Battery multiplying power adjusting method and system based on joint calculation

Abstract

The invention discloses a battery rate adjusting method and system based on joint calculation, and the method comprises the steps: collecting real-time operation parameters of a battery, the real-time operation parameters comprising a real-time current, a real-time voltage observation value, a first electrochemical impedance spectrum, a first temperature stress and a first cycle index; obtaining an SOH value based on the first electrochemical impedance spectrum, the first cycle index and the first temperature stress; estimating based on the SOH value and the real-time current to obtain a first SOC value; correcting the first SOC value based on the real-time voltage observation value to obtain a second SOC value; and determining a first charge-discharge rate according to the SOH value and the second SOC value, and controlling the charge-discharge current of the battery according to the first charge-discharge rate. According to the method, the problems that SOC estimation is not accurate, SOH evaluation lacks dynamics, and rate control is single are solved, and intelligent and self-adaptive adjustment of charge and discharge rates of the battery in different operation states is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a battery rate adjustment method and system based on joint calculation. BACKGROUND

[0002] In the existing battery management system, the estimation of SOC (State of Charge) and SOH (State of Health) and the rate control have many deficiencies. The traditional SOC estimation method mostly uses the ampere-hour integration method, which depends on the long-term integration calculation of the current and is easily affected by the current measurement error, coulomb efficiency change and other factors to produce cumulative error. Especially in dynamic conditions such as acceleration and deceleration, frequent charging and discharging, the SOC estimation deviation is significant, and it is difficult to ensure the accuracy. The existing SOH estimation method is usually based on static cycle life test data, ignoring the influence of real-time temperature stress, instantaneous load fluctuation and internal resistance change and other dynamic factors on the battery aging speed in the running process, which leads to the SOH evaluation lagging behind the real health status of the battery, reducing its guiding role in the control strategy. In addition, the existing rate control strategy mostly uses fixed rate or only adjusts the rate according to the SOC, lacks comprehensive consideration of SOH and other running states, and cannot realize the optimal balance between power output and life maintenance under different working conditions, which easily leads to safety hazards such as overcharge, overdischarge or overheating. Although some schemes introduce the SOC value to participate in the rate adjustment, they do not make comprehensive decisions combined with SOH. Some schemes use SOH for life prediction, but do not combine it with real-time rate control, which leads to the rate control unable to be dynamically optimized with the change of the battery health status.

[0003] Therefore, there is an urgent need for a real-time estimation method that can integrate SOC and SOH to solve the problems of inaccurate SOC estimation, SOH response lag and single rate control in the prior art. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application provides a battery rate adjustment method and system based on joint calculation, aiming to solve the problems of inaccurate SOC estimation, lack of dynamicity in SOH evaluation and single rate control in the prior art, realize intelligent and adaptive adjustment of the charging and discharging rate of the battery under different running states, and improve the comprehensive performance of the battery management system in power output, life maintenance and safety protection.

[0005] The present application discloses a battery rate adjustment method based on joint calculation, comprising: collecting real-time running parameters of the battery, the real-time running parameters including real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number; obtaining the SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress; obtaining the first SOC value based on the SOH value and the real-time current; correcting the first SOC value based on the real-time voltage observation value to obtain the second SOC value; determining the first charge-discharge rate according to the SOH value and the second SOC value, and controlling the charge-discharge current of the battery according to the first charge-discharge rate.

[0006] Optionally, the battery rate adjustment method based on joint calculation further comprises: obtaining the second operating parameter of the controlled battery, and updating the SOH value based on the second operating parameter.

[0007] Optionally, the second operating parameter comprises at least one of the second electrochemical impedance spectrum, the second cycle number and the second temperature stress.

[0008] Optionally, obtaining the SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress comprises: performing frequency domain analysis on the first electrochemical impedance spectrum to obtain an impedance characteristic parameter of the battery; comparing the impedance characteristic parameter with a reference impedance parameter to obtain an internal resistance growth amount; determining a capacity attenuation ratio based on the first cycle number and the first temperature stress; weighting and fusing the internal resistance growth amount and the capacity attenuation ratio to obtain the SOH value.

[0009] Optionally, the battery rate adjustment method based on joint calculation further comprises: collecting the open circuit voltage of the battery, querying the SOC value corresponding to the open circuit voltage in the preset OCV-SOC curve, and taking the SOC value as an initial SOC value.

[0010] Optionally, obtaining the first SOC value based on the SOH value and the real-time current comprises: determining the current rated capacity of the battery based on the SOH value; performing integral operation on the real-time current by using the ampere-hour integral method, and determining a SOC change amount according to the integral operation result and the current rated capacity; updating the initial SOC value according to the SOC change amount to obtain the first SOC value.

[0011] Optionally, correcting the first SOC value based on the real-time voltage observation value to obtain the second SOC value comprises: determining a state variable according to the first SOC value and the real-time voltage observation value; obtaining an end voltage observation value, and determining a deviation value based on the end voltage observation value and the real-time voltage observation value. Update the initial cosquare matrix based on the state variables, and determine the Kalman gain according to the updated result; The first SOC value is corrected according to the deviation value and the Kalman gain to obtain a second SOC value.

[0012] Optionally, determining the first charge-discharge rate according to the SOH value and the second SOC value includes: The SOH value and the second SOC value are used as input parameters, the charge and discharge rate corresponding to the input parameters is searched in a preset rate mapping table, and the charge and discharge rate is used as the first charge and discharge rate.

[0013] Optionally, controlling the charge and discharge current of the battery according to the first charge and discharge rate includes: The SOH value, the second SOC value, the first temperature stress and the historical load are used as state space data; Use the preset adjustment ratio as action space data; Determine the reward function value based on the state space data and the action space data; The first charge and discharge rate is optimized based on the reward function value to obtain a second charge and discharge rate, so as to control the charge and discharge current according to the second charge and discharge rate.

[0014] The present invention also discloses a battery rate adjustment system based on joint calculation, which is used to execute the above-mentioned battery rate adjustment method based on joint calculation. The battery rate adjustment system based on joint calculation includes: The acquisition module is configured to: acquire real-time operating parameters of the battery, the real-time operating parameters including real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number; A first optimization module is configured to obtain a SOH value based on a first electrochemical impedance spectrum, a first cycle number, and a first temperature stress; The joint optimization module is configured to: estimate based on the SOH value and the real-time current to obtain a first SOC value; The second optimization module is configured to: correct the first SOC value based on the real-time voltage observation value to obtain a second SOC value; The intelligent regulation module is configured to determine a first charge and discharge rate according to the SOH value and the second SOC value, and control the charge and discharge current of the battery according to the first charge and discharge rate.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By collecting multi-dimensional real-time operating parameters, it is possible to comprehensively obtain basic data reflecting the current electrochemical characteristics, operating conditions and life status of the battery, providing reliable input for the accurate joint estimation of SOC and SOH, and avoiding estimation bias caused by a single data source.

[0016] 2. Obtain the SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress, and take the dynamic factors in the real-time working condition into the SOH evaluation, so that the SOH value can truly reflect the current health status of the battery, and the evaluation lag problem caused by the traditional method of relying only on static data is avoided.

[0017] 3. The first SOC value is obtained by estimating based on the SOH value and the real-time current, and the second SOC value is obtained by correcting the real-time voltage observation value, a SOC estimation mechanism of multi-algorithm fusion is introduced, the error accumulation problem of the ampere-hour integral method can be inhibited under the dynamic working condition, and the SOC estimation accuracy is improved combined with the voltage correction, and stable and reliable state of charge information is provided for subsequent rate decision.

[0018] 4. The first charging and discharging rate is determined according to the SOH value and the second SOC value, the comprehensive consideration of the battery capacity state and the health state by the rate decision is realized, the one-sidedness of relying only on SOC or only on SOH is avoided, the battery service life can be effectively prolonged while ensuring the power output, and the overcharge, overdischarge and overheating risks caused by unreasonable rate setting are reduced.

[0019] 5. The charging and discharging current of the battery is controlled according to the first charging and discharging rate, so that the charging and discharging process meets the current bearing capacity of the battery, and dynamic adjustment can be realized combined with the battery running state, the safe operation and performance stability of the battery under different working conditions are ensured, and a closed-loop management from state perception, rate decision to execution control is formed, and the intelligent level and response speed of the whole system are further improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of the battery rate regulation method based on joint calculation provided by the present application is shown in the figure. Figure 2 Another flowchart of the battery rate regulation method based on joint calculation provided by the present application is shown in the figure. Figure 3 A structural diagram of the battery rate regulation system based on joint calculation provided by the present application is shown in the figure. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0022] The application will be further described in detail below with reference to the drawings.

[0023] As Figure 1 shown, the embodiment of the application provides a battery rate adjustment method based on joint calculation, comprising the following steps: Step S1, collecting real-time running parameters of the battery, the real-time running parameters including real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number.

[0024] In the embodiment of the application, the real-time running parameters are the basis for subsequent joint estimation of SOC and SOH and dynamic rate control, and accurate and comprehensive collection of these parameters provides data support for efficient operation of the entire battery management system. The real-time running parameters include real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number.

[0025] The real-time current refers to the instantaneous current value of the battery during the charging and discharging process, and its value rule is that the current is positive during discharging and negative during charging. The real-time current is obtained by real-time detection of a current sensor, and the sampling frequency should be no less than 1 Hz during the collection process to ensure that the rapid changes of the current can be captured in time under dynamic conditions such as acceleration and deceleration, frequent charging and discharging, etc., so as to provide continuous and accurate current data for the integral calculation in the subsequent SOC estimation. The real-time voltage observation value refers to the observation value of the terminal voltage of the battery, i.e., the voltage between the two terminals of the battery during the charging and discharging process or when the battery is at rest. This value is obtained by real-time collection of a voltage sensor, which reflects the voltage characteristics of the battery under the current state and will be used for subsequent correction of the SOC value based on the extended Kalman filter (EKF) to improve the accuracy of the SOC estimation. The first electrochemical impedance spectrum refers to the impedance characteristic data of the battery obtained by electrochemical impedance spectrum (EIS) test, which reflects the electrochemical characteristics of the battery, especially the changes of the internal resistance. The first electrochemical impedance spectrum is collected in real time or periodically by a dedicated impedance test module to provide basic data for the internal resistance growth model in the SOH evaluation, helping to accurately judge the changes of the health status of the battery. The first temperature stress refers to the real-time temperature of the battery during operation, which directly affects the chemical reaction rate and aging speed of the battery. The first temperature stress is collected in real time by a temperature sensor at key positions of the battery, such as the surface of the battery cell and the inside of the battery pack, and will be used as an important input parameter for the capacity attenuation model in the SOH evaluation to reflect the dynamic influence of temperature on the aging of the battery. The first cycle number refers to the cumulative number of complete charging and discharging cycles of the battery from full charge to discharge to the cut-off voltage and then to full charge. The first cycle number is recorded and updated in real time by the battery management system: each time the battery completes a complete charging and discharging process, i.e., from a higher SOC state to a lower SOC state and then to a higher SOC state, the cycle number is automatically increased by 1. This parameter will be used in the capacity attenuation model in the SOH evaluation to quantify the influence of cycle use on the capacity of the battery.

[0026] In summary, by collecting real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number, the real-time running state and historical use of the battery can be comprehensively reflected, providing reliable raw data support for the subsequent joint estimation of SOC and SOH based on these parameters and the development of dynamic rate adjustment strategies, ensuring that the entire battery intelligent rate regulation system can accurately and dynamically respond to changes in the state of the battery.

[0027] Step S2, obtaining an SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress.

[0028] In an embodiment of the present invention, the SOH value is used to characterize the current health of the battery. Its accurate acquisition is the key to achieving a balance between battery performance and life. By integrating the internal resistance change reflected by the first electrochemical impedance spectrum, the usage intensity reflected by the first cycle number, and the aging effect caused by the first temperature stress, the SOH value can be made more in line with the actual state of the battery.

[0029] It should be noted that the process of obtaining the SOH value based on the first electrochemical impedance spectrum, the first number of cycles, and the first temperature stress is mainly achieved through the joint calculation of the internal resistance growth model and the capacity decay model. The two models evaluate battery aging from the perspectives of internal resistance change and capacity decay, respectively, and finally the SOH value is obtained by combining the results of the two. The internal resistance growth model is constructed based on the first electrochemical impedance spectrum (i.e., electrochemical impedance spectroscopy EIS data) to reflect the growth of battery internal resistance with aging. The first electrochemical impedance spectrum contains the impedance characteristics of the battery at different frequencies, from which the real-time internal resistance parameters can be extracted. The internal resistance growth model calculates the internal resistance decay ratio by comparing the ratio of the current real-time internal resistance to the initial internal resistance of the battery. The larger the ratio, the more severe the battery aging and the lower the SOH value.

[0030] For example, if the initial internal resistance of the battery is R0, and the current real-time internal resistance obtained based on the first electrochemical impedance spectroscopy is Rt, the internal resistance decay ratio can be expressed as (Rt - R0) / R0, which will serve as an important input for SOH calculation.

[0031] It should be noted that the capacity decay model is constructed based on the first cycle number and the first temperature stress, and is used to quantify the decay of battery capacity with use. The calculation formula of the capacity decay model is as follows: = * (1 -β* ), in, is the current rated capacity, is the initial rated capacity of the battery, is the first cycle number, and β is the attenuation coefficient. The attenuation coefficient β is obtained through experimental calibration and dynamically adjusts with the first temperature stress. The higher the temperature stress, the larger the β value, reflecting the accelerated effect of high temperature on capacity decay.

[0032] The ratio of the current rated capacity to the initial rated capacity is calculated by this formula ( / ), or the capacity decay ratio. A smaller ratio indicates more severe battery aging and a lower SOH value. The SOH value is ultimately determined by combining the internal resistance decay ratio and the capacity retention ratio. In actual calculations, these two ratios can be assigned preset weights based on battery type and usage scenario, and the SOH value is calculated through weighted calculation.

[0033] Exemplarily, the SOH value is obtained based on the first electrochemical impedance spectrum, the first number of cycles and the first temperature stress, including: performing frequency domain analysis on the first electrochemical impedance spectrum to obtain impedance characteristic parameters of the battery; comparing the impedance characteristic parameters with the reference impedance parameters to obtain the internal resistance growth; determining the capacity attenuation ratio based on the first number of cycles and the first temperature stress; and weightedly fusing the internal resistance growth and the capacity attenuation ratio to obtain the SOH value.

[0034] In summary, internal resistance data is obtained through the first electrochemical impedance spectroscopy to evaluate internal resistance growth, capacity decay is calculated through the capacity decay model in combination with the first cycle number and the first temperature stress, and the SOH value is obtained by combining the two results. This process fully considers the real-time operating conditions, such as the internal resistance changes caused by temperature and load, and the impact on battery aging, so that the SOH value can accurately reflect the current health status of the battery and provide a reliable basis for subsequent charge and discharge rate adjustments based on the SOH value.

[0035] Step S3: Estimation is performed based on the SOH value and the real-time current to obtain a first SOC value.

[0036] In an embodiment of the present invention, the first SOC value is a key parameter reflecting the current remaining power of the battery. By combining the dynamic correction of the battery capacity with the SOH value and the integral calculation of the real-time current, the accuracy of the SOC estimation can be improved, providing a reliable basis for subsequent correction steps.

[0037] It should be noted that the process of obtaining the first SOC value based on the SOH value and real-time current mainly adopts the ampere-hour integration method, and dynamically updates the battery rated capacity in combination with the SOH value to eliminate the impact of battery aging on the capacity. Specifically, it includes initialization, real-time current acquisition, integral calculation and SOC update steps. The SOH value is used to dynamically update the current rated capacity of the battery. The initial rated capacity of the battery is a known parameter, and as the battery ages, the actual rated capacity will gradually decay. The calculation formula of the ampere-hour integration method is as follows: SOC(t) = +

[0038] in, is the initial SOC value, I(τ) is the real-time current, η is the Coulomb efficiency, η<1 during charging and η≈1 during discharging, and Qn is the current rated capacity after updating based on the SOH value.

[0039] The SOC change is calculated based on the following formula: , Here, ΔSOC is the change in SOC.

[0040] The formula for updating the initial SOC value according to the SOC change is as follows: = ΔSOC + SOC, + ΔSOC, wherein, is the first SOC value, is the initial SOC value.

[0041] The formula calculates the SOC change amount by integrating the real-time current, combined with the coulomb efficiency and the current rated capacity, and then updates the SOC value.

[0042] It should be noted that the specific processing process of the initial SOC value is as follows. First, make the battery in a static state, ensure the stability of the internal electrochemical state of the battery, and avoid the influence of dynamic current or voltage fluctuation on the open circuit voltage (OCV) measurement. Then, measure the open circuit voltage of the battery at this time. Next, call the OCV-SOC curve obtained by experiment in advance, which is fitted after measuring the OCV value corresponding to different SOC states of the battery, reflecting the corresponding relationship between the open circuit voltage and the state of charge of the battery. Finally, according to the measured current OCV value, the OCV-SOC curve is looked up and matched to determine the corresponding SOC value as the initial SOC value. In this way, the inherent correspondence between OCV and SOC can provide an accurate starting point for SOC estimation, effectively avoiding the cumulative effect of initial value error on subsequent integral calculation, especially in dynamic working conditions, which can improve the overall accuracy of SOC estimation and solve the problem of large initial value error in traditional ampere-hour integral method.

[0043] For example, the battery rate adjustment method based on joint calculation further includes: collecting the open circuit voltage of the battery, querying the SOC value corresponding to the open circuit voltage in the preset OCV-SOC curve, and taking the SOC value as the initial SOC value. Based on the SOH value and the real-time current, the first SOC value is estimated, including: determining the current rated capacity of the battery based on the SOH value; performing integral operation on the real-time current by using the ampere-hour integral method, and determining the SOC change amount according to the integral operation result and the current rated capacity; updating the initial SOC value according to the SOC change amount to obtain the first SOC value.

[0044] In summary, by dynamically updating the current rated capacity of the battery based on the SOH value, and combining the integral calculation of the real-time current by the ampere-hour integral method, the first SOC value can be obtained. This process fully considers the influence of the battery health state on the capacity, reduces the cumulative error caused by the capacity decay in the traditional ampere-hour integral method, and especially maintains high estimation accuracy after long-term use of the battery, laying a good foundation for the subsequent correction step based on the real-time voltage observation value.

[0045] Step S4, correcting the first SOC value based on the real-time voltage observation value to obtain the second SOC value.

[0046] In the embodiment of the present application, the second SOC value is an optimization of the first SOC value, by introducing real-time voltage observation and combining an extended Kalman filter (EKF) algorithm, the cumulative error of the first SOC value under dynamic working conditions can be effectively corrected, and the accuracy of SOC estimation is improved, providing a more reliable state basis for subsequent determination of charge and discharge rate.

[0047] It should be noted that the process of correcting the first SOC value based on real-time voltage observation mainly uses the EKF algorithm. This algorithm realizes dynamic correction of the SOC value by constructing a nonlinear battery model and combining the first SOC value (i.e., state prediction) and real-time voltage observation (i.e., measurement feedback). The specific process includes three core links: model construction, local linearization, and EKF process execution. The nonlinear battery model includes a state equation and an observation equation. The state equation is used to describe the change of the battery state with the input, as shown in the following formula: , wherein, is a state variable, = [SOC k , V RC,k ] T , is the first SOC value, is the polarization voltage, reflecting the internal electrochemical polarization effect of the battery, i.e., the terminal voltage observation. The input variable is = (real-time current), and the process noise obeys a Gaussian distribution, which is used to cover the interference factors not considered in the model, such as internal chemical reaction fluctuations.

[0048] The observation equation is used to associate the state variable with the measurement value, and the observation variable is shown in the following formula: , = , i.e., the real-time voltage observation, and the observation noise obeys a Gaussian distribution, which is related to the measurement accuracy of the voltage sensor and is used to represent the uncertainty of the observation value.

[0049] Local linearization is achieved by calculating the Jacobian matrix, which is used to linearize the nonlinear state equation and observation equation to meet the requirements of EKF for linear models. The Jacobian matrix reflects the local linear relationship between the state variable and the input and output, and is obtained by taking the partial derivative of the state equation and the observation equation at the current state estimation value, ensuring that EKF can still stably and accurately correct the state in a nonlinear system.

[0050] The EKF correction process includes an initialization step, a prediction step, and an update step. In the initialization step, an initial SOC value and an initial covariance matrix are set. The setting of the initial SOC value is as shown in the foregoing. The initial covariance matrix is set according to experience, such as = diag(0.01, 0.001), which is used to quantify the uncertainty of the initial state. In the prediction step, the current state and covariance are predicted based on the state at the last time and the current input. The state prediction adopts the ampere-hour integral method to calculate the SOC prediction value, that is, the first SOC value. The covariance prediction is calculated through the state transition matrix, the covariance matrix at the last time, and the process noise covariance matrix, and the formula is: , wherein, is the state transition matrix, Q is the process noise covariance matrix, which needs to be dynamically adjusted according to the working condition, is the covariance matrix at the last time.

[0051] The predicted state is corrected by using the real-time voltage observation value. First, the Kalman gain is calculated, and the formula is: , wherein, is the observation matrix, which is obtained from the Jacobian matrix, and R is the observation noise covariance, which is related to the precision of the voltage sensor.

[0052] The deviation between the predicted state and the voltage observation value is fused through the Kalman gain to obtain the corrected second SOC value, and the formula is: .

[0053] The covariance matrix is updated, and the formula is: .

[0054] For example, the first SOC value is corrected based on the real-time voltage observation value to obtain the second SOC value, including: determining a state variable according to the first SOC value and the real-time voltage observation value; obtaining an end voltage observation value, and determining a deviation value based on the end voltage observation value and the real-time voltage observation value; updating the initial covariance matrix based on the state variable, and determining the Kalman gain according to the update result; and correcting the first SOC value according to the deviation value and the Kalman gain to obtain the second SOC value.

[0055] In summary, this process makes full use of the real-time feedback of the voltage observation value, effectively suppresses the cumulative error of the traditional ampere-hour integral method, and can significantly improve the SOC estimation accuracy, especially in dynamic working conditions, thereby providing reliable state data for determining the charge and discharge rate based on the SOH value and the second SOC value.

[0056] Step S5, determining a first charge-discharge rate according to the SOH value and the second SOC value, and controlling the charge-discharge current of the battery according to the first charge-discharge rate.

[0057] In the embodiment of the present application, the process is the core link of connecting battery state estimation and actual operation control, and through dynamic matching of the charge-discharge rate and the real-time state of the battery, the optimal balance of power output and life maintenance is realized.

[0058] The process of determining the first charge-discharge rate according to the SOH value and the second SOC value adopts a double-layer strategy of "basic restriction + intelligent optimization", that is, first determining the safe boundary through the adaptive rate mapping table (i.e., the preset rate mapping table), and then dynamically adjusting within the boundary through the AI optimization module to finally obtain the second charge-discharge rate. The control of the charge-discharge current is realized based on the rate, and is continuously optimized in combination with closed-loop feedback.

[0059] It should be noted that the adaptive rate mapping table is used to determine the maximum allowed charge-discharge rate range. The mapping table is calibrated through experiments in advance, and stores the maximum charge-discharge rate corresponding to different combinations of SOH value and second SOC value. When the second SOC value is in an extreme range (such as too low or too high) or the SOH value is low, the mapping table will limit the maximum rate to avoid battery damage. When the second SOC value is in the middle interval and the SOH value is good, the mapping table allows a higher rate to meet the power demand. For example, if the second SOC value is < 20%, regardless of the SOH value, the mapping table will limit the maximum discharge rate to a low level, such as limiting the charge-discharge rate to below 1C. If the SOH value is < 80%, the overall maximum charge-discharge rate will be lower than that of a new battery. The mapping table provides a safe boundary for subsequent optimization, ensuring that the rate adjustment does not exceed the tolerance range of the battery.

[0060] The AI optimization module is used to dynamically adjust within the range of the adaptive rate mapping table to obtain the first charge-discharge rate. The module is based on a reinforcement learning algorithm, and its state space includes the SOH value, the second SOC value, the battery temperature, and the historical load, which reflects the past charge-discharge intensity. The action space is the preset adjustment rate of the charge-discharge rate, such as ±0.5C. The reward function comprehensively evaluates the adjustment effect, encouraging to meet the power demand with the actual output power (Pout) ), punishing overheating with the temperature rise (ΔT), and punishing excessive aging with the health degree decay (ΔSOH). The reward function is as follows: .

[0061] Through continuous learning, the AI optimization module can balance the instantaneous power demand and the long-term life loss under the premise of safety. For example, when a short-term high power demand is detected and the battery temperature is low and the SOH is good, the rate will be appropriately increased. When the temperature rise is too fast or the SOH decay is accelerated, the rate will be actively reduced.

[0062] According to the SOH value and the second SOC value, the first charging and discharging rate is determined, including: taking the SOH value and the second SOC value as input parameters, searching for the charging and discharging rate corresponding to the input parameters in a preset rate mapping table, and taking the charging and discharging rate as the first charging and discharging rate; wherein the preset rate mapping table includes a mapping relationship between the SOH value interval, the SOC value interval and the charging and discharging rate. The charging and discharging current of the battery is controlled according to the first charging and discharging rate, including: taking the SOH value, the second SOC value, the first temperature stress and the historical load as state space data; taking the preset adjustment rate as action space data; determining a reward function value based on the state space data and the action space data; optimizing the first charging and discharging rate based on the reward function value to obtain a second charging and discharging rate, so as to control the charging and discharging current according to the second charging and discharging rate.

[0063] Further, as shown in Figure 2 The battery rate adjustment method based on joint calculation provided by the application further includes: obtaining a second running parameter of the controlled battery, and updating the SOH value based on the second running parameter.

[0064] In the embodiment of the application, the second running parameter includes at least one of a second electrochemical impedance spectrum, a second temperature stress and a second cycle number.

[0065] After each charging and discharging is completed, the system updates the first cycle number, the first temperature stress and other parameters, and reevaluates the SOH value, forming a closed loop of "state estimation → dynamic control → feedback update → restate estimation". For example, after a high-rate discharge, the influence of the cycle on the SOH value is recorded, and the influence is referred to when the rate is determined subsequently, so as to avoid excessive loss under similar working conditions.

[0066] In summary, the first charging and discharging rate is determined through the combination of the adaptive rate mapping table and the AI optimization module, and the charging and discharging current is controlled based on the rate, and at the same time, the closed loop feedback is relied on for continuous adjustment. This process solves the limitations of the traditional fixed rate strategy and realizes the dynamic optimization of the charging and discharging rate with the battery state. It can not only meet the power demand under different working conditions, but also protect the battery to prolong the life, and finally realize the optimal balance between the battery performance and the life.

[0067] As shown in Figure 3 The embodiment of the application further provides a battery rate adjustment system based on joint calculation, which is used to execute the battery rate adjustment method based on joint calculation described above, and includes: a collection module 201, a first optimization module 202, a joint optimization module 203, a second optimization module 204 and an intelligent adjustment module 205.

[0068] The collection module 201 is configured to collect real-time operation parameters of the battery, and the real-time operation parameters include real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number.

[0069] The first optimization module 202 is configured to obtain an SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress. The joint optimization module 203 is configured to obtain a first SOC value based on the SOH value and the real-time current. The second optimization module 204 is configured to correct the first SOC value based on the real-time voltage observation value to obtain a second SOC value. The intelligent adjustment module 205 is configured to determine a first charge-discharge rate according to the SOH value and the second SOC value, and control the charge-discharge current of the battery according to the first charge-discharge rate.

[0070] According to the above technical solution, the application discloses a battery rate adjustment method and system based on joint calculation. The real-time operation parameters of the battery are collected, the SOH value is obtained based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress, the second SOC value is estimated and corrected in combination with the SOH value and the real-time current, the first charge-discharge rate is determined according to the SOH value and the second SOC value, and the charge-discharge current is controlled. The problems of inaccurate traditional SOC estimation, ignoring dynamic influence of SOH and single rate control are effectively solved. The joint accurate estimation of SOC and SOH is realized, the charge-discharge rate can be dynamically adjusted according to the state of the battery, the instantaneous power demand and the long-term life loss are balanced, the battery power density is improved, the temperature rise control is optimized, and the cycle life is prolonged. The method has significant application value in the fields of electric vehicles, energy storage systems and consumer electronics.

[0071] Compared with the prior art, the method has the following effects: the power density is greater than or equal to 5000W / kg and the 10-second pulse is sustained, which is 3 times higher than that of a conventional LFP battery; the temperature rise control is that the temperature rise is less than or equal to 30 DEG C when discharging at 50C, while the temperature rise of the traditional scheme is greater than or equal to 80 DEG C; and the cycle life is 2000 times at 5C charge-discharge, and the capacity retention rate is greater than 85%.

[0072] The above is only a preferred embodiment of the application and is not used to limit the application. For those skilled in the art, the application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for battery rate regulation based on joint computation, characterized in that, The method comprises: collecting real-time operation parameters of the battery, the real-time operation parameters comprising real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number; obtaining an SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress; estimating a first SOC value based on the SOH value and the real-time current; correcting the first SOC value based on the real-time voltage observation value to obtain a second SOC value; determining a first charge-discharge rate according to the SOH value and the second SOC value, and controlling the charge-discharge current of the battery according to the first charge-discharge rate.

2. The method of claim 1, wherein, The method further comprises: obtaining second operation parameters of the battery after control, and updating the SOH value based on the second operation parameters.

3. The method of claim 2, wherein, The second operation parameters comprise at least one of a second electrochemical impedance spectrum, a second temperature stress and a second cycle number.

4. The method of claim 1, wherein, The method of obtaining an SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress comprises: performing frequency domain analysis on the first electrochemical impedance spectrum to obtain impedance characteristic parameters of the battery; comparing the impedance characteristic parameters with reference impedance parameters to obtain an internal resistance growth amount; determining a capacity attenuation ratio based on the first cycle number and the first temperature stress; and weighting and fusing the internal resistance growth amount and the capacity attenuation ratio to obtain the SOH value.

5. The method of claim 1, wherein, The method further comprises: collecting an open-circuit voltage of the battery, and querying an SOC value corresponding to the open-circuit voltage in a preset OCV-SOC curve and taking the SOC value as an initial SOC value.

6. The method of claim 5, wherein, The method of estimating a first SOC value based on the SOH value and the real-time current comprises: determining a current rated capacity of the battery based on the SOH value; integrating the real-time current by using an ampere-hour integration method, and determining an SOC change amount according to an integration result and the current rated capacity; and updating the initial SOC value according to the SOC change amount to obtain the first SOC value.

7. The joint-computation-based battery rate adjustment method of claim 1, wherein, The method of correcting the first SOC value based on the real-time voltage observation value to obtain a second SOC value comprises: determining a state variable according to the first SOC value and the real-time voltage observation value; obtaining a terminal voltage observation value, and determining a deviation value based on the terminal voltage observation value and the real-time voltage observation value; updating an initial covariance matrix based on the state variable, and determining a Kalman gain according to an updating result; and correcting the first SOC value according to the deviation value and the Kalman gain to obtain the second SOC value.

8. The joint-computation-based battery rate adjustment method of claim 1, wherein, The method of determining a first charge-discharge rate according to the SOH value and the second SOC value comprises: taking the SOH value and the second SOC value as input parameters, searching for a charge-discharge rate corresponding to the input parameters in a preset rate mapping table, and taking the charge-discharge rate as the first charge-discharge rate.

9. The joint-computation-based battery rate adjustment method of claim 1, wherein, The method of controlling the charge-discharge current of the battery according to the first charge-discharge rate comprises: taking the SOH value, the second SOC value, the first temperature stress and historical load as state space data; The preset adjustment multiple is taken as the action space data; Based on the state space data and the action space data, a reward function value is determined; Based on the reward function value, the first charge-discharge multiple is optimized to obtain a second charge-discharge multiple, so as to control the charge-discharge current according to the second charge-discharge multiple.

10. A joint-computation-based battery rate adjustment system for performing the joint-computation-based battery rate adjustment method according to any one of claims 1 to 9, characterized by, Comprise: The acquisition module is configured to collect real-time operation parameters of the battery, the real-time operation parameters including real-time current, real-time voltage observation value, first electrochemical impedance spectrum, first temperature stress and first cycle number; The first optimization module is configured to obtain an SOH value based on the first electrochemical impedance spectrum, the first cycle number and the first temperature stress; The joint optimization module is configured to estimate based on the SOH value and the real-time current to obtain a first SOC value; The second optimization module is configured to correct the first SOC value based on the real-time voltage observation value to obtain a second SOC value; The intelligent adjustment module is configured to determine a first charge-discharge multiple according to the SOH value and the second SOC value, and control the charge-discharge current of the battery according to the first charge-discharge multiple.

Images