Vehicle charging method and device, vehicle, medium and program product

Abstract

The present disclosure provides a vehicle charging method, device, vehicle, medium and program product, relating to the technical field of vehicles, the method comprising: in the process of charging the vehicle, obtaining charging data corresponding to the vehicle, wherein the charging data comprises heat data, and the heat data comprises at least one of the following: heat data generated by the vehicle battery, heat data provided by the thermal management system of the vehicle, and heat data provided by the environment in which the vehicle is located; determining a plurality of candidate control parameters according to the charging data; determining one of the plurality of candidate control parameters as a target control parameter; and controlling the vehicle battery charging according to the target control parameter. The present disclosure selects a better one from a plurality of candidate control parameters as a target control parameter to control the vehicle battery charging, thereby avoiding the use of excessive cooling power to cause large temperature fluctuations of the vehicle battery and reducing the damage to the vehicle battery.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and more particularly to a vehicle charging method, apparatus, vehicle, medium, and program product. Background Technology

[0002] During the charging process of electric vehicles, both excessively high and low battery temperatures can affect charging efficiency and battery lifespan. Currently, the battery thermal management systems in vehicles typically employ a fixed-threshold temperature control strategy during charging. For example, if the battery temperature exceeds the threshold, the thermal management system switches to maximum cooling power to forcibly cool the battery. Large temperature fluctuations during charging can damage battery health and reduce its lifespan. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a vehicle charging method, apparatus, vehicle, medium, and program product.

[0004] According to a first aspect of the present disclosure, a vehicle charging method is provided. The vehicle charging method includes: during vehicle charging, acquiring charging data corresponding to the vehicle, wherein the charging data includes heat data, and the heat data includes at least one of the following: heat data generated by the vehicle battery, heat data provided by the vehicle's thermal management system, and heat data provided by the environment in which the vehicle is located; determining a plurality of candidate control parameters based on the charging data; determining one of the plurality of candidate control parameters as a target control parameter, and controlling the charging of the vehicle battery according to the target control parameter. Compared to the method of using maximum cooling power in related technologies, this embodiment selects a superior one from multiple candidate control parameters as the target control parameter to control the charging of the vehicle battery, avoiding large temperature fluctuations of the vehicle battery caused by excessive cooling power, reducing damage to the vehicle battery, and eliminating unnecessary power consumption by using the target control parameter, thus reducing charging power consumption. Furthermore, compared to the method of using only the vehicle battery temperature for judgment in related technologies, this embodiment combines multiple heat data from the vehicle for more precise charging control, further avoiding large temperature fluctuations of the vehicle battery and reducing damage to the vehicle battery.

[0005] In some possible implementations, the charging data may further include at least one of the following: charging current for the vehicle battery, state of charge (SOC) of the vehicle battery, terminal voltage of the vehicle battery, and water flow rate of the water pump in the thermal management system.

[0006] In some possible implementations, determining multiple candidate control parameters based on the charging data includes: obtaining candidate control parameters based on the charging data; obtaining new charging data based on the charging data and the candidate control parameters; replacing the original charging data with the new charging data, and performing the step of obtaining candidate control parameters based on the charging data, until a preset number of candidate control parameters are obtained. This facilitates the subsequent selection of a superior candidate control parameter as the target control parameter for controlling the charging of the vehicle battery, avoids excessive cooling power leading to large temperature fluctuations in the vehicle battery, and reduces wear and tear on the vehicle battery.

[0007] In some possible implementations, the charging data may include at least: the terminal voltage of the vehicle battery and the state of charge (SOC) of the vehicle battery. Obtaining new charging data based on the charging data and the candidate control parameters includes: inputting the charging data and the candidate control parameters into a pre-trained electrical model to obtain the new charging data, wherein the new charging data includes: a new terminal voltage, a new SOC, and a polarization voltage. This facilitates the subsequent acquisition of multiple candidate control parameters, allowing for the selection of a superior parameter as the target control parameter for vehicle battery charging. It also avoids excessive cooling power that could lead to large temperature fluctuations in the vehicle battery, thus reducing wear and tear on the battery.

[0008] In some possible implementations, the charging data may include at least: the charging current for the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system. Obtaining new charging data based on the charging data and the candidate control parameters includes: inputting the charging data and the candidate control parameters into a pre-trained thermal model to obtain the new charging data, wherein the new charging data includes: new heat data generated by the vehicle battery and new heat data provided by the vehicle's thermal management system. Selecting a superior candidate control parameter from multiple candidate control parameters as the target control parameter for controlling vehicle battery charging avoids excessive cooling power that could lead to large temperature fluctuations in the vehicle battery, thus reducing wear and tear on the vehicle battery.

[0009] In some possible implementations, the vehicle charging method further includes: obtaining the cumulative reward value of each of the plurality of candidate control parameters; the step of determining one of the plurality of candidate control parameters as the target control parameter includes: determining the candidate control parameter corresponding to the largest cumulative reward value from the plurality of cumulative reward values ​​as the target control parameter.

[0010] In some possible implementations, obtaining the cumulative reward value of each candidate control parameter among the plurality of candidate control parameters includes: when the candidate control parameter is the first among a plurality of sequentially obtained candidate control parameters, determining the reward value of the candidate control parameter as the reward value of the candidate control parameter; when the candidate control parameter is not the first among a plurality of sequentially obtained candidate control parameters, determining the reward value of the candidate control parameter, and obtaining the cumulative reward value of the candidate control parameter based on the reward value and the cumulative reward value of the previous candidate control parameter of the candidate control parameter.

[0011] In some possible implementations, obtaining the cumulative reward value of the candidate control parameter based on the reward value and the cumulative reward value of the previous candidate control parameter includes: determining the sum of the product between the reward value and the discount factor and the cumulative reward value of the previous candidate control parameter as the cumulative reward value of the candidate control parameter.

[0012] In some possible implementations, the reward value of each candidate control parameter is obtained by: determining a sub-reward value for each candidate control parameter and obtaining a reward coefficient corresponding to the sub-reward value; and obtaining the reward value of each candidate control parameter based on the product of the sub-reward value and the reward coefficient.

[0013] In some possible implementations, the sub-reward value includes at least one of the following: charging speed sub-reward value, energy consumption sub-reward value, and vehicle battery lifespan sub-reward value.

[0014] In some possible implementations, the target control parameters include at least one of the following: the charging current of the vehicle battery, the coolant flow rate provided by the thermal management system, and the temperature of the coolant provided by the thermal management system. By controlling the charging of the vehicle battery in multiple ways, energy consumption is reduced while maintaining the temperature of the vehicle battery.

[0015] According to a second aspect of the present disclosure, a vehicle charging device is provided, the vehicle charging device comprising: an acquisition module configured to acquire charging data corresponding to the vehicle during the vehicle charging process, wherein the charging data includes heat data, the heat data including at least one of: heat data generated by the vehicle battery, heat data of the vehicle's thermal management system, and heat data of the environment in which the vehicle is located; a determination module configured to determine a plurality of candidate control parameters based on the charging data; and a charging module configured to determine one of the plurality of candidate control parameters as a target control parameter, and control the charging of the vehicle battery according to the target control parameter.

[0016] In some possible implementations, the charging data may further include at least one of the following: charging current for the vehicle battery, state of charge (SOC) of the vehicle battery, terminal voltage of the vehicle battery, and water flow rate of the water pump in the thermal management system.

[0017] In some possible implementations, the determining module includes: a first determining module configured to obtain candidate control parameters based on the charging data; a second determining module configured to obtain new charging data based on the charging data and the candidate control parameters; and a third determining module configured to replace the charging data with the new charging data and perform the step of obtaining candidate control parameters based on the charging data until a preset number of candidate control parameters are obtained.

[0018] According to a third aspect of the present disclosure, a vehicle is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the method described in the first aspect when executing the instructions.

[0019] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the vehicle charging method provided in the first aspect of the present disclosure.

[0020] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the vehicle charging method provided in the first aspect of the present disclosure.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0023] Figure 1 This is a flowchart illustrating a vehicle charging method according to an exemplary embodiment.

[0024] Figure 2 This is a flowchart illustrating a vehicle charging method according to another exemplary embodiment.

[0025] Figure 3 This is a flowchart illustrating a vehicle charging method according to another exemplary embodiment.

[0026] Figure 4 This is a flowchart illustrating a vehicle charging method according to another exemplary embodiment.

[0027] Figure 5 This is a block diagram illustrating a vehicle charging device according to an exemplary embodiment.

[0028] Figure 6 This is a block diagram illustrating a vehicle according to an exemplary embodiment. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0030] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0031] With the increasing popularity of electric vehicles, optimizing the energy efficiency of their thermal management systems has become a key technology. The thermal management system of an electric vehicle mainly addresses the cooling needs of the battery, motor, and electronic control system. The core component of an electric vehicle is the power battery pack (which can be lithium-ion batteries), and its performance, lifespan, and safety are highly dependent on operating temperature. The ideal operating temperature range for the battery pack is 15℃ to 35℃. During the charging process of a battery-powered vehicle, excessively low or high temperatures will affect the battery's lifespan and safety.

[0032] Currently, vehicles typically employ a temperature control strategy with a fixed threshold during charging. For example, if the battery temperature exceeds a certain threshold, the thermal management system switches to maximum cooling power to force cooling of the battery. This control strategy can lead to large temperature fluctuations during charging, which may damage battery health, make the battery more prone to aging, and reduce its lifespan.

[0033] To address the aforementioned problems, this disclosure provides a vehicle charging method. Please refer to [link / reference]. Figure 1 The vehicle charging method described above can be applied to Figure 5 The vehicle charging device 200 shown Figure 6 The diagram shows a vehicle 600, a charging station connected to the vehicle, a computer program product, and a computer-readable storage medium. The following example uses a vehicle as an example. The following section will address... Figure 2 The process shown will be described in detail. The vehicle charging method may include the following steps:

[0034] Step S110: During the vehicle charging process, acquire the charging data corresponding to the vehicle, wherein the charging data includes heat data, and the heat data includes at least one of the following: heat data generated by the vehicle battery, heat data provided by the vehicle's thermal management system, and heat data provided by the environment in which the vehicle is located.

[0035] The vehicle is equipped with sensors that collect charging data during the charging process. This charging data includes thermal data, specifically the heat generated by the vehicle's battery (characterizing battery heat generation), the heat generated by the vehicle's thermal management system (characterizing the system's ability to dissipate heat from the battery), and the heat generated by the environment (characterizing the environment's heat dissipation capabilities).

[0036] The heat data generated by the vehicle's battery can be the battery's temperature. The heat data provided by the vehicle's thermal management system can include the inlet and outlet temperatures of the water pump. The heat data provided by the vehicle's environment can be the ambient temperature.

[0037] Step S120: Determine multiple candidate control parameters based on the charging data.

[0038] Based on the charging data, multiple candidate control parameters are determined. Different candidate control parameters can control the vehicle battery to charge in different ways.

[0039] In one implementation, a pre-trained imitation learning model is deployed locally in the vehicle. The vehicle loads the local imitation learning model and then uses it to determine multiple candidate control parameters based on charging data.

[0040] Step S130: Determine one of the multiple candidate control parameters as the target control parameter, and control the charging of the vehicle battery according to the target control parameter.

[0041] By controlling the charging of the vehicle's on-board battery through target control parameters, the temperature of the on-board battery can be kept within the ideal temperature range of 15℃ to 35℃, resulting in small temperature fluctuations.

[0042] The vehicle charging method provided in this embodiment acquires vehicle-related charging data during the charging process. This charging data includes thermal data, which includes at least one of the following: heat generated by the vehicle battery, heat provided by the vehicle's thermal management system, and heat provided by the vehicle's environment. Based on the charging data, multiple candidate control parameters are determined, and one of these is selected as the target control parameter. The vehicle battery charging is then controlled according to the target control parameter. Compared to the method using maximum cooling power in related technologies, this embodiment selects a superior candidate control parameter as the target control parameter to control vehicle battery charging. This avoids large temperature fluctuations in the vehicle battery caused by excessive cooling power, reducing wear and tear on the vehicle battery. Furthermore, using the target control parameter eliminates unnecessary power consumption, reducing charging power consumption. In addition, compared to the method in related technologies that only uses vehicle battery temperature for judgment, this embodiment combines multiple thermal data from the vehicle for more precise charging control, further avoiding large temperature fluctuations in the vehicle battery and reducing wear and tear on the vehicle battery.

[0043] For example, the target control parameters include at least one of the following: the charging current of the vehicle battery, the coolant flow rate provided by the thermal management system, and the coolant temperature provided by the thermal management system. For example, the charging current is 90A, the coolant flow rate is 10 liters / minute, and the coolant temperature is 34°C.

[0044] The charging current used to charge the vehicle battery affects the heat generated by the battery and also determines the charging speed. The coolant flow rate provided by the thermal management system affects the battery's heat dissipation capacity. Specifically, a higher coolant flow rate results in higher energy consumption of the water pump in the thermal management system and faster heat dissipation of the battery. Conversely, a lower coolant flow rate results in lower water pump energy consumption and slower heat dissipation of the battery. The coolant temperature determines the cooling effect on the battery. For example, the lower the coolant temperature, the higher the energy consumption for producing cryogenic coolant, the greater the temperature difference between the coolant and the battery, and the stronger the cooling effect. Conversely, the higher the coolant temperature, the lower the energy consumption for producing cryogenic coolant, the smaller the temperature difference between the coolant and the battery, and the weaker the cooling effect.

[0045] Optionally, the charging data may further include at least one of the following: the charging current for charging the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system.

[0046] In one possible implementation, step S120 may include: obtaining candidate control parameters based on the charging data; obtaining new charging data based on the charging data and the candidate control parameters; replacing the charging data with the new charging data, and performing the step of obtaining candidate control parameters based on the charging data, until a preset number of candidate control parameters are obtained.

[0047] Based on the collected charging data S0, candidate control parameter at0 is determined. If the vehicle is in the charging data S0 state, the on-board battery is charged according to the candidate control parameter at0, and a new charging data S1 is predicted. The new charging data S1 replaces the charging data S0, and the candidate control parameter at1 is determined based on the charging data S1. If the vehicle is in the charging data S1 state, and the on-board battery is charged according to the candidate control parameter at1, a new charging data S2 is predicted. This process is repeated until a preset number of N candidate control parameters are obtained.

[0048] For example, the preset quantity N can be 100, 200, 240, etc.

[0049] In one approach, the charging data may include at least: the terminal voltage of the vehicle battery and the state of charge (SOC) of the vehicle battery. Obtaining new charging data based on the charging data and the candidate control parameters includes: inputting the charging data and the candidate control parameters into a pre-trained electrical model to obtain the new charging data, wherein the new charging data includes: a new terminal voltage, a new SOC, and a polarization voltage.

[0050] An electrical model can be deployed locally on the vehicle. This model primarily simulates the battery's terminal voltage and state-of-charge (SOC) characteristics under different operating conditions. Physically based neural networks are a novel modeling method that integrates electrochemical mechanisms with data-driven approaches. They maintain the physical consistency of the mechanistic model while learning complex nonlinear relationships through data. Please refer to [link / reference]. Figure 2 The charging data includes battery temperature, charging current, state of charge (SOC), and terminal voltage. This charging data is input into the electrical model, where a neural network calculates the data loss L_data, and physical law loss L_physics is obtained through physical formula constraints. Then, a loss function L = L_data + a·L_physics is calculated based on these two losses, where a is a predetermined coefficient. Combining the loss function L, new charging data is calculated, including new terminal voltage, new SOC, and polarization voltage.

[0051] For example, the physical formula constraints include: Ohm's Law: V=V oc(SOC) -I·R0-V p (1) Polarization dynamic equations: (2) Evolution equation of SOC: (3) In equation (1) above, V is the terminal voltage of the vehicle battery, and Voc (SOC) This is the open-circuit voltage, Voc. (SOC) It is obtained based on the state of charge (SOC) of the vehicle battery, where I is the charging current for the vehicle battery, R0 is the ohmic resistance of the vehicle battery, and V... p This is the polarization voltage. In equation (2) above, V is the polarization time constant. p Where is the polarization voltage, and I is the charging current for charging the vehicle battery. Let Q be the polarization resistance of the vehicle battery. In equation (3) above, SOC is the state of charge of the vehicle battery, and Q is the polarization resistance of the vehicle battery. rated This refers to the rated capacity of the vehicle's battery.

[0052] For example, the heat data generated by the vehicle battery includes the temperature of the vehicle battery. See also Figure 3 According to the law of conservation of energy, the temperature change of a vehicle battery is determined by the net heat generated per unit time. This relationship of heat can be expressed by the following formula: (4) Where m is the mass of the vehicle battery, in kg. The specific heat capacity of the vehicle battery is expressed in J / (kg). ℃), It is related to factors such as the materials and SOC of the vehicle battery. This represents the rate of change of the vehicle battery temperature over time, expressed in °C / s. This refers to the heat dissipation power of the vehicle battery itself, measured in W. This represents the heat power removed by the thermal management system, measured in W. This refers to the heat dissipation power of the vehicle battery to the surrounding environment, measured in W.

[0053] The heat generated by a vehicle battery mainly comes from irreversible heat and polarization heat during chemical reactions, and is generally calculated using the Joule-Lenz law combined with the battery's own characteristics. Therefore, the aforementioned heat generation power of the vehicle battery itself... It can be expressed by the following formula: Q1=I·(VV oc (5) Where I is the charging current of the vehicle battery, and V is the terminal voltage of the vehicle battery. oc This is the open-circuit voltage, and its value is related to factors such as the SOC and temperature of the vehicle battery. This means that V ocThe value will change with SOC and temperature.

[0054] Heat removed by the thermal management system The calculation is mainly based on the energy change of the coolant, and can be performed using the following formula: (6) Here, F represents the flow rate of the coolant, measured in m³ / s. This refers to the density of the coolant, expressed in kg / m³. This refers to the specific heat capacity of the coolant, expressed in J / (kg). ℃). This refers to the outlet temperature of the coolant. The inlet temperature of the coolant is shown in °C.

[0055] According to Newton's law of cooling, the amount of heat dissipated by the battery to the environment... The amount of heat dissipated from the battery to the environment can be calculated using the following formula, which is related to the temperature difference between the battery and the environment. : (7) in, The heat dissipation coefficient is expressed in W / (m²). ℃), heat dissipation coefficient It is related to the heat dissipation method (such as natural convection, forced air cooling, etc.) and the state of the battery surface area. This represents the heat dissipation area of ​​the battery, measured in m². This refers to the temperature of the vehicle's battery. The ambient temperature.

[0056] Substituting equations (5) to (7) into equation (4), the temperature of the vehicle battery can be calculated. Temperature of vehicle battery This refers to new heat data generated by the vehicle's battery.

[0057] In one approach, the charging data may include at least: the charging current for charging the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system. Obtaining new charging data based on the charging data and the candidate control parameters includes: inputting the charging data and the candidate control parameters into a pre-trained thermal model to obtain the new charging data, wherein the new charging data includes: new heat data generated by the vehicle battery and new heat data provided by the vehicle's thermal management system.

[0058] For example, a thermal model can be deployed locally on the vehicle. The battery's thermal model is primarily used to predict the heat data of the onboard battery based on charging data. See also... Figure 4 The charging data includes SOC, charging current, terminal voltage, vehicle battery temperature, inlet and outlet temperatures of the thermal management system, and ambient temperature. The charging data is input into a thermal model, and the data loss L_data is calculated using the model's neural network. The physical law loss L_physics is obtained through physical formula constraints. Then, the loss function L = L_data + a·L_physics is calculated based on the aforementioned two losses, where a is a predetermined coefficient. Combining the loss function L, new charging data is calculated, including new vehicle battery temperature and thermal management system heat data.

[0059] Optionally, the vehicle charging method further includes: obtaining the cumulative reward value G of each of the plurality of candidate control parameters.

[0060] In one implementation, the cumulative reward value can be determined as follows: if the candidate control parameter is the first of a plurality of sequentially acquired candidate control parameters, the reward value R0 of the candidate control parameter is determined as the reward value G0 of the candidate control parameter.

[0061] If the candidate control parameter is not the first among multiple sequentially acquired candidate control parameters, determine the reward value R of the candidate control parameter. n And according to the reward value R n The cumulative reward value G of the previous candidate control parameter and the candidate control parameter. n-1 The cumulative reward value G of the candidate control parameter is obtained. n =R n +G n-1 .

[0062] For example, obtaining the cumulative reward value of the candidate control parameter based on the reward value and the cumulative reward value of the previous candidate control parameter includes: Determine the reward value R n and discount factor The product of the two, and the cumulative reward value G of the previous candidate control parameter. n-1 The sum of these values ​​serves as the cumulative reward value G for the candidate control parameter. n =G n-1 + n-1 ·R n If G n-1 Expanding on this, it's understandable that G... n =R0+ ·R1+ 2 R2+… n-1 ·Rn .

[0063] Discount factor The decision was made by G. n Size, discount factor The value ranges from 0 to 1. If the vehicle is more concerned about its charging status in a short period of time, then the discount factor... You can choose a smaller value, such as the discount factor. It can be 0.6. If you are more concerned about charging over a longer period of time, then the discount factor can be increased. You can choose a larger value, such as a discount factor. It can be 0.9.

[0064] The reward value for each candidate control parameter is obtained as follows: a sub-reward value for each candidate control parameter is determined, and the reward coefficient corresponding to the sub-reward value is obtained. The reward value for each candidate control parameter is obtained by multiplying the sub-reward value and the reward coefficient.

[0065] For example, the sub-reward value includes at least one of the following: charging speed sub-reward value. Energy consumption reward sub-value Vehicle battery lifespan bonus value .

[0066] Among them, the charging speed sub-reward value This is used to encourage rapid increases in the State of Charge (SOC) of vehicle batteries. Charging speed sub-reward value. It can be calculated using the following formula: (8) in, For the nth SOC, for A previous SOC.

[0067] Energy consumption reward sub-value To ensure the battery is fully charged while minimizing the energy consumption of the thermal management system, Q2= .

[0068] Vehicle battery lifespan bonus value Battery lifespan is related to vehicle battery temperature; temperatures above the optimal range and below the minimum range both reduce battery life. (Vehicle battery lifespan sub-reward value) Calculated using the following formula: (9) in, Let the temperature of the nth vehicle battery be... The optimal temperature for vehicle batteries, for example The temperature is 30℃.

[0069] For example, the reward value Rt for any candidate control parameter is calculated as follows: R n (10) in, and All are coefficients.

[0070] Based on this, step S130 may include: determining the candidate control parameter corresponding to the largest cumulative reward value from multiple cumulative reward values ​​as the target control parameter.

[0071] By iterating through the aforementioned process, a preset number of N candidate control parameters are obtained. The candidate control parameter corresponding to the largest one among the N candidate control parameters is determined as the target control parameter. By controlling the charging of the vehicle battery through the target control parameter, the policy network can be continuously optimized towards the goal of maximizing the long-term expected return.

[0072] This disclosure also provides a vehicle charging method, wherein the charging data includes heat data, which includes at least one of the following: heat data generated by the vehicle battery, heat data provided by the vehicle's thermal management system, and heat data provided by the environment in which the vehicle is located. The charging data further includes: the terminal voltage of the vehicle battery, the state of charge (SOC) of the vehicle battery, the charging current for charging the vehicle battery, the SOC of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system. The charging data and the candidate control parameters are input into a pre-trained electrical model to obtain new charging data, wherein the new charging data includes: a new terminal voltage, a new SOC, and a polarization voltage. Furthermore, the charging data and the candidate control parameters are input into a pre-trained thermal model to obtain new charging data, wherein the new charging data includes: new heat data generated by the vehicle battery and new heat data provided by the vehicle's thermal management system. The imitation learning model includes: a policy network (Actor), a value network (Critic), and a target policy network (Actor). T and Target Value Network Critic T The policy network (Actor) selects candidate control parameters based on new charging data to maximize value. This is achieved using the target policy network (Actor). T and Target Value Network Critic T The value network (Critic) is updated based on minimizing the value difference (TD-error), and the policy network (Actor) is updated based on the advantage function. The policy network (Actor) is periodically copied to update the target policy network (Actor). TCopy the value network Critic network and update the target value network Critic T .

[0073] Because the battery charging current and cooling power control in charging scenarios require real-time response, the model is deployed in the vehicle's NPU (Neural Processing Unit) chip for real-time prediction. Specifically, one prediction chain involves the CPU receiving input signals from the Carservice, processing the signals, sending them to the NPU for inference, the NPU returning the inference structure to the CPU, and finally, control is performed in the control domain.

[0074] The vehicle charging method provided in this application, based on big data of vehicle charging, utilizes physical formulas combined with neural networks to construct electrical and thermal models, saving significant calibration costs. Then, using these models as a simulator, the PPO algorithm interacts with the simulator to obtain training samples for optimization, resulting in the optimal control strategy. Finally, the optimized PPO algorithm is deployed on the vehicle to control the charging process in real time, directly controlling charging current, thermal management system coolant flow, inlet water temperature, etc., achieving multi-objective control with short charging time, low energy consumption, and long battery life. Experiments conducted by the inventors show that, during the charging process from 10% to 100% SOC of the vehicle battery, at an ambient temperature of 25°C and a maximum charging pile current capacity of 160A, using the method of this application can save up to 30% in energy consumption and shorten charging time.

[0075] Figure 5 This is a block diagram illustrating a vehicle charging device according to an exemplary embodiment. Please refer to... Figure 5 The vehicle charging device 200 includes: The acquisition module 210 is configured to acquire charging data corresponding to the vehicle during the vehicle charging process, wherein the charging data includes heat data, and the heat data includes at least one of the following: heat data generated by the vehicle battery, heat data of the vehicle's thermal management system, and heat data of the environment in which the vehicle is located. The determining module 220 is configured to determine multiple candidate control parameters based on the charging data; The charging module 230 is configured to determine one of the plurality of candidate control parameters as a target control parameter, and control the charging of the vehicle battery according to the target control parameter.

[0076] In one possible implementation, the charging data further includes at least one of the following: the charging current for charging the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system.

[0077] In one possible implementation, the determining module 220 includes: The first determining module is configured to obtain candidate control parameters based on the charging data; The second determining module is configured to obtain new charging data based on the charging data and the candidate control parameters; The third determining module is configured to replace the charging data with the new charging data and execute the step of obtaining candidate control parameters based on the charging data until a preset number of candidate control parameters are obtained.

[0078] In one possible implementation, the charging data further includes at least: the terminal voltage of the vehicle battery and the state of charge (SOC) of the vehicle battery. The second determining module is specifically configured to input the charging data and the candidate control parameters into a pre-trained electrical model to obtain the new charging data, wherein the new charging data includes: a new terminal voltage, a new SOC, and a polarization voltage.

[0079] In one possible implementation, the charging data further includes at least: the charging current for charging the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump of the thermal management system. The second determining module is specifically configured to input the charging data and the candidate control parameters into a pre-trained thermal model to obtain the new charging data, wherein the new charging data includes: new heat data generated by the vehicle battery and new heat data provided by the vehicle's thermal management system.

[0080] In one possible implementation, the vehicle charging device 200 further includes: The reward acquisition module is configured to acquire the cumulative reward value of each of the plurality of candidate control parameters; The charging module 230 is specifically configured to determine the candidate control parameter corresponding to the largest cumulative reward value from multiple cumulative reward values ​​as the target control parameter.

[0081] In one possible implementation, the reward acquisition module includes: The first reward acquisition module is configured to determine the reward value of the candidate control parameter as the reward value of the candidate control parameter when the candidate control parameter is the first among a plurality of sequentially acquired candidate control parameters. The second reward acquisition module is configured to determine the reward value of the candidate control parameter when the candidate control parameter is not the first among a plurality of sequentially acquired candidate control parameters, and to obtain the cumulative reward value of the candidate control parameter based on the reward value and the cumulative reward value of the previous candidate control parameter.

[0082] In one possible implementation, the second reward acquisition module is specifically configured to determine the sum of the product between the reward value and the discount factor and the cumulative reward value of the previous candidate control parameter of the candidate control parameter, as the cumulative reward value of the candidate control parameter.

[0083] In one possible implementation, the first reward value acquisition module is specifically configured to determine the sub-reward value of each candidate control parameter and acquire the reward coefficient corresponding to the sub-reward value; The reward value for each candidate control parameter is obtained by multiplying the sub-reward value and the reward coefficient.

[0084] In one possible implementation, the sub-reward value includes at least one of the following: charging speed sub-reward value, energy consumption sub-reward value, and vehicle battery lifespan sub-reward value.

[0085] In one possible implementation, the target control parameters include at least one of the following: the charging current of the vehicle battery, the coolant flow rate provided by the thermal management system, and the temperature of the coolant provided by the thermal management system.

[0086] Regarding the vehicle charging device 200 in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0087] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the vehicle charging method provided in this disclosure.

[0088] Figure 6 This is a block diagram illustrating a vehicle 600 according to an exemplary embodiment. For example, vehicle 600 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicle. Vehicle 600 may have driver assistance functions.

[0089] Reference Figure 6 The vehicle 600 may include various subsystems, such as an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. The vehicle 600 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of the vehicle 600 can be interconnected via wired or wireless means.

[0090] In some embodiments, the infotainment system 610 may include a communication system, an entertainment system, and a navigation system, etc.

[0091] The perception system 620 may include several sensors for sensing information about the environment surrounding the vehicle 600. For example, the perception system 620 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0092] The decision control system 630 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0093] The drive system 640 may include components that provide powered motion to the vehicle 600. In one embodiment, the drive system 640 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0094] Some or all of the functions of vehicle 600 are controlled by computing platform 650. Computing platform 650 may include at least one processor 651 and memory 652, and processor 651 may execute instructions 653 stored in memory 652.

[0095] Processor 651 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0096] The memory 652 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0097] In addition to instruction 653, memory 652 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 652 can be used by computing platform 650.

[0098] In this embodiment of the disclosure, processor 651 may execute instruction 653 to complete all or part of the steps of the above-described vehicle charging method.

[0099] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the described functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this application.

[0100] Furthermore, the term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept in a concrete manner. As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from the context, “X applies A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this application and the appended claims are generally understood to mean “one or more.”

[0101] Similarly, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of this disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations, as may be desired and advantageous to any given or particular application. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0102] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0103] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A vehicle charging method, characterized in that, The vehicle charging method includes: During the vehicle charging process, charging data corresponding to the vehicle is acquired, wherein the charging data includes heat data, and the heat data includes at least one of the following: heat data generated by the vehicle battery, heat data provided by the vehicle's thermal management system, and heat data provided by the environment in which the vehicle is located. Based on the charging data, multiple candidate control parameters are determined; One of the multiple candidate control parameters is selected as the target control parameter, and the vehicle battery is charged according to the target control parameter.

2. The vehicle charging method according to claim 1, characterized in that, The charging data also includes at least one of the following: the charging current for charging the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system.

3. The vehicle charging method according to claim 1 or 2, characterized in that, The step of determining multiple candidate control parameters based on the charging data includes: Based on the charging data, candidate control parameters are obtained; New charging data is obtained based on the charging data and the candidate control parameters; Replace the charging data with the new charging data, and execute the step of obtaining candidate control parameters based on the charging data until a preset number of candidate control parameters are obtained.

4. The vehicle charging method according to claim 3, characterized in that, The charging data further includes at least: the terminal voltage of the vehicle battery and the state of charge (SOC) of the vehicle battery. Obtaining new charging data based on the charging data and the candidate control parameters includes: The charging data and the candidate control parameters are input into a pre-trained electrical model to obtain the new charging data, wherein the new charging data includes: a new terminal voltage, a new state of charge (SOC), and a polarization voltage.

5. The vehicle charging method according to claim 3, characterized in that, The charging data includes at least: the charging current for the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system. Obtaining new charging data based on the charging data and the candidate control parameters includes: The charging data and the candidate control parameters are input into a pre-trained thermal model to obtain the new charging data, wherein the new charging data includes: new heat data generated by the vehicle battery and new heat data provided by the vehicle's thermal management system.

6. The vehicle charging method according to claim 1, characterized in that, The vehicle charging method also includes: Obtain the cumulative reward value for each of the multiple candidate control parameters; The step of determining one of the plurality of candidate control parameters as the target control parameter includes: The candidate control parameter corresponding to the largest cumulative reward value among multiple cumulative reward values ​​is determined as the target control parameter.

7. The vehicle charging method according to claim 6, characterized in that, The step of obtaining the cumulative reward value of each candidate control parameter among the plurality of candidate control parameters includes: If the candidate control parameter is the first among a plurality of sequentially acquired candidate control parameters, the reward value of the candidate control parameter is determined as the reward value of the candidate control parameter. If the candidate control parameter is not the first among multiple sequentially acquired candidate control parameters, the reward value of the candidate control parameter is determined, and the cumulative reward value of the candidate control parameter is obtained based on the reward value and the cumulative reward value of the previous candidate control parameter.

8. The vehicle charging method according to claim 7, characterized in that, The step of obtaining the cumulative reward value of the candidate control parameter based on the reward value and the cumulative reward value of the previous candidate control parameter includes: The product of the reward value and the discount factor is summed with the cumulative reward value of the previous candidate control parameter, and this sum is taken as the cumulative reward value of the candidate control parameter.

9. The vehicle charging method according to claim 7, characterized in that, The reward value for each candidate control parameter is obtained in the following way: Determine the sub-reward value for each candidate control parameter, and obtain the reward coefficient corresponding to the sub-reward value; The reward value for each candidate control parameter is obtained by multiplying the sub-reward value and the reward coefficient.

10. The vehicle charging method according to claim 9, characterized in that, The sub-reward value includes at least one of the following: charging speed sub-reward value, energy consumption sub-reward value, and vehicle battery lifespan sub-reward value.

11. The vehicle charging method according to claim 1 or 2, characterized in that, The target control parameters include at least one of the following: the charging current of the vehicle battery, the coolant flow rate provided by the thermal management system, and the temperature of the coolant provided by the thermal management system.

12. A vehicle charging device, characterized in that, The vehicle charging device includes: The acquisition module is configured to acquire charging data corresponding to the vehicle during the vehicle charging process, wherein the charging data includes heat data, and the heat data includes at least one of the following: heat data generated by the vehicle battery, heat data of the vehicle's thermal management system, and heat data of the environment in which the vehicle is located. The determination module is configured to determine multiple candidate control parameters based on the charging data; The charging module is configured to determine one of the plurality of candidate control parameters as a target control parameter, and control the charging of the vehicle battery according to the target control parameter.

13. The vehicle charging device according to claim 12, characterized in that, The charging data also includes at least one of the following: the charging current for charging the vehicle battery, the state of charge (SOC) of the vehicle battery, the terminal voltage of the vehicle battery, and the water flow rate of the water pump in the thermal management system.

14. The vehicle charging device according to claim 12 or 13, characterized in that, The determining module includes: The first determining module is configured to obtain candidate control parameters based on the charging data; The second determining module is configured to obtain new charging data based on the charging data and the candidate control parameters; The third determining module is configured to replace the charging data with the new charging data and execute the step of obtaining candidate control parameters based on the charging data until a preset number of candidate control parameters are obtained.

15. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1 to 11 when executing the instruction.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 11.

17. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 11.

Images