Battery health evaluation index-based vehicle-pile cooperative adaptive charging control method, device and equipment and storage medium

Abstract

This application discloses a vehicle-charging pile cooperative adaptive charging control method, device, equipment, and storage medium based on battery health evaluation indicators, relating to the field of new energy vehicle charging control technology. The method includes: obtaining a battery temperature prediction sequence, a state of charge prediction sequence, and a health status prediction sequence for a preset future time period based on battery operating status data and a battery status prediction model; obtaining an aging health index based on the battery operating status data and the battery status prediction sequence; when the aging health index is within a preset threshold range, obtaining a charging current sequence for a preset future time period based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence; and adjusting the output power of the charging pile according to the charging current sequence. This achieves the technical effect of adjusting the charging current in advance before the risk of battery aging increases, effectively suppressing the battery aging rate while ensuring charging efficiency.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle charging control technology, and in particular to a vehicle-charging station cooperative adaptive charging control method, device, equipment and storage medium based on battery health evaluation indicators. Background Technology

[0002] With the rapid development of the new energy vehicle industry, DC fast charging technology has been widely applied, and users' requirements for charging efficiency are constantly increasing. At the same time, the cycle life of the power battery and charging safety have become key factors affecting the market acceptance of electric vehicles. This requires that, in the charging control process, not only should charging time be shortened to improve charging efficiency, but also the battery temperature rise and accelerated material aging caused by high-rate charging should be effectively suppressed, thereby achieving a dynamic balance between charging efficiency and battery life.

[0003] Existing charging control strategies typically rely on passive protection based on voltage, current, or temperature thresholds. This means reducing the charging current when the battery temperature exceeds a set threshold and stopping charging when the battery voltage reaches its upper limit. These methods can only provide remedial measures when the battery approaches a dangerous state and cannot identify potential aging risks during charging in advance. Furthermore, some studies attempt to optimize charging strategies by estimating battery health status, but most employ rule-based control methods, lacking a comprehensive assessment mechanism for battery temperature, state of charge, health status, and health degradation trends. This makes it difficult to predictively adjust the charging process based on the battery's real-time operating status.

[0004] Due to the lack of predictive capabilities for future trends in battery temperature, state of charge, and health, and the absence of comprehensive evaluation indicators for battery aging risks, the charging control system is unable to adjust the charging current in advance before the risk of battery aging increases. As a result, it is difficult to effectively suppress the rate of battery aging while ensuring charging efficiency.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this application is to provide a vehicle-pile cooperative adaptive charging control method, device, equipment, and storage medium based on battery health evaluation indicators, aiming to solve the technical problem of how to effectively suppress battery aging rate while ensuring charging efficiency.

[0007] To achieve the above objectives, this application proposes a vehicle-charging station cooperative adaptive charging control method based on battery health evaluation indicators. The method includes: Based on battery operating status data and battery status prediction model, a battery status prediction sequence for a future preset time period is obtained, wherein the battery status prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health status prediction sequence. Based on the battery operating status data and the battery status prediction sequence, an aging health index is obtained; When the aging health index is within a preset threshold range, the charging current sequence for a future preset period is obtained based on the preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence. The output power of the charging pile is adjusted according to the charging current sequence.

[0008] In one embodiment, the battery state prediction model includes a battery temperature prediction model, a battery health degradation model, and a state of charge prediction model; The step of obtaining a battery state prediction sequence for a future preset time period based on battery operating status data and a battery state prediction model includes: Obtain battery operating status data; Based on the battery operating status data and the battery temperature prediction model, the battery temperature is predicted within a preset period in the future to obtain a temperature prediction sequence. Based on the temperature prediction sequence, the battery operating status data, and the battery health degradation model, the battery health status is predicted within a preset period in the future, and a health status prediction sequence is obtained. Based on the battery operating status data and the state of charge prediction model, the state of charge is predicted within a preset time period to obtain a state of charge prediction sequence. The temperature prediction sequence, the health status prediction sequence, and the state of charge prediction sequence are used as the battery state prediction sequence.

[0009] In one embodiment, the step of predicting the battery temperature within a preset time period based on the battery operating status data and the battery temperature prediction model to obtain a temperature prediction sequence includes: Based on the battery operating status data, the current charging current and the battery temperature at the current time step are obtained; The current charging current and the battery temperature at the current time step are used as inputs to the battery temperature prediction model to obtain the predicted battery temperature at the next time step. The battery temperature prediction model is obtained by discretizing the battery temperature change thermal balance model. The predicted battery temperature is used as the current time step battery temperature, and the process returns to the step of using the current charging current and the current time step battery temperature as inputs to the battery temperature prediction model to obtain the predicted battery temperature for the next time step, until the cumulative number of time steps reaches the number of time steps corresponding to a future preset period. All predicted battery temperatures are then arranged in chronological order to obtain a temperature prediction sequence.

[0010] In one embodiment, the step of predicting the battery health status within a preset time period based on the temperature prediction sequence, the battery operating status data, and the battery health degradation model to obtain a health status prediction sequence includes: Based on the battery operating status data, the current charging current, the battery temperature at the current time step, and the battery health status at the current time step are obtained. The current charging current, the battery temperature at the current time step, and the battery health status at the current time step are used as inputs to the battery health degradation model to obtain the predicted battery health status at the next time step. The battery health degradation model is obtained by discretizing the battery health status degradation rate equation. The predicted battery health state is used as the current time step battery health state, and the process returns to the step of using the current charging current, the current time step battery temperature, and the current time step battery health state as inputs to the battery health degradation model to obtain the predicted battery health state for the next time step, until the cumulative number of time steps reaches the number of time steps corresponding to a future preset period. All predicted battery health states are then arranged in chronological order to obtain the health state prediction sequence.

[0011] In one embodiment, the step of predicting the state of charge (SOC) within a preset time period based on the battery operating state data and the SOC prediction model to obtain a SOC prediction sequence includes: Based on the battery operating status data, the battery rated capacity, current charging current, and current state of charge at the current time step are obtained; The rated capacity of the battery, the current charging current, and the state of charge at the current time step are used as inputs to the state of charge prediction model to obtain the predicted state of charge at the next time step. The state of charge prediction model is obtained by discretizing the ampere-hour integral equation. The predicted state of charge is used as the current state of charge at the current time step, and the process of taking the battery rated capacity, the current charging current, and the current state of charge at the current time step as inputs to the state of charge prediction model to obtain the predicted state of charge at the next time step is repeated until the number of accumulated time steps reaches the number of time steps corresponding to a future preset period. All predicted states of charge are then arranged in chronological order to obtain the state of charge prediction sequence.

[0012] In one embodiment, obtaining the aging health index based on the battery operating status data and the battery status prediction sequence includes: Based on the battery operating status data, the current charging current, battery rated capacity, rated temperature, rated state of charge, rated charging rate, and reference health status are obtained. Based on the battery state prediction sequence, the rated temperature, and the rated state of charge, a comprehensive battery health index is obtained; Based on the health status prediction sequence, the amount of health degradation per unit time is obtained; The aging health index is obtained based on the battery comprehensive health index, the amount of health degradation per unit time, the reference health state, the rated charging rate, the battery rated capacity, and the current charging current.

[0013] In one embodiment, when the aging health index is within a preset threshold range, obtaining the charging current sequence for a preset future time period based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence includes: The constraints are obtained based on the preset temperature threshold, preset battery charge threshold, and preset charging current threshold. Minimizing the overall aging cost is used as the optimization objective of the preset overall aging cost function; The aging health index, the temperature prediction sequence, and the health status prediction sequence are used as inputs to a preset comprehensive aging cost function. The preset comprehensive aging cost function is solved according to the constraints and the optimization objective to obtain the charging current sequence within a preset future time period.

[0014] In addition, to achieve the above objectives, this application also proposes a vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators. The vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators includes: a prediction module, used to obtain a battery state prediction sequence within a future preset time period based on battery operating state data and a battery state prediction model, wherein the battery state prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health state prediction sequence. An aging module is used to obtain an aging health index based on the battery operating status data and the battery status prediction sequence. The current module is used to obtain the charging current sequence for a future preset time period based on the preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence when the aging health index is within a preset threshold range. An adjustment module is used to adjust the output power of the charging pile according to the charging current sequence.

[0015] Furthermore, to achieve the above objectives, this application also proposes a vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators as described above.

[0016] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators as described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators as described above.

[0018] This application, based on battery operating status data and a battery status prediction model, obtains a battery status prediction sequence for a future preset time period. This battery status prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health status prediction sequence. Based on the battery operating status data and the battery status prediction sequence, an aging health index is obtained. When the aging health index is within a preset threshold range, a charging current sequence for the future preset time period is obtained based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence. The output power of the charging pile is adjusted according to the charging current sequence. This solves the technical problem that the charging control system cannot adjust the charging current in advance before the battery aging risk increases due to a lack of predictive ability for future battery status changes and a lack of comprehensive evaluation indicators for battery aging risk. It achieves the technical effect of adjusting the charging current in advance before the battery aging risk increases, effectively suppressing the battery aging rate while ensuring charging efficiency. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in this application. Figure 2 This is a structural diagram of the battery comprehensive health index model provided in Embodiment 1 of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation index in this application. Figure 3 This is a system structure diagram provided in Embodiment 1 of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in this application; Figure 4 This is a flowchart illustrating Embodiment 2 of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in this application. Figure 5 A simplified flowchart of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators provided in Embodiment 2 of this application; Figure 6 This is a schematic diagram of the module structure of the vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators according to an embodiment of this application; Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in the embodiments of this application.

[0022] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of this application embodiment is as follows: Based on battery operating status data and a battery status prediction model, a battery status prediction sequence for a future preset time period is obtained, wherein the battery status prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health status prediction sequence; based on the battery operating status data and the battery status prediction sequence, an aging health index is obtained; when the aging health index is within a preset threshold range, a charging current sequence for a future preset time period is obtained based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence; and the output power of the charging pile is adjusted according to the charging current sequence.

[0026] In this embodiment, for ease of description, the following description will focus on the vehicle-pile cooperative adaptive charging control system based on battery health evaluation indicators.

[0027] Existing charging control strategies typically rely on passive protection based on voltage, current, or temperature thresholds. This means reducing the charging current when the battery temperature exceeds a set threshold and stopping charging when the battery voltage reaches its upper limit. These methods can only provide remedial measures when the battery approaches a dangerous state and cannot proactively identify potential aging risks during charging. Furthermore, while some studies attempt to optimize charging strategies by estimating battery health status, most employ rule-based control methods, lacking a comprehensive assessment mechanism for battery temperature, state of charge, health status, and health degradation trends. This makes it difficult to predictively adjust the charging process based on the battery's real-time operating status.

[0028] This application provides a solution that addresses the technical problem that the charging control system cannot adjust the charging current in advance before the risk of battery aging increases due to the lack of predictive ability for battery state changes in the future and the lack of comprehensive evaluation indicators for battery aging risk. It achieves the technical effect of adjusting the charging current in advance before the risk of battery aging increases, and effectively suppressing the battery aging rate while ensuring charging efficiency.

[0029] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a vehicle-pile cooperative adaptive charging control system based on battery health evaluation indicators. The following description uses a vehicle-pile cooperative adaptive charging control system based on battery health evaluation indicators as an example to illustrate this embodiment and the subsequent embodiments.

[0030] Based on this, embodiments of this application provide a vehicle-charging station cooperative adaptive charging control method based on battery health evaluation indicators, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in this application.

[0031] In this embodiment, the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators includes steps S10~S40: Step S10: Based on the battery operating status data and the battery status prediction model, obtain the battery status prediction sequence for the future preset time period. It should be noted that battery operating status data refers to the real-time operating parameters of the power battery collected by the vehicle-side battery management system, which can include various parameters such as battery voltage, battery current, battery temperature, battery state of charge (SOC), and battery health status. The battery state prediction model is a mathematical model used to describe the evolution of battery temperature, SOC, and health status over time, including battery temperature prediction models, battery health degradation models, and SOC prediction models. The future preset time period refers to a time window projected backward from the current moment; the length of this time window can be the preset number of prediction steps multiplied by the sampling time interval. The battery state prediction sequence refers to the sequence of predicted values ​​of battery temperature, SOC, and health status at each discrete time step within the future preset time period, arranged in chronological order.

[0032] Specifically, the battery temperature, state of charge (SOC), health status, and charging current collected at the current moment are used as the initial inputs to the battery state prediction model. The battery temperature prediction model calculates the battery temperature at the next moment based on the current battery temperature and charging current, combined with the battery's thermal capacity, equivalent internal resistance, heat dissipation coefficient, and ambient temperature. The battery health degradation model calculates the battery health status at the next moment based on the current battery health status, charging current, and battery temperature. The SOC prediction model calculates the SOC at the next moment based on the current SOC, charging current, and battery rated capacity. The calculated state values ​​at the next moment are used as the new current states, and the above calculation process is repeated until the number of prediction steps corresponding to the preset future time period is reached. All prediction results are arranged in chronological order to obtain the battery state prediction sequence.

[0033] Understandably, existing technologies rely solely on current threshold values ​​for charging control, failing to anticipate future temperature changes and health degradation trends during charging. This results in the control system being unable to respond effectively when battery temperature or aging risks rise sharply. Therefore, step S10 allows for advance acquisition of battery state change trends over a preset time period, providing a data foundation for predictive charging control and improving its predictability and safety.

[0034] Step S20: Obtain the aging health index based on the battery operating status data and the battery status prediction sequence; It should be noted that the Aging Health Index is a dimensionless comprehensive evaluation index used to quantitatively assess the degree of aging risk of a battery under current charging conditions. The higher the index value, the higher the risk of battery aging.

[0035] It is understandable that since a single state parameter (such as battery temperature or charging rate alone) cannot fully reflect the overall aging risk of the battery, and battery aging is a cumulative process coupled with multiple factors, step S20 can integrate information from multiple dimensions such as battery temperature, state of charge, health status, and health degradation rate into a comprehensive evaluation index, thereby achieving a quantitative assessment of battery aging risk and providing a basis for subsequent charging strategy decisions.

[0036] In one feasible implementation, step S20 may include: obtaining the current charging current, battery rated capacity, rated temperature, rated state of charge, rated charging rate, and reference health state based on the battery operating state data; obtaining a comprehensive battery health index based on the battery state prediction sequence, the rated temperature, and the rated state of charge; obtaining the amount of health degradation per unit time based on the health state prediction sequence; and obtaining an aging health index based on the comprehensive battery health index, the amount of health degradation per unit time, the reference health state, the rated charging rate, the battery rated capacity, and the current charging current.

[0037] It should be noted that the comprehensive battery health index is a weighted composite index of battery temperature, state of charge, and state of health, used to reflect the overall health of the battery's current operating state. The health degradation per unit time refers to the absolute value of the difference between the predicted values ​​of the battery's state of health at adjacent time points, used to characterize the rate of decline in the battery's state of health under current charging conditions. The charging rate refers to the ratio of the current charging current to the battery's rated capacity, used to characterize the relative magnitude of the charging current.

[0038] The current charging current refers to the charging current output by the charging pile to the power battery within this time step. Battery rated capacity refers to the amount of charge the battery can store or release under standard conditions, as specified by the battery manufacturer. Rated temperature refers to the standard operating temperature of the battery under its design conditions. Rated state of charge refers to the state of charge of the battery when fully charged. Rated charging rate refers to the maximum allowable charging rate of the battery under its design conditions. Reference health status is a benchmark value for the battery's health status, used to normalize the amount of health degradation per unit time.

[0039] Specifically, such as Figure 2As shown, during battery operation, by collecting operating data under the current state and performing state prediction and data estimation, the battery temperature, state of charge, and health state prediction sequences required for calculating the comprehensive battery health index are obtained. Simultaneously, the amount of health degradation per unit time is calculated based on the changes in the predicted health state values. Subsequently, by combining benchmark parameters such as battery rated capacity, rated temperature, rated state of charge, rated charging rate, and reference health state, the above comprehensive health index and degradation rate are weighted and normalized to finally generate an aging health index reflecting the current level of battery aging risk. The larger the index value, the higher the aging risk of the battery under the current charging conditions. This aging health index serves as the input basis for subsequent charging control strategies, used to dynamically assess battery health risk and adjust charging current or charging rate to achieve optimized control results that balance charging efficiency and extended battery life.

[0040] To comprehensively assess battery operating status, a Battery Health Index (BHI) is constructed:

[0041] Where: T is the battery temperature; The maximum permissible temperature is the rated temperature; SOC is the battery state of charge. The maximum SOC (State of Charge) is the rated state of charge; the state of health (SOH) is the battery's state of health. These are the weighting coefficients.

[0042] Based on this, a battery aging health index (AHI) is further constructed:

[0043] in, The amount of health deterioration per unit time; For reference regarding health status; Maximum charging rate, The charging rate is determined by the ratio of the current charging current I to the battery's rated capacity. The calculation yields the following result:

[0044] In this embodiment, by integrating battery temperature, state of charge, health status, health degradation rate, and charging rate into an aging health index, the problem that a single parameter cannot comprehensively assess the risk of battery aging is solved, and the accuracy and comprehensiveness of battery aging risk assessment are improved.

[0045] The above are merely feasible implementations of step S20 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S20.

[0046] Step S30: When the aging health index is within a preset threshold range, the charging current sequence for a future preset time period is obtained based on the preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence. It should be noted that the preset threshold range refers to the battery operating state areas divided according to the magnitude of the aging health index, including low-risk, medium-risk, and high-risk areas, each corresponding to different control strategies. The preset comprehensive aging cost function is a cost function used to evaluate the comprehensive impact of different charging current sequences on the degree of battery aging. The smaller the value of this function, the better the comprehensive performance of the charging current sequence in balancing charging efficiency and battery life. The charging current sequence refers to the sequence of predicted charging current values ​​that the charging pile should output at each discrete time step within a preset future period, arranged in chronological order.

[0047] Based on the Aging Health Index (AHI), the battery operating status is divided into three regions:

[0048] Among them, A1 and A2 are preset thresholds.

[0049] Understandably, since complex optimization control is not required when the risk of battery aging is low, and safety should be prioritized over charging efficiency when the risk of battery aging is too high, performing step S30 and starting optimization control only when the aging health index is in the medium-risk zone can avoid unnecessary computational overhead. At the same time, it can prioritize battery safety when the risk is high, thereby achieving a reasonable balance between computational efficiency, charging efficiency and battery life.

[0050] In one feasible implementation, step S30 may include: obtaining constraints based on a preset temperature threshold, a preset battery charge threshold, and a preset charging current threshold; using minimizing the overall aging cost as the optimization objective of a preset overall aging cost function; using the aging health index, the temperature prediction sequence, and the health status prediction sequence as inputs to the preset overall aging cost function, and solving the preset overall aging cost function based on the constraints and the optimization objective to obtain the charging current sequence within a preset future time period.

[0051] It should be noted that the preset temperature threshold refers to the maximum temperature value that the battery is allowed to reach during charging, used to constrain the optimization process to ensure battery thermal safety. The preset state-of-charge (SOC) threshold refers to the maximum SOC value that the battery is allowed to reach during charging, used to prevent overcharging. The preset charging current threshold refers to the maximum current value that the charging station is allowed to output during charging, limited by the charging station's hardware capabilities and battery safety requirements. Constraints refer to the restrictions that must be met during the optimization process, including that the predicted temperature at each time step does not exceed the preset temperature threshold, the predicted SOC does not exceed the preset SOC threshold, and the charging current is between zero and the preset charging current threshold. The optimization objective is the goal that the optimization process aims to achieve, namely, minimizing the value of the comprehensive aging cost function.

[0052] Specifically, when the battery is in a medium-risk operating state, the charging current needs to be optimized. The optimization objective is to minimize the overall aging cost. The input to the preset overall aging cost function is the charging current sequence, the temperature prediction sequence, and the health status prediction sequence, and its expression is as follows:

[0053]

[0054] in, The overall aging cost at the k-th time step, The aging health index at the k-th time step. Let the battery temperature be at the k-th time step. To determine the amount of health degradation per unit time at the k-th time step, is the weighting coefficient, and N is the preset future time period.

[0055] When solving optimization problems, the following constraints must be satisfied: Temperature constraints:

[0056] SOC constraints:

[0057] Current constraint:

[0058] in, Let be the battery temperature at the k-th time step; The preset temperature threshold can be the rated temperature; This represents the battery state of charge at the k-th time step. To preset the battery charge threshold, the rated state of charge can be used; This represents the charging current at the k-th time step. This is the maximum charging current.

[0059] Calculate the overall aging cost for each time step; then add up the overall aging costs for all time steps to obtain the total overall aging cost corresponding to the charging current sequence. The optimization solution process is to find the charging current sequence that minimizes the total overall aging cost among all charging current sequences that meet the constraints.

[0060] In this embodiment, by setting temperature constraints, state of charge constraints, and current constraints, and solving the charging current sequence that minimizes the overall aging cost under the constraints, the problem of how to achieve coordinated optimization of charging efficiency and battery life while ensuring battery safety is solved, thus realizing optimal charging control under safety constraints.

[0061] The above are merely feasible implementations of step S30 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S30.

[0062] Step S40: Adjust the output power of the charging pile according to the charging current sequence.

[0063] It should be noted that the output power of a charging pile refers to the electrical power output from the charging pile to the power battery, which is determined by the product of the output voltage and the output current. In actual control, the output power is changed by adjusting the output current of the charging pile, because the charging voltage is determined by the current state of the battery, not by the arbitrary setting of the charging pile.

[0064] Specifically, the charging current sequence obtained in step S30 is used as a control command; after receiving the control command, the charging pile control system adjusts the output current to the command value through the internal power adjustment circuit; the charging pile charges the power battery with the adjusted output current until the next control cycle arrives, at which point steps S10 to S40 are executed again to update the charging current command.

[0065] It is understandable that, since the battery state changes dynamically over time and the prediction model has inherent errors, performing step S40 and executing only the first value in the charging current sequence allows for the use of the latest battery state feedback information to re-predict and optimize in the next control cycle, forming closed-loop control. This reduces the impact of model prediction errors on control accuracy and improves the robustness and adaptability of the control system.

[0066] like Figure 3As shown in this embodiment, in the vehicle-pile cooperative adaptive charging control system based on battery health evaluation indicators, the vehicle-side battery management module collects the voltage, current, temperature, state of charge, and health status of the power battery in real time as battery operating status data. The collected data is sent to the charging pile control system through the vehicle-pile communication module. The state prediction module inside the charging pile control system establishes a battery state prediction model based on the received battery operating status data, predicts the battery temperature prediction sequence, state of charge prediction sequence, and health status prediction sequence within a preset time period, and outputs these prediction sequences as battery state prediction sequences to the health indicator calculation module.

[0067] The health index calculation module calculates the aging health index (BHI and AHI) based on battery operating status data and battery status prediction sequence, and sends the calculated aging health index to the optimization control module. When the aging health index is within a preset threshold range, the optimization control module solves for the optimal charging current sequence within a preset future time period based on a preset comprehensive aging cost function, temperature prediction sequence, and health status prediction sequence, and sends this current sequence to the charging power control module. The charging power control module adjusts the output power of the charging equipment according to the received optimal charging current and outputs the adjusted charging current to the power battery, thereby forming an adaptive charging control closed loop that coordinates the work between the vehicle side and the charging pile side.

[0068] This embodiment provides a vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators. Based on battery operating status data and a battery status prediction model, a battery status prediction sequence for a preset future time period is obtained. This battery status prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health status prediction sequence. An aging health index is obtained based on the battery operating status data and the battery status prediction sequence. When the aging health index is within a preset threshold range, a charging current sequence for the preset future time period is obtained based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence. The output power of the charging pile is adjusted according to the charging current sequence. This solves the technical problem that the charging control system cannot adjust the charging current in advance before the battery aging risk increases due to a lack of predictive ability for future battery status changes and a lack of comprehensive evaluation indicators for battery aging risk. It achieves the technical effect of adjusting the charging current in advance before the battery aging risk increases, effectively suppressing the battery aging rate while ensuring charging efficiency.

[0069] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4The vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators, step S10, further includes steps S11 to S15: Step S11: Obtain battery operating status data; Specifically, the battery operating status data collected in real time by the vehicle-side battery management system at a preset sampling period can be sent to the charging pile control system through the vehicle-to-charging pile communication interface (such as controller area network bus or power line communication).

[0070] Understandably, obtaining complete battery operating status data can provide the necessary input parameters for subsequent status prediction and health assessment, avoiding inaccurate predictions and assessments due to missing data.

[0071] Step S12: Based on the battery operating status data and the battery temperature prediction model, predict the battery temperature within a preset time period in the future to obtain a temperature prediction sequence; It should be noted that the temperature prediction sequence refers to the sequence of predicted battery temperatures arranged in chronological order at each discrete time step within a preset future period.

[0072] Specifically, the current battery temperature is used as the initial value for temperature prediction. The battery temperature prediction model calculates the predicted battery temperature for the next moment based on the current battery temperature and charging current, combined with the battery thermal capacity, equivalent internal resistance, heat dissipation coefficient, and ambient temperature. This predicted value is used as the new current temperature, and the above calculation is repeated until the number of prediction steps reaches the number of steps corresponding to the preset future time period.

[0073] Since battery temperature changes have thermal inertia and cannot change abruptly, step S12 can use a thermal balance model to capture the dynamic change pattern of battery temperature, thereby predicting the future trend of battery temperature in advance and avoiding safety risks caused by sudden temperature changes.

[0074] In one feasible implementation, step S12 may include: obtaining the current charging current and the current time step battery temperature based on the battery operating state data; using the current charging current and the current time step battery temperature as input to the battery temperature prediction model to obtain the predicted battery temperature for the next time step, wherein the battery temperature prediction model is obtained by discretizing the battery temperature change thermal balance model; using the predicted battery temperature as the current time step battery temperature, and returning to execute the step of using the current charging current and the current time step battery temperature as input to the battery temperature prediction model to obtain the predicted battery temperature for the next time step, until the accumulated number of time steps reaches the number of time steps corresponding to a future preset period, and arranging all predicted battery temperatures in chronological order to obtain a temperature prediction sequence.

[0075] It should be noted that the current charging current refers to the charging current output by the charging pile within this time step. The current battery temperature refers to the measured temperature of the battery at the beginning of this time step or the predicted temperature of the previous time step. The predicted battery temperature refers to the estimated battery temperature for the next time step calculated using the battery temperature prediction model. The battery temperature change thermal balance model is a differential equation describing the dynamic balance between Joule heat generation and heat dissipation to the environment within the battery. Its discretized form establishes the recursive relationship of battery temperature between adjacent time steps. The cumulative time steps refer to the number of time steps from the current moment onwards that have completed prediction calculations.

[0076] Specifically, the temperature change of the power battery during charging can be described using a thermal balance model:

[0077] Where C is the battery heat capacity; T is the battery temperature; I is the charging current; R is the battery equivalent internal resistance; and h is the heat dissipation coefficient. For ambient temperature, This represents the rate of temperature change.

[0078] Discretizing the model yields the battery temperature prediction model:

[0079] Where k is the time step. Let the battery temperature be at the k-th time step. Here, represents the battery temperature at time step (k+1); C represents the battery thermal capacity; I represents the charging current; R represents the battery equivalent internal resistance; and h represents the heat dissipation coefficient. Ambient temperature; Sampling time.

[0080] In this embodiment, by using a discretized thermal balance model for temperature recursion prediction, the problem of how to quantitatively predict future temperature changes based on the current state is solved, and quantitative prediction of battery temperature change trends is achieved.

[0081] The above are merely feasible implementations of step S12 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S12.

[0082] Step S13: Based on the temperature prediction sequence, the battery operating status data, and the battery health degradation model, predict the battery health status within a preset period in the future to obtain a health status prediction sequence.

[0083] It should be noted that the health status prediction sequence refers to the sequence of predicted battery health status values ​​arranged in chronological order at each discrete time step within a preset future period.

[0084] Specifically, the current health status of the battery is used as the initial value for health status prediction; the battery health degradation model calculates the predicted value of the battery health status at the next moment based on the current battery health status, charging current and battery temperature; wherein, the battery temperature comes from the predicted value of the corresponding time step in the temperature prediction sequence obtained in step S12; the predicted value is used as the new current health status, and the above calculation is repeated until the number of prediction steps reaches the number of steps corresponding to the future preset time period.

[0085] It is understandable that, since battery health degradation is an irreversible cumulative process and the degradation rate is significantly affected by temperature and charging rate, step S13 can use the health degradation model to capture the cumulative effect of battery aging, thereby predicting the degradation trend of battery health in the future and providing a quantitative basis for assessing aging risk.

[0086] In one feasible implementation, step S13 may include: obtaining the current charging current, the current time step battery temperature, and the current time step battery health status based on the battery operating state data; using the current charging current, the current time step battery temperature, and the current time step battery health status as inputs to the battery health degradation model to obtain the predicted battery health status for the next time step, wherein the battery health degradation model is obtained by discretizing the battery health status degradation rate equation; using the predicted battery health status as the current time step battery health status, and returning to execute the step of using the current charging current, the current time step battery temperature, and the current time step battery health status as inputs to the battery health degradation model to obtain the predicted battery health status for the next time step, until the accumulated number of time steps reaches the number of time steps corresponding to a future preset period, and arranging all predicted battery health statuses in chronological order to obtain the health status prediction sequence.

[0087] It should be noted that the battery health status at the current time step refers to the measured value of the battery health status at the beginning of this time step or the predicted value from the previous time step. The predicted battery health status refers to the estimated battery health status for the next time step calculated using the battery health degradation model. The battery health status degradation rate equation is a differential equation describing the rate of degradation of the battery health status over time. This rate has an exponential relationship with the charging current and battery temperature, and its discretized form establishes a recursive relationship between the battery health status of adjacent time steps.

[0088] Specifically, the change in battery state of health (SOH) is closely related to temperature and charge rate, and its degradation rate can be expressed as:

[0089] Where r is the degradation coefficient; The current influence index is represented by E, which is the activation energy. Represents the rate of change in health status; I is the charging current; R is the battery's equivalent internal resistance; T is the battery temperature; e is the base of the natural logarithm.

[0090] Discretization yields a battery health state prediction model:

[0091] Where k is the time step. The battery health status at time step k. This represents the battery health state at time step k+1; I is the charging current; and R is the battery's equivalent internal resistance. Let r be the predicted battery temperature at the k-th time step; r is the degradation coefficient. The current influence index is represented by E, which is the activation energy. To represent the sampling time, e is the base of the natural logarithm.

[0092] In this embodiment, by employing a discrete health degradation model for recursive prediction of health status, the problem of how to quantitatively predict health status degradation based on the current state and future temperature prediction values ​​is solved, thus realizing quantitative prediction of the battery aging process.

[0093] The above are merely feasible implementations of step S13 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S13.

[0094] Step S14: Based on the battery operating status data and the state of charge prediction model, predict the state of charge within a preset time period in the future to obtain a state of charge prediction sequence.

[0095] It should be noted that the state of charge prediction sequence refers to the sequence of predicted values ​​of the battery state of charge arranged in chronological order at each discrete time step within a preset future period.

[0096] Specifically, the current state of charge (SOC) of the battery is used as the initial value for SOC prediction. The SOC prediction model calculates the predicted SOC value for the next moment based on the current SOC and charging current, combined with the battery's rated capacity. This predicted value is then used as the new current SOC, and the above calculation is repeated until the number of prediction steps reaches the number of steps corresponding to a future preset time period.

[0097] It is understandable that since the state of charge increases monotonically with the integral of the charging current, and the charging current may change dynamically in the future based on the optimization results, step S14 can use the ampere-hour integral model to predict the future changes of the state of charge, thereby ensuring that the state of charge will never exceed the safe upper limit during the optimization process and avoiding overcharging.

[0098] In one feasible implementation, step S14 may include: obtaining the battery rated capacity, current charging current, and current time step state of charge (SPC) based on the battery operating state data; using the battery rated capacity, current charging current, and current time step SPC as input to the SPC prediction model to obtain the predicted SPC for the next time step, wherein the SPC prediction model is obtained by discretizing the ampere-hour integral equation; using the predicted SPC as the current time step SPC, and returning to execute the step of using the battery rated capacity, current charging current, and current time step SPC as input to the SPC prediction model to obtain the predicted SPC for the next time step, until the accumulated number of time steps reaches the number of time steps corresponding to a future preset time period, and arranging all predicted SPCs in chronological order to obtain the SPC prediction sequence.

[0099] It should be noted that the rated capacity of a battery refers to the usable charge capacity of the battery under standard conditions as specified by the battery manufacturer, usually measured in ampere-hours (Ahs). The current time step state of charge (SOC) refers to the measured SOC at the beginning of this time step or the predicted SOC from the previous time step. The predicted SOC refers to the estimated SOC for the next time step calculated using a SOC prediction model. The Ah integral equation describes the mathematical relationship between the SOC and the time integral of the charging current; its discretized form establishes a recursive relationship between the SOCs of adjacent time steps.

[0100] Specifically, the discrete form of the ampere-hour integral model is:

[0101] in, This represents the charging state at the k-th time step; This represents the charged state at the (k+1)th time step; This is the current charging current; Sampling time; This refers to the battery's rated capacity.

[0102] In this embodiment, by using a discretized ampere-hour integral model for recursive prediction of the state of charge, the problem of how to quantitatively predict changes in the state of charge based on the current state of charge and the future charging current is solved, and quantitative prediction of the trend of changes in the state of charge is realized.

[0103] The above are merely feasible implementations of step S14 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S14.

[0104] Step S15: Use the temperature prediction sequence, the health status prediction sequence, and the state of charge prediction sequence as the battery state prediction sequence.

[0105] Specifically, the temperature prediction sequence obtained in step S12, the health state prediction sequence obtained in step S13, and the state of charge prediction sequence obtained in step S14 are aligned along the same prediction time axis to form a composite sequence containing three state dimensions. This composite sequence is the battery state prediction sequence. In subsequent steps S20 and S30, the battery state prediction sequence is used as the basic input for calculating the aging health index and solving for the optimal charging current.

[0106] It is understandable that, since there is a coupling relationship between battery temperature, health status and state of charge (e.g., temperature affects the rate of health degradation), step S15 can encapsulate the prediction results of the three state dimensions into a unified battery state prediction sequence, providing a unified data interface for subsequent comprehensive evaluation and optimization control, and avoiding information loss and calculation chaos caused by data dispersion.

[0107] This embodiment provides a vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators. The method acquires battery operating status data; predicts battery temperature within a preset time period based on the battery operating status data and a battery temperature prediction model, obtaining a temperature prediction sequence; predicts battery health status within a preset time period based on the temperature prediction sequence, the battery operating status data, and a battery health degradation model, obtaining a health status prediction sequence; predicts the state of charge within a preset time period based on the battery operating status data and a state of charge prediction model, obtaining a state of charge prediction sequence; and uses the temperature prediction sequence, the health status prediction sequence, and the state of charge prediction sequence as the battery state prediction sequence. By employing temperature recursive prediction based on a thermal balance model, health status recursive prediction based on a degradation rate equation, and state of charge recursive prediction based on an ampere-hour integral equation, the method avoids the limitation of relying solely on the current state for judgment, solves the technical problem of being unable to predict future battery state changes, and thus provides a complete state prediction data foundation for achieving predictive charging control.

[0108] For example, to help understand the implementation process of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators obtained by combining this embodiment with the above embodiment one, please refer to... Figure 5 , Figure 5A simplified flowchart of a vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators is provided, specifically: Battery operation data is collected, including battery voltage, current, temperature, state of charge (SOC), and state of health. The collected data is then filtered and normalized before preprocessing. Based on the preprocessed operation data, a battery state prediction model is established. This model includes a battery temperature prediction model, a battery health degradation model, and a SOC prediction model. These models predict the battery temperature change trend, battery SOC change trend, and battery health degradation trend within future time windows, respectively, thus obtaining a battery state prediction sequence composed of temperature prediction sequence, SOC prediction sequence, and health state prediction sequence.

[0109] After completing the state prediction, health indicators are calculated based on the predicted battery state sequence. These indicators include a comprehensive health indicator and an aging health index. The comprehensive health indicator is a weighted composite of battery temperature, state of charge, and health status. The aging health index is further calculated by combining the comprehensive health indicator, the amount of health degradation per unit time, and the charging rate. The calculated aging health index is used to determine the current aging health risk level of the battery. When the aging health index is within the preset medium-risk threshold range, the model predictive control optimization process is triggered. With minimizing the comprehensive aging cost as the optimization objective, the temperature prediction sequence and the health status prediction sequence are used as inputs to the comprehensive aging cost function. Under the conditions of satisfying temperature constraints, state of charge constraints, and current constraints, the optimal charging current is solved.

[0110] The optimal charging current obtained from the solution is output as a control command. The charging pile adjusts its output power according to the command, thereby completing the charging current adjustment for this control cycle. At the beginning of the next control cycle, the system returns to the step of collecting battery operating data and re-executes the above prediction, evaluation and optimization process to form a rolling optimization closed-loop control mechanism to achieve dynamic adaptive adjustment of the charging current.

[0111] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators. Any simple modifications based on this technical concept are within the protection scope of this application.

[0112] This application also provides a vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators. Please refer to [link / reference]. Figure 6 The vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators includes: Prediction module 10 is used to obtain a battery state prediction sequence for a future preset time period based on battery operating state data and battery state prediction model, wherein the battery state prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health state prediction sequence. The aging module 20 is used to obtain an aging health index based on the battery operating status data and the battery status prediction sequence. The current module 30 is used to obtain a charging current sequence for a future preset time period based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence when the aging health index is within a preset threshold range. The adjustment module 40 is used to adjust the output power of the charging pile according to the charging current sequence.

[0113] The vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators provided in this application, employing the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in the above embodiments, can solve the technical problem of effectively suppressing battery aging rate while ensuring charging efficiency. Compared with the prior art, the beneficial effects of the vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators provided in this application are the same as the beneficial effects of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators provided in the above embodiments, and other technical features in the vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0114] The prediction module 10 is further configured to acquire battery operating status data; predict the battery temperature within a preset time period based on the battery operating status data and the battery temperature prediction model, thereby obtaining a temperature prediction sequence; predict the battery health status within a preset time period based on the temperature prediction sequence, the battery operating status data, and the battery health degradation model, thereby obtaining a health status prediction sequence; predict the state of charge within a preset time period based on the battery operating status data and the state of charge prediction model, thereby obtaining a state of charge prediction sequence; and use the temperature prediction sequence, the health status prediction sequence, and the state of charge prediction sequence as the battery status prediction sequence.

[0115] The prediction module 10 is further configured to obtain the current charging current and the current time step battery temperature based on the battery operating status data; use the current charging current and the current time step battery temperature as input to the battery temperature prediction model to obtain the predicted battery temperature for the next time step, wherein the battery temperature prediction model is obtained by discretizing the battery temperature change thermal balance model; use the predicted battery temperature as the current time step battery temperature, and return to execute the step of using the current charging current and the current time step battery temperature as input to the battery temperature prediction model to obtain the predicted battery temperature for the next time step, until the accumulated number of time steps reaches the number of time steps corresponding to a future preset period, and arrange all predicted battery temperatures in chronological order to obtain a temperature prediction sequence.

[0116] The prediction module 10 is further configured to obtain the current charging current, the current time step battery temperature, and the current time step battery health status based on the battery operating status data; use the current charging current, the current time step battery temperature, and the current time step battery health status as inputs to the battery health degradation model to obtain the predicted battery health status for the next time step, wherein the battery health degradation model is obtained by discretizing the battery health status degradation rate equation; use the predicted battery health status as the current time step battery health status, and return to execute the step of using the current charging current, the current time step battery temperature, and the current time step battery health status as inputs to the battery health degradation model to obtain the predicted battery health status for the next time step, until the accumulated number of time steps reaches the number of time steps corresponding to a future preset period, and arrange all predicted battery health statuses in chronological order to obtain the health status prediction sequence.

[0117] The prediction module 10 is further configured to obtain the battery rated capacity, current charging current, and current time step state of charge (SPC) based on the battery operating state data; use the battery rated capacity, current charging current, and current time step SPC as input to the SPC prediction model to obtain the predicted SPC for the next time step, wherein the SPC prediction model is obtained by discretizing the ampere-hour integral equation; use the predicted SPC as the current time step SPC, and return to execute the step of using the battery rated capacity, current charging current, and current time step SPC as input to the SPC prediction model to obtain the predicted SPC for the next time step, until the accumulated number of time steps reaches the number of time steps corresponding to a future preset time period, and arrange all predicted SPCs in chronological order to obtain the SPC prediction sequence.

[0118] The aging module 20 is further configured to obtain the current charging current, battery rated capacity, rated temperature, rated state of charge, rated charging rate, and reference health status based on the battery operating status data; obtain a comprehensive battery health index based on the battery status prediction sequence, the rated temperature, and the rated state of charge; obtain the amount of health degradation per unit time based on the health status prediction sequence; and obtain an aging health index based on the comprehensive battery health index, the amount of health degradation per unit time, the reference health status, the rated charging rate, the battery rated capacity, and the current charging current.

[0119] The current module 30 is further configured to obtain constraints based on a preset temperature threshold, a preset battery charge threshold, and a preset charging current threshold; to use minimizing the overall aging cost as the optimization objective of the preset overall aging cost function; and to use the aging health index, the temperature prediction sequence, and the health status prediction sequence as inputs to the preset overall aging cost function, and to solve the preset overall aging cost function based on the constraints and the optimization objective to obtain the charging current sequence within a preset future time period.

[0120] This application provides a vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators. The vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators includes: at least one processor; and a memory communicatively connected to at least one processor; wherein the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to execute the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in the above embodiment 1.

[0121] The following is for reference. Figure 7 This document illustrates a structural schematic diagram of a vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators, suitable for implementing embodiments of this application. The vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The vehicle-pile cooperative adaptive charging control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0122] like Figure 7 As shown, the vehicle-to-pile cooperative adaptive charging control device based on battery health evaluation indicators may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the vehicle-to-pile cooperative adaptive charging control device based on battery health evaluation indicators. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the vehicle-to-pile cooperative adaptive charging control device based on battery health evaluation indicators to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a vehicle-to-pile cooperative adaptive charging control device based on battery health evaluation indicators with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0123] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0124] The vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators provided in this application, employing the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in the above embodiments, can solve the technical problem of effectively suppressing battery aging rate while ensuring charging efficiency. Compared with the prior art, the beneficial effects of the vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators provided in this application are the same as the beneficial effects of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators provided in the above embodiments, and other technical features in this vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0125] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0127] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators in the above embodiments.

[0128] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0129] The aforementioned computer-readable storage medium may be included in the vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators; or it may exist independently and not be assembled into the vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators.

[0130] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators, the device causes the following: Based on battery operating status data and a battery status prediction model, it obtains a battery status prediction sequence for a future preset time period, wherein the battery status prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health status prediction sequence; Based on the battery operating status data and the battery status prediction sequence, it obtains an aging health index; When the aging health index is within a preset threshold range, it obtains a charging current sequence for the future preset time period based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence; and Adjusts the output power of the charging pile according to the charging current sequence.

[0131] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0133] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0134] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described vehicle-to-pile cooperative adaptive charging control method based on battery health evaluation indicators. This solves the technical problem of effectively suppressing battery aging rate while ensuring charging efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the vehicle-to-pile cooperative adaptive charging control method based on battery health evaluation indicators provided in the above embodiments, and will not be repeated here.

[0135] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators as described above.

[0136] The computer program product provided in this application can solve the technical problem of effectively suppressing the battery aging rate while ensuring charging efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators provided in the above embodiments, and will not be repeated here.

[0137] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators, characterized in that, The method includes: Based on battery operating status data and battery status prediction model, a battery status prediction sequence for a future preset time period is obtained, wherein the battery status prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health status prediction sequence. Based on the battery operating status data and the battery status prediction sequence, an aging health index is obtained; When the aging health index is within a preset threshold range, the charging current sequence for a future preset period is obtained based on the preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence. The output power of the charging pile is adjusted according to the charging current sequence.

2. The method as described in claim 1, characterized in that, The battery state prediction model includes a battery temperature prediction model, a battery health degradation model, and a state of charge prediction model. The step of obtaining a battery state prediction sequence for a future preset time period based on battery operating status data and a battery state prediction model includes: Obtain battery operating status data; Based on the battery operating status data and the battery temperature prediction model, the battery temperature is predicted within a preset period in the future to obtain a temperature prediction sequence. Based on the temperature prediction sequence, the battery operating status data, and the battery health degradation model, the battery health status is predicted within a preset period in the future, and a health status prediction sequence is obtained. Based on the battery operating status data and the state of charge prediction model, the state of charge is predicted within a preset time period to obtain a state of charge prediction sequence. The temperature prediction sequence, the health status prediction sequence, and the state of charge prediction sequence are used as the battery state prediction sequence.

3. The method as described in claim 2, characterized in that, The process of predicting the battery temperature within a preset time period based on the battery operating status data and the battery temperature prediction model to obtain a temperature prediction sequence includes: Based on the battery operating status data, the current charging current and the battery temperature at the current time step are obtained; The current charging current and the battery temperature at the current time step are used as inputs to the battery temperature prediction model to obtain the predicted battery temperature at the next time step. The battery temperature prediction model is obtained by discretizing the battery temperature change thermal balance model. The predicted battery temperature is used as the current time step battery temperature, and the process returns to the step of using the current charging current and the current time step battery temperature as inputs to the battery temperature prediction model to obtain the predicted battery temperature for the next time step, until the cumulative number of time steps reaches the number of time steps corresponding to a future preset period. All predicted battery temperatures are then arranged in chronological order to obtain a temperature prediction sequence.

4. The method as described in claim 2, characterized in that, The process of predicting the battery health status within a preset time period based on the temperature prediction sequence, the battery operating status data, and the battery health degradation model, to obtain a health status prediction sequence, includes: Based on the battery operating status data, the current charging current, the battery temperature at the current time step, and the battery health status at the current time step are obtained. The current charging current, the battery temperature at the current time step, and the battery health status at the current time step are used as inputs to the battery health degradation model to obtain the predicted battery health status at the next time step. The battery health degradation model is obtained by discretizing the battery health status degradation rate equation. The predicted battery health state is used as the current time step battery health state, and the process returns to the step of using the current charging current, the current time step battery temperature, and the current time step battery health state as inputs to the battery health degradation model to obtain the predicted battery health state for the next time step, until the cumulative number of time steps reaches the number of time steps corresponding to a future preset period. All predicted battery health states are then arranged in chronological order to obtain the health state prediction sequence.

5. The method as described in claim 2, characterized in that, The step of predicting the state of charge (SOC) within a preset time period based on the battery operating status data and the SOC prediction model to obtain a SOC prediction sequence includes: Based on the battery operating status data, the battery rated capacity, current charging current, and current state of charge at the current time step are obtained; The rated capacity of the battery, the current charging current, and the state of charge at the current time step are used as inputs to the state of charge prediction model to obtain the predicted state of charge at the next time step. The state of charge prediction model is obtained by discretizing the ampere-hour integral equation. The predicted state of charge is used as the current state of charge at the current time step, and the process of taking the battery rated capacity, the current charging current, and the current state of charge at the current time step as inputs to the state of charge prediction model to obtain the predicted state of charge at the next time step is repeated until the number of accumulated time steps reaches the number of time steps corresponding to a future preset period. All predicted states of charge are then arranged in chronological order to obtain the state of charge prediction sequence.

6. The method as described in claim 1, characterized in that, The step of obtaining the aging health index based on the battery operating status data and the battery status prediction sequence includes: Based on the battery operating status data, the current charging current, battery rated capacity, rated temperature, rated state of charge, rated charging rate, and reference health status are obtained. Based on the battery state prediction sequence, the rated temperature, and the rated state of charge, a comprehensive battery health index is obtained; Based on the health status prediction sequence, the amount of health degradation per unit time is obtained; The aging health index is obtained based on the battery comprehensive health index, the amount of health degradation per unit time, the reference health state, the rated charging rate, the battery rated capacity, and the current charging current.

7. The method as described in claim 1, characterized in that, When the aging health index is within a preset threshold range, a charging current sequence for a future preset time period is obtained based on a preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence, including: The constraints are obtained based on the preset temperature threshold, preset battery charge threshold, and preset charging current threshold. Minimizing the overall aging cost is used as the optimization objective of the preset overall aging cost function; The aging health index, the temperature prediction sequence, and the health status prediction sequence are used as inputs to a preset comprehensive aging cost function. The preset comprehensive aging cost function is solved according to the constraints and the optimization objective to obtain the charging current sequence within a preset future time period.

8. A vehicle-charging station cooperative adaptive charging control device based on battery health evaluation indicators, characterized in that, The device includes: The prediction module is used to obtain a battery state prediction sequence for a future preset time period based on battery operating status data and a battery state prediction model. The battery state prediction sequence includes a battery temperature prediction sequence, a state of charge prediction sequence, and a health state prediction sequence. An aging module is used to obtain an aging health index based on the battery operating status data and the battery status prediction sequence. The current module is used to obtain the charging current sequence for a future preset time period based on the preset comprehensive aging cost function, the temperature prediction sequence, and the health status prediction sequence when the aging health index is within a preset threshold range. An adjustment module is used to adjust the output power of the charging pile according to the charging current sequence.

9. A vehicle-pile cooperative adaptive charging control device based on battery health evaluation indicators, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the vehicle-pile cooperative adaptive charging control method based on battery health evaluation indicators as described in any one of claims 1 to 7.

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