that, The status information also includes activation indication information, which indicates whether the battery is activated. The step of inputting the state information of the battery into the first neural network model to obtain the reference SOC output by the first neural network model includes: When the activation indication information indicates that the battery is activated, the battery status information is input into the first neural network model to obtain the reference SOC output by the first neural network model.

3. The method as described in claim 2, characterized in that, The status information also includes at least one of the accuracy of the current state of charge (SOC), fault information, and battery health status (SOH), wherein the fault information indicates whether the battery is faulty.

4. The method as described in claim 1, characterized in that, The status information also includes at least one of the following: the accuracy of the current state of charge (SOC), fault information, battery health status (SOH), and start-up indication information. The start-up indication information indicates whether the battery is turned on, and the fault information indicates whether the battery is faulty.

5. A battery charging control device, characterized in that, The device includes: The acquisition module is used to acquire the status information of the battery in the vehicle, the status information including at least the current state of charge (SOC), current, voltage and temperature; The determination module is used to determine the target output voltage of the vehicle's DC-DC converter (DCDC) based on the state information of the battery using a neural network model. A charging module is used to control the DC-DC converter to charge the battery according to the target output voltage; The neural network model includes a first neural network model; the determining module includes: The first determining submodule is used to input the state information of the battery into the first neural network model to obtain the reference SOC output by the first neural network model. The reference SOC is the SOC that the battery needs to achieve during this charging. The second determining submodule is used to determine the target output voltage based on the battery's state information and the reference SOC; or, The neural network model includes a second neural network model; the determining module includes: The third determining submodule is used to determine the reference SOC based on the state information of the battery; The fourth determining submodule is used to input the state information of the battery and the reference SOC into the second neural network model to obtain the target output voltage output by the second neural network model.

6. A vehicle, characterized in that, The vehicle includes a memory and a controller, the memory being used to store a computer program, and the controller being used to run the computer program to perform the steps of the method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by the controller, implement the method described in any one of claims 1-4.

8. A computer program product containing instructions, characterized in that, When the instructions are executed on the controller, the method described in any one of claims 1-4 is implemented.

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