Power control method and device for power battery

Abstract

The application relates to the technical field of electric vehicle battery management, in particular to a power control method and device for a power battery, wherein the method comprises the following steps: sending effective data of a power battery of a vehicle to a server, receiving a working condition optimal matching calibration quantity generated by the server based on the effective data, updating a current vehicle end calibration quantity of the vehicle according to the working condition optimal matching calibration quantity, and acquiring a power limitation coefficient and a power filtering rate of the power battery, so as to calculate an actual allowable power of the power battery and control power output of the power battery. According to the application, the power of the power battery can be controlled based on the actual condition of the power battery, the battery power limitation coefficient and the power filtering rate are adjusted in real time according to the working condition optimal matching calibration quantity, the flexible adjustment of the battery power is realized, the protection effect of the power battery of the vehicle is enhanced, the safe and stable operation of the vehicle is ensured, and the user experience is improved.

Description

Technical Field

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

[0002] The discharge power of a power battery affects vehicle operation and battery safety, but its allowable power is affected by a combination of factors such as temperature and aging, making it difficult to accurately assess. Therefore, appropriate battery power management strategies must be implemented during the actual use of electric vehicles to maintain the safe and stable operation of the entire vehicle.

[0003] In related technologies, the voltage of a single power battery cell is mainly used as the input for power battery power control. For example, patent CN112763918A controls the switching of allowable power between peak discharge power and continuous discharge power based on different battery temperatures and states of charge, according to different pool models, in order to achieve power control.

[0004] However, the relevant technologies do not consider the exponential decay of the power limiting factor and its linear characteristics, resulting in a lack of power limiting capability in the later stages of single-cell voltage drop and an inability to adapt to the limiting requirements of different ranges. Furthermore, the power limiting factor corresponding to the secondary voltage threshold cannot be directly calibrated because it is affected by the basic power limiting factor and the equal fraction of the range. This narrows the application scope of battery power management in different scenarios, makes it impossible to fully realize the safety protection of the power battery, and makes it difficult to effectively avoid power interruption in the vehicle, thus reducing the safety and reliability of driving. These issues urgently need to be addressed. Summary of the Invention

[0005] This application provides a power control method and device for a power battery to solve the problems in related technologies that do not consider the exponential decay law and linear characteristics of the power limiting coefficient, resulting in a lack of power limiting capability in the later stage of single-cell voltage drop and an inability to adapt to the limiting requirements of different ranges. Furthermore, the power limiting coefficient corresponding to the secondary voltage threshold cannot be directly calibrated because it is affected by the basic power limiting coefficient and the equal fraction of the range. This narrows the application scope of battery power management in different scenarios, fails to fully realize the safety protection of the power battery, and makes it difficult to effectively avoid power interruption of the vehicle, thereby reducing the safety and reliability of driving.

[0006] The first aspect of this application provides a power control method for a power battery, applied to a vehicle, comprising the following steps: sending valid power battery data of the vehicle to a server, and receiving an optimal matching calibration value generated by the server based on the valid data; updating the current vehicle-side calibration value of the vehicle according to the optimal matching calibration value; obtaining a preset power limiting range based on the current vehicle-side calibration value; obtaining the power limiting coefficient and power filtering rate of the power battery according to the extreme values ​​of the individual cell voltages of the power battery; calculating the actual allowable power of the power battery according to the power limiting coefficient, the power filtering rate, and the current peak power table of the vehicle's charging and discharging process, so as to control the power output of the power battery.

[0007] Based on the above technical means, the embodiments of this application can control the power of the vehicle's power battery according to the actual condition of the power battery, and adjust the battery power limiting coefficient and power filtering rate in real time according to the optimal matching calibration value, thereby realizing flexible adjustment of battery power, enhancing the protection effect of the vehicle's power battery, ensuring the safe and stable operation of the vehicle, and improving the user experience.

[0008] Optionally, in one embodiment of this application, before calculating the actual allowable power of the power battery, the method further includes: acquiring the charge and discharge data of the power battery; updating the peak power table of the vehicle based on the charge and discharge data to obtain the current peak power table, and sending the current peak power table to the server.

[0009] Based on the above technical means, the embodiments of this application can obtain the charging and discharging data of the power battery, update the peak power table of the vehicle based on the charging and discharging data, obtain the current peak power table, and send the current peak power table to the server to obtain the data basis required for calculating the allowable power of the power battery.

[0010] Optionally, in one embodiment of this application, obtaining the power limiting coefficient and power filtering rate of the power battery based on the extreme value of the single cell voltage of the power battery includes: substituting the extreme value of the single cell voltage into a preset power limiting coefficient mapping model and a preset power filtering rate mapping model, and outputting the power limiting coefficient and the power filtering rate.

[0011] Based on the above technical means, the embodiments of this application can substitute the extreme values ​​of the single cell voltage into the preset power limit coefficient mapping model and the preset power filter rate mapping model, and output the power limit coefficient and the power filter rate, thereby comprehensively improving the calculation level of power battery related data, so as to improve the timeliness and accuracy of power battery power control and improve the safety management level of the vehicle.

[0012] Optionally, in one embodiment of this application, before receiving the optimal matching calibration value of the operating condition generated by the server based on the valid data, the method further includes: extracting the actual state factor of the power battery based on the valid data of the vehicle's power battery, and matching the optimal matching calibration value of the power battery in the optimal action table according to the actual state factor.

[0013] Based on the above technical means, the embodiments of this application can extract the actual state factor of the power battery based on the effective data of the vehicle's power battery, and match the optimal matching calibration quantity of the power battery's working condition in the optimal action table according to the actual state factor. In this way, when the state of charge estimation of the power battery deviates or the acquisition temperature is abnormal, it can ensure timely control of the power battery power, improve the safe operation of the power battery, and ensure stable power delivery of the vehicle.

[0014] Optionally, in one embodiment of this application, the step of matching the optimal operating condition calibration value of the power battery in the optimal action table according to the actual state factor includes: matching the corresponding original element in the original element set based on the state factor of the power battery; obtaining the optimal action table of the original element to match the optimal action; and confirming the optimal operating condition calibration value of the power battery according to the optimal action.

[0015] Based on the above technical means, the embodiments of this application can match the corresponding original elements in the original element set based on the state factors of the power battery, obtain the optimal action table of the original elements to match the optimal action, confirm the optimal matching calibration value of the power battery's operating condition based on the optimal action, collect operating data to build a learning environment through the collaboration between the vehicle and the cloud, and have the intelligent agent mine the optimal calibration value from the environment and push it to the vehicle, thereby effectively avoiding the mismatch phenomenon of calibration value as the battery operates, and further improving the accuracy of power battery power control.

[0016] Optionally, in one embodiment of this application, before receiving the optimal matching calibration value generated by the server based on the valid data, the method further includes: determining whether the optimal matching calibration value meets a preset valid condition; if the optimal matching calibration value meets the preset valid condition, controlling the vehicle to store the optimal matching calibration value to update the current vehicle-side calibration value of the vehicle.

[0017] Based on the above technical means, the embodiments of this application can determine whether the optimal matching calibration value under working conditions meets the preset valid conditions, and when the optimal matching calibration value under working conditions meets the preset valid conditions, control the vehicle to store the optimal matching calibration value under working conditions in order to update the current vehicle-side calibration value of the vehicle, thereby further ensuring the effectiveness and reliability of vehicle-cloud data communication and further maintaining the stable control of power battery power.

[0018] Optionally, in one embodiment of this application, before obtaining the power limiting coefficient and the power filtering rate of the power battery based on the extreme values ​​of the individual cell voltages of the power battery, the method further includes: obtaining initial values ​​of the power limiting coefficient and the power filtering rate corresponding to each stage based on the historical data of the power battery.

[0019] Based on the above technical means, the embodiments of this application can obtain the initial values ​​of the power limiting coefficient and power filtering rate corresponding to each stage based on the historical data of the power battery before obtaining the power limiting coefficient and power filtering rate of the power battery according to the extreme value of the single cell voltage of the power battery, so as to further update the corresponding data according to the updated calibration value of the vehicle.

[0020] A second aspect of this application provides a power control device for a power battery, applied to a vehicle, comprising: an update module, configured to send valid power battery data of the vehicle to a server, and receive an optimal matching calibration value generated by the server based on the valid data, and update the current vehicle-side calibration value of the vehicle according to the optimal matching calibration value; an acquisition module, configured to acquire a preset power limit range based on the current vehicle-side calibration value of the vehicle, and acquire the power limit coefficient and power filtering rate of the power battery according to the extreme values ​​of the individual cell voltages of the power battery; and a control module, configured to calculate the actual allowable power of the power battery according to the power limit coefficient, the power filtering rate, and the current peak power table of the vehicle's charging and discharging process, so as to control the power output of the power battery.

[0021] Optionally, in one embodiment of this application, the control module further includes: a first acquisition unit, configured to acquire charge and discharge data of the power battery before calculating the actual allowable power of the power battery; and a transmission unit, configured to update the peak power table of the vehicle according to the charge and discharge data, obtain the current peak power table, and send the current peak power table to the server.

[0022] Optionally, in one embodiment of this application, the acquisition module includes: an output unit, configured to substitute the individual voltage extreme value into a preset power limiting coefficient mapping model and a preset power filtering rate mapping model, and output the power limiting coefficient and the power filtering rate.

[0023] Optionally, in one embodiment of this application, the update module includes: an update unit, configured to, before receiving the optimal matching calibration value of the operating condition generated by the server based on the valid data, extract the actual state factor of the power battery based on the valid data of the vehicle's power battery, and match the optimal matching calibration value of the power battery in the optimal action table according to the actual state factor.

[0024] Optionally, in one embodiment of this application, the updating unit is further configured to: match corresponding original elements in the original element set based on the state factor of the power battery; obtain the optimal action table of the original elements to match the optimal action; and confirm the optimal matching calibration value of the power battery's operating condition based on the optimal action.

[0025] Optionally, in one embodiment of this application, the update module further includes: a judgment unit, configured to determine whether the optimal matching calibration value for operating conditions meets preset valid conditions before receiving the optimal matching calibration value for operating conditions generated by the server based on the valid data; and a storage unit, configured to control the vehicle to store the optimal matching calibration value for operating conditions when the optimal matching calibration value for operating conditions meets the preset valid conditions, so as to update the current vehicle-side calibration value of the vehicle.

[0026] Optionally, in one embodiment of this application, the acquisition module further includes: a second acquisition unit, configured to acquire initial values ​​of the power limiting coefficient and the power filtering rate corresponding to each stage based on historical data of the power battery before acquiring the power limiting coefficient and the power filtering rate of the power battery according to the extreme values ​​of the single cell voltage of the power battery.

[0027] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power control method for a power battery as described in the above embodiments.

[0028] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power control method for a power battery as described above.

[0029] The beneficial effects of this application are:

[0030] (1) The embodiments of this application can control the power of the power battery based on the actual condition of the vehicle power battery, and adjust the battery power limit coefficient and power filtering rate in real time according to the optimal matching calibration, thereby realizing flexible adjustment of battery power, enhancing the protection effect of the vehicle power battery, ensuring the safe and stable operation of the vehicle, and improving the user experience.

[0031] (2) The embodiments of this application can substitute the extreme values ​​of the single cell voltage into the preset power limit coefficient mapping model and the preset power filter rate mapping model, and output the power limit coefficient and the power filter rate, thereby comprehensively improving the calculation level of the power battery related data, so as to improve the timeliness and accuracy of power battery power control and improve the safety management level of the vehicle.

[0032] (3) The embodiments of this application can match the corresponding original elements in the original element set based on the state factors of the power battery, obtain the optimal action table of the original elements to match the optimal action, confirm the optimal matching calibration quantity of the power battery working condition based on the optimal action, collect operating data to build a learning environment through the collaboration between the vehicle and the cloud, and the intelligent agent mines the optimal calibration quantity from the environment and pushes it to the vehicle, thereby effectively avoiding the mismatch phenomenon of calibration quantity with battery operation, and further improving the accuracy of power battery power control.

[0033] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0034] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0035] Figure 1 This is a flowchart of a power control method for a power battery according to an embodiment of this application;

[0036] Figure 2 This is a schematic diagram of a cloud-based reinforcement learning-based calibration quantification process according to one embodiment of this application;

[0037] Figure 3 This is a schematic diagram illustrating an example of voltage threshold levels at various stages according to an embodiment of this application;

[0038] Figure 4 This is a schematic diagram illustrating the power limiting factor characteristics of one embodiment of this application;

[0039] Figure 5 This is a schematic diagram of the power filtering rate characteristics of one embodiment of this application;

[0040] Figure 6 This is a flowchart of a vehicle-cloud collaborative electric vehicle power control according to an embodiment of this application;

[0041] Figure 7 This is a schematic diagram of the power control device for a power battery according to an embodiment of this application;

[0042] Figure 8 This is a structural schematic diagram of a vehicle according to an embodiment of this application.

[0043] Among them, 10-power control device for power battery; 100-update module, 200-acquisition module and 300-control module; 801-memory, 802-processor and 803-communication interface. Detailed Implementation

[0044] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0045] The following description, with reference to the accompanying drawings, illustrates a power control method and apparatus for a power battery according to embodiments of this application. Addressing the shortcomings of the aforementioned background technologies, which fail to consider the exponential decay and linear characteristics of the power limiting coefficient, resulting in insufficient power limiting capability after a single-cell voltage drop and an inability to adapt to the limiting requirements of different ranges, and because the power limiting coefficient corresponding to the secondary voltage threshold cannot be directly calibrated due to the influence of the base power limiting coefficient and the equal fraction of the range, the application scope of battery power management in different scenarios is narrowed. This hinders comprehensive safety protection of the power battery, makes it difficult to effectively prevent power interruption in the vehicle, and reduces driving safety and reliability. This application provides a power control method for a power battery that can control the power battery power based on the actual condition of the vehicle's power battery. It adjusts the battery power limiting coefficient and power filtering rate in real time according to the optimal matching calibration value, achieving flexible adjustment of battery power, enhancing the protection effect of the vehicle's power battery, ensuring the safe and stable operation of the vehicle, and improving the user experience. This solves the problems in related technologies that do not consider the exponential decay of the power limiting factor and its linear characteristics, resulting in a lack of power limiting capability in the later stages of single-cell voltage drop and an inability to adapt to the limiting requirements of different ranges. Furthermore, the power limiting factor corresponding to the secondary voltage threshold cannot be directly calibrated because it is affected by the basic power limiting factor and the equal fraction of the range. This narrows the application scope of battery power management in different scenarios, makes it impossible to fully realize the safety protection of the power battery, and makes it difficult to effectively avoid power interruption in the vehicle, thus reducing the safety and reliability of driving.

[0046] Specifically, Figure 1 This is a schematic flowchart illustrating a power control method for a power battery provided in an embodiment of this application.

[0047] like Figure 1 As shown, the power control method for this power battery includes the following steps:

[0048] In step S101, the valid data of the vehicle's power battery is sent to the server, and the optimal matching calibration value generated by the server based on the valid data is received. The current vehicle-side calibration value of the vehicle is updated according to the optimal matching calibration value.

[0049] It is understood that, in the embodiments of this application, the optimal matching calibration value for the operating condition is the optimal matching calibration value for the operating condition corresponding to the vehicle in the current scenario, obtained from cloud data. The vehicle can obtain valid data of the power battery and send it to the cloud server for processing to obtain the current optimal matching calibration value. Then, the vehicle receives the optimal matching calibration value for the operating condition and updates the vehicle's current calibration value data.

[0050] Optionally, in one embodiment of this application, before receiving the optimal matching calibration value of the operating condition generated by the server based on valid data, the method further includes: extracting the actual state factor of the power battery based on the valid data of the vehicle's power battery, and matching the optimal matching calibration value of the power battery in the optimal action table according to the actual state factor.

[0051] It is understood that, in the embodiments of this application, the server can construct the optimal action table in the cloud, and then extract the actual state factors of the power battery, match the current operating condition of the power battery corresponding to the actual state factors in the optimal action table, so as to obtain the optimal matching calibration value of the power battery based on the obtained current operating condition of the power battery.

[0052] For example, state factors can be selected, such as temperature and SOC. An optimal action table can be constructed using temperature and SOC. The SOC range in the table includes 0% to 100%, while the temperature covers all power ranges in the peak power table where the power is not zero. The vehicle end is divided into finite intervals according to temperature, and the calibration value within each interval is kept uniform. The calibration value within each temperature interval is selected from the table according to the most conservative principle to match and obtain the optimal matching calibration value for the power battery under the operating conditions.

[0053] This application embodiment can extract the actual state factor of the power battery based on the effective data of the vehicle's power battery, and match the optimal matching calibration quantity of the power battery's working condition in the optimal action table according to the actual state factor. In this way, when there is a deviation in the estimation of the state of charge of the power battery or an abnormality in the acquisition temperature, it can ensure timely control of the power battery power, improve the safe operation of the power battery, and ensure stable power delivery of the vehicle.

[0054] Optionally, in one embodiment of this application, matching the optimal matching calibrator of the power battery's operating condition in the optimal action table based on the actual state factors includes: matching the corresponding original elements in the original element set based on the state factors of the power battery; obtaining the optimal action table of the original elements to match the optimal action; and confirming the optimal matching calibrator of the power battery's operating condition based on the optimal action.

[0055] It is understood that, in the embodiments of this application, an original element set can be constructed based on the effective data of the vehicle's power battery. According to the actual operating conditions corresponding to the state factors of the power battery, and following the principle of shortest spatial distance, the most matching original element is selected from the original element set to determine the optimal action. Based on the optimal action, the optimal matching calibration quantity for the power battery's operating conditions is confirmed. The elements in the table are obtained by matching the operating conditions closest to the original element set according to temperature and SOC, obtaining the corresponding numerical table from the original elements corresponding to that operating condition, and selecting the optimal action. For example, the vehicle-side calibration quantity can be simulated and calibrated in the cloud based on reinforcement learning, including but not limited to undervoltage and overvoltage thresholds, the power limiting coefficients corresponding to each level, the power filtering coefficients corresponding to each level, and the control coefficient β and compensation coefficient b of the power filtering coefficients.

[0056] Specifically, the process of constructing the original feature set is as follows: Figure 2 As shown, the data is first segmented according to time intervals, and limits are set for the proportion of outliers and missing data. When either ratio exceeds the limit, the current data interval is considered invalid; otherwise, the data is considered valid, outliers are removed, missing values ​​are sampled and filled, and the data is stored in the data warehouse. In reality, the cloud-based data warehouse will continuously grow as data collection continues. Considering actual storage capacity, a certain update principle can be configured, i.e., a maximum capacity for the data warehouse is set. When the capacity is full, newly collected data will overwrite the oldest data, ensuring that the cloud-stored data effectively reflects the true state of the vehicles at the most recent time.

[0057] Then, based on the fragmented data in the data warehouse, a second extraction is performed to obtain the data fragments that trigger undervoltage and overvoltage. The resulting fragments are combined to form the original elements D = (d1, d2, ..., d...) of the reinforcement learning interactive environment. N Given N>0, construct an interactive environment. The child elements d in the original element D... i This can be viewed as a data set composed of timing data such as individual voltage extreme values, current, lookup table power, power limiting factor, and SOC. An example is d. i ={time, SOC, T max T min v + v - Meanwhile, the original elements also contain all the initial calibration values ​​of the vehicle, so the cloud has the ability to backtrack the historical valid data of the vehicle.

[0058] Next, define the state space array S. n =(s1,s2,…,s n ) and action space array A m =(a1,a2,…,a m Construct an n x m Q array.di Value table, with the initial value of each element in the table being 0, Q di The elements in the value table represent the future reward expectations under corresponding states and actions. The state elements are an array including but not limited to the extreme values of single-cell voltage, the extreme values of single-cell temperature, and SOC. The action elements are an array composed of the vehicle-end to-be-standardized quantities, and the change range of each standardized quantity is pre-agreed. State space array S n The state in it is: s = {v + , v -}}. Discretize it. Exemplarily, it is discretized into 20 states. The lower bound of v + is V 1+ , and the upper bound is V 3+ . Similarly, the lower bound of v - is V 3- , and the upper bound is V 1- . Action space array A m The action a in it is:

[0059]

[0060] Among them, the decision interval of V i+ or V i- is both (V 3- , V 3+ ), but it needs to satisfy that when i < j, V i+ < V j+ and V i- > V j- ; LimFact i+ and LimFact i- The decision interval is (0, 1), but it needs to satisfy and and The decision range of is is the maximum filtering rate that can be achieved considering smoothness; considering the executability of action selection, the action a needs to be discretized. Exemplarily, it is discretized into 20 actions, and the Q di table is established, which is composed of 20 states and 20 actions, and each of its elements is assigned 0 at the initial moment.

[0061] The initial state s1 where the agent is located is determined by the initial value of d s i in D, which represents the current state of the power battery. The agent randomly samples an action a i with probability in the current state s i and executes it to interact with the environment, obtaining the transition state s', calculating the corresponding reward value R(s i , a i ), and updating Q di (s ia i The reward value is calculated using the following formula:

[0062]

[0063] Where η1 is the voltage coefficient, Δv i Characterization in state s i Perform action a i Subsequently, the degree to which the voltage approaches the normal range is determined by η2, which is the voltage threshold coefficient. To perform action a i The distance between the voltage threshold and the initial value, η3 is the power coefficient, ΔLimFact i To perform action a i The distance between the power limiting factor and the initial value, η4 is the filter coefficient, Δf i p To perform action a i The distance between the subsequent filter coefficients and the initial values. The Q value is updated using the following formula:

[0064] Q k+1 (s i ,a i )=Q k (s i ,a i )+lr*[R(s i ,a i )+γ*max a′ Q(s′,a′)-Q k (s i ,a i )],

[0065] Where lr is the learning rate, γ is the discount rate, and max a′ Q(s′,a′) represents the expected maximum reward for state s′. Obtaining the transition state s′: In state s... i An action is selected using an ε-greedy strategy, meaning an action is randomly selected from the action set with probability ε, and the action with the largest Q value is selected with probability 1-ε. The action is then executed and the state s′ is obtained through interaction with the environment. i Match the closest state in the state set according to the principle of proximity, and treat it as the transition state s'. Repeat this process until the original element d is completed. i .

[0066] Finally, repeat the above steps until all elements in the original element D are completely covered. Based on the Q-value table corresponding to each element in D, reconstruct the overall Q-value table of the state space array S and the action space array A over a large range. Based on the principle of optimal Q-value, obtain the values ​​of the parameters to be calibrated on the vehicle side.

[0067] This application embodiment can match corresponding original elements in the original element set based on the state factors of the power battery, obtain the optimal action table of the original elements to match the optimal action, and confirm the optimal matching calibration value of the power battery's operating condition based on the optimal action. Through the collaboration between the vehicle and the cloud, operating data is collected to build a learning environment, and the intelligent agent mines the optimal calibration value from the environment and pushes it to the vehicle, thereby effectively avoiding the mismatch phenomenon of calibration value as the battery operates, and further improving the accuracy of power battery power control.

[0068] Optionally, in one embodiment of this application, before receiving the optimal matching calibration value generated by the server based on valid data, the method further includes: determining whether the optimal matching calibration value meets a preset valid condition; if the optimal matching calibration value meets the preset valid condition, controlling the vehicle to store the optimal matching calibration value to update the vehicle's current vehicle-side calibration value.

[0069] It is understood that, in the embodiments of this application, the validity of data between the vehicle and the cloud can be determined based on preset valid conditions. In actual execution, a fixed invalid value can be set when the cloud has no calibrated quantity update task. After the vehicle receives valid values ​​for N consecutive cycles, it determines that the data is valid, stores the calibrated quantity sent by the cloud, and updates the corresponding calibrated quantity on the vehicle when the power is off.

[0070] It should be noted that the preset effective conditions can be set by those skilled in the art according to the actual situation, and no specific limitations are made here.

[0071] For example, it can be agreed that when a cloud-based task updates without a specified quantitative value, a fixed invalid value will be issued: {0, L, 0}. 1×14 The vehicle-side system judges the data sent from the cloud according to the action decision space. If the sent data is within the effective decision space for 5 consecutive cycles, the sent data is deemed valid, the cloud-sent calibration quantity is stored, and the corresponding calibration quantity on the vehicle-side system is updated when the vehicle is powered off.

[0072] The embodiments of this application can determine whether the optimal matching calibration value under working conditions meets the preset valid conditions, and when the optimal matching calibration value under working conditions meets the preset valid conditions, control the vehicle to store the optimal matching calibration value under working conditions in order to update the current vehicle-side calibration value, thereby further ensuring the effectiveness and reliability of vehicle-cloud data communication and further maintaining the stable control of power battery power.

[0073] In step S102, a preset power limiting range is obtained based on the current vehicle-side calibration value, and the power limiting coefficient and power filtering rate of the power battery are obtained according to the extreme values ​​of the single cell voltage of the power battery.

[0074] It is understood that, in the embodiments of this application, the voltage space between two adjacent voltage thresholds can be defined as the power limiting range, so as to obtain the power limiting coefficient and power filtering rate of the power battery by calculation under different power scenarios based on the extreme values ​​of the individual voltages of the power battery.

[0075] It should be noted that the preset power limit range can be set by those skilled in the art according to the actual situation, and no specific limitation is made here.

[0076] In actual operation, the vehicle can periodically acquire and upload data such as the current temperature, state of charge, and extreme values ​​of individual cell voltages of the battery to the cloud. It is agreed that temperature data is collected on a per-cell basis, and the highest and lowest individual cell temperatures are obtained. The state of charge is collected on a per-cell basis, and its sampling period is consistent with the temperature data collection period. The extreme values ​​of individual cell voltages are collected on a per-cell basis, and the highest and lowest individual cell voltages are obtained. The sampling period is consistent with the temperature data collection period. The data transmission frequency is pre-agreed between the vehicle and the cloud, and the data uploaded by the vehicle and the data sent by the cloud are carried out at the agreed frequency.

[0077] Optionally, in one embodiment of this application, obtaining the power limiting coefficient and power filtering rate of the power battery based on the extreme values ​​of the individual cell voltages includes: substituting the extreme values ​​of the individual cell voltages into a preset power limiting coefficient mapping model and a preset power filtering rate mapping model, and outputting the power limiting coefficient and power filtering rate.

[0078] Specifically, the voltage space between two adjacent voltage threshold levels is defined as the power limiting effective range. The power limiting coefficient mapping model and the power filtering rate mapping model are activated only when the extreme value of a single-unit voltage falls within this range. Let the two threshold levels be V... thdi and V thdj The threshold voltage interval is V b The current extreme value of the single-cell voltage v k The extreme value of the single-unit voltage at the previous moment was v. k-1 The power limitation factor mapping model is as follows:

[0079]

[0080] The power filter rate mapping model is as follows:

[0081]

[0082] Among them, |v k -V thdj | Represents the distance between the current voltage and the next voltage threshold. In overvoltage scenarios, V thdi ≤v k ≤V thdj In under-voltage scenarios, Vthdj ≤v k ≤V thdi Therefore |v k -V thdj |∈[0,V b ]; β is the control coefficient of the function, which controls the mapping characteristics of the function; b is the compensation coefficient of the function, defined as b∈(0,1]; LimFact thdi and LimFact thdj Voltage threshold V thdi and V thdj The corresponding power limiting factor; These are the voltage thresholds V thdi and V thdj The corresponding power filtering rate; the selection of ± depends on the power usage scenario.

[0083] In power feedback scenarios, the voltage of individual battery cells rises, and the extreme value of the individual cell voltage is selected as the maximum value, with a voltage threshold V. thdi <V thdj By comparing the maximum voltage value of a single cell with the voltage threshold, the power limiting factor and power filtering rate at time k are determined: Maximum voltage value of a single cell v k ∈(V thdi V thdj The allowable power is calculated according to the power limitation coefficient mapping model and the power filtering rate mapping model; the maximum voltage of a single unit is v. k ≤V thdi The power limiting factor and power filtering rate are determined by the calculation results of the previous interval. If the previous interval is a normal interval, the power limiting factor is set to 1, and the power filtering rate is set to the normal filtering rate; the maximum voltage of a single unit is v. k >V thdi The power limiting factor and power filtering rate are determined by the calculation results of the next interval.

[0084] In power output scenarios, the voltage of individual battery cells drops, and the extreme value of the individual cell voltage is selected as the minimum value, with a voltage threshold V. thdi >V thdj By comparing the minimum voltage value of a single cell with the voltage threshold, the power limiting factor and power filtering rate at time k are determined: Minimum voltage value v k ∈[V thdj V thdi The allowable power is calculated according to the power limitation coefficient mapping model and the power filter rate mapping model; the minimum value of the single-cell voltage v k ≥V thdi The power limiting factor and power filtering rate are determined by the calculation results of the previous interval. If the previous interval is a normal interval, the power limiting factor is set to 1, and the power filtering rate is set to the normal filtering rate; the minimum value of the single-cell voltage v k <Vthdj The power limiting factor and power filtering rate are determined by the calculation results of the next interval.

[0085] Among them, the power limiting coefficient mapping model realizes the automatic updating of the power limiting coefficient with the severity of overvoltage / undervoltage, and the change of the power limiting coefficient accelerates as the severity progresses to the later stage, effectively avoiding power over-limit in the early stage and lack of power limiting capability in the later stage, meeting the normal power use of electric vehicles, and avoiding abnormal battery overcharging and over-discharging. The power filtering rate mapping model considers the voltage changes at adjacent moments and the overall voltage changes within the interval, so that the power filtering speed can be updated in real time based on the changing trend of the voltage extreme value of the power battery cell. It can realize rapid power control when the voltage is rapidly approaching the threshold, keeping the battery's operating voltage within a safe range.

[0086] It should be noted that the preset power limiting coefficient mapping model and the preset power filtering rate mapping model can be set by those skilled in the art according to the actual situation, and no specific limitations are made here.

[0087] For example, such as Figure 3 As shown, the current voltage extreme value When all parameters are in region 3, the power limiting factor is taken as LimFact, and the power filtering rate is taken as f. p Model characteristics such as Figure 4 and Figure 5 As shown, The power constraint factor LimFact at time k, falling into region 2. k Calculate using the following formula:

[0088]

[0089] The power filtering rate at time k is:

[0090]

[0091] The allowable power at time k is:

[0092]

[0093] Among them, |v k –V 2+ | represents the distance between the current voltage and the next voltage threshold; β is the control coefficient of the function, which controls the mapping characteristics of the function; b is the compensation coefficient of the function, defined as b∈(0,1]; Let be the peak allowable power at time k.

[0094] The embodiments of this application can substitute the extreme values ​​of the single cell voltage into the preset power limit coefficient mapping model and the preset power filter rate mapping model, and output the power limit coefficient and the power filter rate, thereby comprehensively improving the calculation level of power battery related data, so as to improve the timeliness and accuracy of power battery power control and improve the safety management level of the vehicle.

[0095] Optionally, in one embodiment of this application, before obtaining the power limiting coefficient and power filtering rate of the power battery based on the extreme values ​​of the individual cell voltages of the power battery, the method further includes: obtaining initial values ​​of the power limiting coefficient and power filtering rate corresponding to each stage based on historical data of the power battery.

[0096] In some embodiments, the undervoltage and overvoltage thresholds at the vehicle end can be determined based on the cell-level cutoff voltage. Multiple initial values ​​are set at certain voltage intervals, and the power limiting coefficient and power filtering rate corresponding to each level can initially be assigned a conservative value based on past experience. Figure 3 As shown, the initial value assigned can be matched and calibrated later by the vehicle's current vehicle-side calibration value.

[0097] In this embodiment, before obtaining the power limiting coefficient and power filtering rate of the power battery based on the extreme values ​​of the individual cell voltages, the initial values ​​of the power limiting coefficient and power filtering rate for each stage can be obtained based on the historical data of the power battery, so as to further update the corresponding data according to the updated calibration values ​​on the vehicle side.

[0098] In step S103, the actual allowable power of the power battery is calculated based on the power limiting coefficient, the power filtering rate, and the current peak power table of the vehicle's charging and discharging process, so as to control the power output of the power battery.

[0099] In actual implementation, the current peak power of the vehicle's power battery during the charging and discharging process can be obtained, and the allowable power can be calculated based on the power limitation coefficient and power filtering rate obtained in the above steps, as follows:

[0100]

[0101] Among them, LimFact k This is the power limiting factor. For power filtering rate, Let be the peak allowable power at time k, and the selection of ± depends on the power usage scenario. The actual allowable power of the power battery is calculated based on the updated calibration value at the vehicle end, thereby realizing the control of the power battery power output.

[0102] Optionally, in one embodiment of this application, before calculating the actual allowable power of the power battery, the method further includes: acquiring the charge and discharge data of the power battery; updating the peak power table of the vehicle based on the charge and discharge data to obtain the current peak power table, and sending the current peak power table to the server.

[0103] It is understood that, in the embodiments of this application, the peak power meter can use battery temperature (Temp) and state of charge (SOC) as index variables. The BMS (Battery Management System) policies on the vehicle and cloud sides need to be synchronized; that is, the policies configured on the vehicle side need to be synchronized with the cloud side configuration, and the data involved in the vehicle-cloud policies must be consistent to achieve synchronized updates of the peak power meter on both the vehicle and cloud sides.

[0104] In actual operation, the peak power of the charging and discharging process can be measured according to the national standard power measurement method. The peak power meter can be classified according to the duration of the test pulse, such as 10s / 30s / 60s peak power meters.

[0105] This application embodiment can obtain the charging and discharging data of the power battery, update the peak power table of the vehicle based on the charging and discharging data, obtain the current peak power table, and send the current peak power table to the server to obtain the data basis required to calculate the allowable power of the power battery.

[0106] like Figure 6 As shown below, the working content of the embodiment of this application will be described in detail with a specific example.

[0107] Step S601: Obtain the peak power meter during the charging and discharging process and synchronize the vehicle-to-cloud BMS strategy.

[0108] Specifically, the BMS policies on the vehicle and in the cloud need to be synchronized; that is, the policies configured on the vehicle need to be synchronized with the configurations in the cloud, and the data involved in the vehicle-cloud policies must be consistent. The peak power meter uses battery temperature and state of charge as index variables.

[0109] Step S602: Initial values ​​for undervoltage and overvoltage thresholds, power limiting coefficient, and power filtering rate are given at the vehicle end.

[0110] Specifically, the undervoltage and overvoltage thresholds at the vehicle end are determined based on the cell-level cutoff voltage. The power limiting coefficient and power filtering rate corresponding to each level can initially be set to a conservative value based on past experience, and can be matched and calibrated in the cloud later.

[0111] Step S603: Obtain the power limitation coefficient and power filtering rate at the vehicle end, and calculate the allowable power.

[0112] Specifically, the current temperature data, state of charge, and extreme values ​​of individual cell voltages of the battery can be acquired periodically. Based on the current battery temperature and state of charge, the current maximum available power is calculated from the peak power table, which includes the maximum available discharge power and the maximum available recharge power. Based on the relationship between the current extreme values ​​of individual cell voltages and the voltage thresholds at each level, the current power limitation coefficient is obtained through the power limitation coefficient mapping model, and the current power filtering rate is obtained through the power filtering mapping model to calculate the allowable power.

[0113] Step S604: The cloud preprocesses the data uploaded by the vehicle terminal to obtain the optimal matching calibration quantity for the working conditions.

[0114] Specifically, cloud-based data preprocessing is performed and stored in a data warehouse. Based on the fragmented data in the data warehouse, secondary extraction is conducted to obtain data fragments that trigger undervoltage and overvoltage. These fragments are then combined to form the original elements D = (d1, d2, ..., d...) of the reinforcement learning interactive environment. N Given N>0, construct an interactive environment; define a state space array S. n =(s1,s2,…,s n ) and action space array A m =(a1,a2,…,a m Construct an n x m Q array. di A value table, with each element initially set to 0, Q. di The elements in the value table represent the expected future rewards under the corresponding state and action; the initial state s1 of the agent is determined by d in D. i The initial value determines the current state of the power battery; the agent in the current state s i A probabilistic random sampling selects an action a. i Execute and interact with the environment to obtain the transition state s', and calculate the corresponding reward value R(s). i a i ), Update Q di (s i a i The value continues until the original element d is fully executed. i Continue until all elements in the original element D are completely covered; based on the Q-value table corresponding to each element in D, reconstruct the overall Q-value table of the state space array S and the action space array A over a large range, and obtain the values ​​of the vehicle-side parameters to be calibrated based on the principle of optimal Q-value;

[0115] Step S605: Data is sent from the cloud, and the vehicle terminal judges the validity of the data and updates the calibration value in a timely manner.

[0116] Specifically, it is agreed that when there is no quantitative update task in the cloud, a fixed invalid value will be issued: {0,L,0}. 1×14The vehicle-side system judges the data sent from the cloud according to the action decision space. If the sent data is within the valid decision space for 5 consecutive cycles, the sent data is deemed valid, the cloud-sent calibration quantity is stored, and the corresponding calibration quantity on the vehicle-side system is updated when the power is off.

[0117] The power control method for a power battery proposed in this application can control the power battery power based on the actual condition of the vehicle's power battery. It adjusts the battery power limiting coefficient and power filtering rate in real time according to the optimal matching calibration value, achieving flexible adjustment of battery power, enhancing the protection effect of the vehicle's power battery, ensuring the safe and stable operation of the vehicle, and improving the user experience. This solves the problems in related technologies that fail to consider the exponential decay and linear characteristics of the power limiting coefficient, resulting in insufficient power limiting capability after the single-cell voltage drops and an inability to adapt to the limiting requirements of different ranges. Furthermore, the power limiting coefficient corresponding to the secondary voltage threshold cannot be directly calibrated due to the influence of the basic power limiting coefficient and the equal fraction of the range, thus narrowing the application scope of battery power management in different scenarios, failing to comprehensively achieve safety protection for the power battery, and making it difficult to effectively prevent power interruption in the vehicle, thereby reducing driving safety and reliability.

[0118] Next, the power control device for a power battery according to an embodiment of this application is described with reference to the accompanying drawings.

[0119] Figure 7 This is a schematic diagram of the power control device for a power battery according to an embodiment of this application.

[0120] like Figure 7 As shown, the power control device 10 of the power battery includes: an update module 100, an acquisition module 200, and a control module 300.

[0121] The update module 100 is used to send valid data of the vehicle's power battery to the server, receive the optimal matching calibration value generated by the server based on the valid data, and update the current vehicle-side calibration value of the vehicle according to the optimal matching calibration value.

[0122] The acquisition module 200 is used to acquire a preset power limit range based on the current vehicle-side calibration value, and to acquire the power limit coefficient and power filtering rate of the power battery according to the extreme value of the single cell voltage of the power battery.

[0123] The control module 300 is used to calculate the actual allowable power of the power battery based on the power limiting factor, the power filtering rate, and the current peak power table of the vehicle's charging and discharging process, so as to control the power output of the power battery.

[0124] Optionally, in one embodiment of this application, the control module 300 further includes a first acquisition unit and a transmission unit.

[0125] The first acquisition unit is used to acquire the charge and discharge data of the power battery before calculating the actual allowable power of the power battery.

[0126] The transmission unit is used to update the vehicle's peak power table based on the charging and discharging data, obtain the current peak power table, and send the current peak power table to the server.

[0127] Optionally, in one embodiment of this application, the acquisition module 200 includes an output unit.

[0128] The output unit is used to substitute the extreme values ​​of the individual voltages into the preset power limit coefficient mapping model and the preset power filter rate mapping model, and output the power limit coefficient and the power filter rate.

[0129] Optionally, in one embodiment of this application, the update module 100 includes an update unit.

[0130] The update unit is used to extract the actual state factor of the power battery based on the effective data of the vehicle's power battery before receiving the optimal matching calibration value of the power battery in the optimal action table based on the actual state factor.

[0131] Optionally, in one embodiment of this application, the updating unit is further configured to: match the corresponding original elements in the original element set based on the state factor of the power battery; obtain the optimal action table of the original elements to match the optimal action; and confirm the optimal matching calibration value of the power battery's operating condition based on the optimal action.

[0132] Optionally, in one embodiment of this application, the update module 100 further includes a judgment unit and a storage unit.

[0133] The judgment unit is used to determine whether the optimal matching calibration of the working condition meets the preset valid conditions before receiving the optimal matching calibration value generated by the server based on valid data.

[0134] The storage unit is used to control the vehicle to store the optimal matching calibration value under operating conditions when the optimal matching calibration value meets the preset valid conditions, so as to update the current vehicle-side calibration value.

[0135] Optionally, in one embodiment of this application, the acquisition module 200 further includes a second acquisition unit.

[0136] The second acquisition unit is used to acquire initial values ​​of the power limiting coefficient and power filtering rate corresponding to each stage based on the historical data of the power battery before acquiring the power limiting coefficient and power filtering rate of the power battery according to the extreme values ​​of the single cell voltage of the power battery.

[0137] It should be noted that the foregoing explanation of the power control method embodiment for the power battery also applies to the power control device for the power battery in this embodiment, and will not be repeated here.

[0138] The power control device for a power battery proposed in this application can control the power battery power based on the actual condition of the vehicle's power battery. It adjusts the battery power limiting coefficient and power filtering rate in real time according to the optimal matching calibration value, achieving flexible adjustment of battery power, enhancing the protection effect of the vehicle's power battery, ensuring the safe and stable operation of the vehicle, and improving the user experience. This solves the problems in related technologies that fail to consider the exponential decay and linear characteristics of the power limiting coefficient, resulting in insufficient power limiting capability after the single-cell voltage drops and an inability to adapt to the limiting requirements of different ranges. Furthermore, the power limiting coefficient corresponding to the secondary voltage threshold cannot be directly calibrated due to the influence of the basic power limiting coefficient and the equal fraction of the range, thus narrowing the application scope of battery power management in different scenarios, failing to comprehensively achieve safety protection for the power battery, and making it difficult to effectively prevent power interruption in the vehicle, thereby reducing driving safety and reliability.

[0139] Figure 8 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0140] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0141] When the processor 802 executes the program, it implements the power control method for the power battery provided in the above embodiments.

[0142] Furthermore, the vehicle also includes:

[0143] Communication interface 803 is used for communication between memory 801 and processor 802.

[0144] The memory 801 is used to store computer programs that can run on the processor 802.

[0145] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0146] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0147] Alternatively, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0148] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0149] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power control method for the power battery described above.

[0150] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0151] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0152] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0154] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0155] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0156] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0157] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A power control method for a power battery, characterized in that, Applied to vehicles, the method includes the following steps: Send valid data of the vehicle's power battery to the server, and receive the optimal matching calibration value generated by the server based on the valid data, and update the current vehicle-side calibration value of the vehicle according to the optimal matching calibration value. Based on the current vehicle-side calibration data, a preset power limiting range is obtained; and based on the extreme values ​​of the individual cell voltages of the power battery, the power limiting coefficient and power filtering rate of the power battery are obtained; and Based on the power limiting factor, the power filtering rate, and the current peak power table of the vehicle's charging and discharging process, the actual allowable power of the power battery is calculated to control the power battery power output; Before receiving the optimal matching calibration value for the operating condition generated by the server based on the valid data, the process also includes: Based on the effective data of the vehicle's power battery, the actual state factor of the power battery is extracted, and the optimal matching calibration value of the power battery's working condition is matched in the optimal action table according to the actual state factor. Determine whether the optimal matching calibration value for the working condition meets the preset valid conditions; If the optimal matching calibration value for the operating condition meets the preset valid conditions, then control the vehicle to store the optimal matching calibration value for the operating condition and update the current vehicle-side calibration value of the vehicle.

2. The method according to claim 1, characterized in that, Before calculating the actual allowable power of the power battery, the following steps are also included: Obtain the charge and discharge data of the power battery; The peak power table of the vehicle is updated based on the charging and discharging data to obtain the current peak power table, and the current peak power table is sent to the server.

3. The method according to claim 1, characterized in that, The step of obtaining the power limiting coefficient and power filtering rate of the power battery based on the extreme values ​​of the individual cell voltages includes: Substitute the extreme values ​​of the individual voltages into a preset power limiting coefficient mapping model and a preset power filtering rate mapping model to output the power limiting coefficient and the power filtering rate.

4. The method according to claim 1, characterized in that, The step of matching the optimal operating condition calibration value of the power battery in the optimal action table according to the actual state factor includes: Based on the state factors of the power battery, the corresponding original elements are matched in the original element set; Obtain the optimal action table of the original elements to match the optimal action, and confirm the optimal matching calibration value of the power battery based on the optimal action.

5. The method according to claim 1, characterized in that, Before obtaining the power limiting coefficient and the power filtering rate of the power battery based on the extreme values ​​of the individual cell voltages of the power battery, the method further includes: Based on the historical data of the power battery, the initial values ​​of the power limiting coefficient and power filtering rate corresponding to each stage are obtained.

6. A power control device for a power battery, characterized in that, Applied to vehicles, wherein the device includes: The update module is used to send the vehicle's power battery valid data to the server, and receive the optimal matching calibration value generated by the server based on the valid data, and update the current vehicle-side calibration value of the vehicle according to the optimal matching calibration value. The acquisition module is used to obtain a preset power limiting range based on the current vehicle-side calibration of the vehicle, and to obtain the power limiting coefficient and power filtering rate of the power battery according to the extreme values ​​of the individual cell voltages of the power battery; and The control module is used to calculate the actual allowable power of the power battery based on the power limiting coefficient, the power filtering rate and the current peak power table of the vehicle's charging and discharging process, so as to control the power battery power output; Before receiving the optimal matching calibration value for the operating condition generated by the server based on the valid data, the process also includes: Based on the effective data of the vehicle's power battery, the actual state factor of the power battery is extracted, and the optimal matching calibration value of the power battery's working condition is matched in the optimal action table according to the actual state factor. Determine whether the optimal matching calibration value for the working condition meets the preset valid conditions; If the optimal matching calibration value for the operating condition meets the preset valid conditions, then control the vehicle to store the optimal matching calibration value for the operating condition and update the current vehicle-side calibration value of the vehicle.

7. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the power control method for a power battery as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the power control method for the power battery as described in any one of claims 1-5.

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