BMS-based Battery Charge and Discharge Optimization Method

Abstract

The present invention relates to the technical field of batteries, and specifically relates to a battery charge and discharge optimization method based on a BMS, including: collecting battery charge and discharge historical state parameters during the battery charge and discharge process through the BMS, creating a database, and storing the collected battery charge and discharge historical state parameters in the database; during the battery charging process, collecting battery charging state parameters in real time, analyzing the real-time battery power storage performance based on the accumulation of the real-time collected battery charging state parameters, and based on the analysis result of the real-time battery power storage performance, the present invention can assist the BMS system to optimize the charge and discharge behavior of the battery in real time. Compared with the existing BMS system, the optimization response efficiency for battery charge and discharge is higher, and it can better adapt to the scenario where the battery charge and discharge interference is frequent and complex. At the same time, during the optimization process of the battery charge and discharge, the current health state of the battery can also be evaluated synchronously.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and particularly to a method for optimizing battery charging and discharging based on BMS. Background Art

[0002] BMS is mainly used to manage and control battery packs, mostly for secondary batteries, which can prevent overcharging and over-discharging of batteries, improve battery utilization rate, and extend battery life. Its functions include accurately estimating the state of charge of the battery pack, real-time monitoring of parameters such as the terminal voltage, temperature, charging and discharging current of the battery, and achieving balance between batteries, and it is widely used in fields such as electric vehicles, battery cars, robots, and drones.

[0003] A Chinese invention patent with the application number 201710219416.6 discloses an optimized scheduling method for energy storage batteries based on ordered charging and discharging. The method includes the following steps: 1) Based on the rotation use of batteries, formulate ordered charging and discharging scheduling rules; 2) By calculating the maximum value of the number of charge and discharge cycles under ordered charging and discharging, calculate the reference value of the number of batteries for power distribution: 3) Taking the operating cost of the battery energy storage system as the optimization goal, establish an economic optimization model, and the economic optimization model is: taking the operating cost of the battery energy storage system as the evaluation index of the optimization scheduling effect, and the operating cost C includes the depreciation cost CL and the maintenance cost Cm: C = CL + Cm.

[0004] This application aims to solve the problems of: "In existing research, most attention is paid to the research on the control method of a single battery device, and little attention is paid to how to distribute power among multiple batteries. Moreover, for the power distribution of batteries in energy storage scheduling, an average distribution method is mostly used, without considering the impact of frequent charging and discharging on battery life. When the energy storage system operates, the usage status of each battery in all charging and discharging processes is the same, which will lead to a relatively large total number of charge and discharge cycles of all batteries, reducing the life of the energy storage system."

[0005] However, currently, during the process of the BMS system managing batteries, there may be many interferences in the charging and discharging scenarios of the batteries. The BMS system has a poor adjustment response efficiency for battery charging and discharging and cannot fully adapt to the interferences in the changeable battery charging and discharging scenarios.

[0006] Therefore, a method for optimizing battery charging and discharging based on BMS is proposed. Summary of the Invention

[0007] In view of the above-mentioned drawbacks of the prior art, the present invention provides a method for optimizing battery charging and discharging based on BMS, which solves the technical problems raised in the above background art.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0009] BMS-based battery charge and discharge optimization method, including:

[0010] During the battery charge and discharge process, the BMS collects the historical state parameters of the battery charge and discharge, creates a database, and stores the collected historical state parameters of the battery charge and discharge in the database; during the battery charging process, the battery charging state parameters are collected in real time, and based on the accumulation of the real-time collected battery charging state parameters, the real-time battery storage performance is analyzed. Based on the analysis result of the real-time battery storage performance, the application of the battery charging optimization logic is determined; during the battery discharge process, the battery discharge state parameters are collected in real time, and based on the accumulation of the real-time collected battery discharge state parameters, the real-time battery discharge performance is analyzed. Based on the analysis result of the real-time battery discharge performance, the application of the battery discharge optimization logic is determined; according to the decision result, the battery charging optimization logic and the battery discharge optimization logic are applied to optimize the battery charge and discharge process; record the battery charge and discharge process optimization information, evaluate the battery health state based on the optimization information, and establish a trend chart representing the change of the battery health state based on the battery health state evaluation result; output and real-time update the trend chart representing the change of the battery health state.

[0011] Furthermore, the historical state parameters of the battery charge and discharge include: battery cell temperature, overall battery temperature, battery charge and discharge duration, battery cell charge and discharge voltage, overall battery charge and discharge voltage, SOC extreme value record, SOC change curve, SOH change curve. The historical state parameters of the battery charge and discharge all come from the BMS. The historical state parameters of the battery charge and discharge are collected based on a specified collection period. In the database, when storing the historical state parameters of the battery charge and discharge, they are stored separately based on the collection period of the source of the historical state parameters of the battery charge and discharge;

[0012] Among them, after the historical state parameters of the battery charge and discharge are stored in the database, the historical state parameters of the battery charge and discharge stored in each separately stored interval in the database are traversed, and the battery cell temperature, overall battery temperature, battery charge and discharge duration, battery cell charge and discharge voltage, and overall battery charge and discharge voltage parameters in the historical state parameters of the battery charge and discharge are retrieved, and the trigger threshold for the battery charge and discharge optimization operation is set with reference to the retrieved parameters;

[0013] The trigger threshold for the battery charge and discharge optimization operation is not unique, and the trigger threshold for the battery charge and discharge optimization operation is user-defined by the system end user.

[0014] Further, during the battery charging process, when collecting the battery charging state parameters in real time, it is synchronously monitored whether the collected battery charging state parameters meet any one of the battery charging optimization operation trigger thresholds. When any one of the battery charging state parameters meets the battery charging optimization operation trigger threshold, the battery charging state parameters collected before the compliance determination are discarded, and the collection of the battery charging state parameters is continued, and the real-time electricity storage performance of the battery is analyzed by applying the collected battery charging state parameters;

[0015] The battery charging state parameters include: battery cell temperature, overall battery temperature, battery charging duration, battery cell charging voltage, overall battery charging voltage, SOC change curve, SOH change curve.

[0016] Further, when analyzing the real-time electricity storage performance of the battery during the analysis operation, three groups of the latest collected battery charging state parameters are applied, and the analysis logic of the real-time electricity storage performance of the battery is expressed as:

[0017]

[0018] In the formula: H is the electricity storage performance shown by two consecutive sets of collected battery charging state parameters; n is the total number of battery cells in the battery pack; is the ratio of the battery cell temperature of the i-th battery cell in two consecutive sets of collected battery charging state parameters; is the voltage ratio of the i-th battery cell in two consecutive sets of collected battery charging state parameters; is the ratio of the battery pack temperature in two consecutive sets of collected battery charging state parameters; is the ratio of the battery pack voltage in two consecutive sets of collected battery charging state parameters; m is the total number of nodes in the SOC change curve; is the ratio of the slopes of the curves where the j-th node and the j + 1-th node are located; MAX(l SOH ) and MIN(l SOH ) are the maximum and minimum values in the SOH change curve; T is the cumulative charging duration in two consecutive sets of collected battery charging state parameters;

[0019] Among them, based on the battery real-time electricity storage performance analysis logic formula, the earliest two groups and the latest two groups of the three groups of the latest collected battery charging state parameters are respectively applied to perform two calculations of the electricity storage performance H, and the calculation results are recorded as H 1 、H 2 ,H 1 ≥H 2 indicates that the electricity storage performance of the battery is stable, and H 1 <H 2 indicates that the electricity storage performance of the battery is decaying, and the decision battery charging optimization logic is applied.

[0020] Further, the battery charging optimization logic is as follows: Set a safety threshold for the battery charging voltage. When the battery's power storage performance decays, control the current charging voltage of the battery to continuously decrease at a specified frequency and not exceed the minimum value of the battery charging voltage safety threshold.

[0021] Among them, the frequency applied when the battery charging voltage decreases is user-defined. When the real-time power storage performance analysis results of the battery are stable for two consecutive times, the battery charging voltage continuously rises based on the original decreasing frequency and does not exceed the maximum value of the battery charging voltage safety threshold.

[0022] Further, during the battery discharge process, when collecting the battery discharge state parameters in real time, synchronously monitor whether the collected battery discharge state parameters meet any one of the battery discharge optimization operation trigger thresholds. When any one of the battery discharge state parameters meets the battery discharge optimization operation trigger threshold, analyze the real-time discharge performance of the battery using the battery discharge state parameters collected before the determination.

[0023] The battery discharge state parameters include: battery cell temperature, battery overall temperature, battery discharge duration, battery cell discharge voltage, battery overall discharge voltage, SOC change curve, SOH change curve, SOC extreme value record.

[0024] Further, when analyzing the real-time discharge performance of the battery, three groups of the latest collected battery discharge state parameters are used. The real-time discharge performance analysis logic of the battery is expressed as:

[0025]

[0026] In the formula: F is the discharge performance shown by two groups of continuously collected battery discharge state parameters; MAX(soc) and MIN(soc) are the maximum and minimum values in the extreme value record; MAX(c 1 , c 2 , c 3 ,...) are the maximum temperatures of the battery cells in two groups of continuously collected battery discharge state parameters; MAX(C) is the maximum temperature of the battery pack in two groups of continuously collected battery discharge state parameters; is the average voltage of the battery pack in two groups of continuously collected battery discharge state parameters; a is the total value of the battery cell voltage values in two groups of continuously collected battery discharge state parameters; vb is the bth voltage of the battery cell; SIMM(L SOC , L SOH ) is the similarity between the SOC change curve and the SOH change curve; T is the cumulative battery discharge duration in two groups of continuously collected battery discharge state parameters.

[0027] Among them, based on the real-time discharge performance analysis logic formula of the battery, the earliest two groups and the latest two groups of the three groups of newly collected battery discharge state parameters are respectively applied to perform two calculations of the discharge performance F, and the calculation results are denoted as F1 and F2. F1 < F2 indicates that the discharge performance of the battery decays, and F1 ≥ F2 indicates that the discharge performance of the battery is stable.

[0028] Furthermore, the battery discharge optimization logic is as follows: Set the battery discharge voltage limit threshold. When the discharge performance of the battery decays, control the current discharge voltage of the battery to continuously rise at a specified frequency and not exceed the maximum value of the battery discharge voltage limit threshold.

[0029] Among them, the frequency applied when the battery discharge voltage decreases is user-defined. When the real-time discharge performance analysis results of the battery are stable in two consecutive times, the battery discharge voltage continuously decreases based on the original decreasing frequency and does not exceed the minimum value of the battery discharge voltage limit threshold.

[0030] Furthermore, the battery health status evaluation logic is expressed as:

[0031]

[0032] In the formula: G is the battery health status performance value; H is the battery charge storage performance; F is the battery discharge performance.

[0033] Among them, the smaller the battery health status performance value G is, the healthier the battery is. On the contrary, it means the battery is less healthy. The continuously calculated battery health status performance values G are denoted as G 1 、G 2 、G 3 、... Through the comparison of G 1 、G 2 、G 3 、..., determine whether the battery is healthy.

[0034] Furthermore, the determination logic of whether the battery is healthy is expressed as:

[0035] Gx - 2 ≥Gx - 1 ≥Gx;

[0036] In the formula: Gx - 2 、Gx - 1 、Gx represent the battery health status performance values obtained from the latest three calculations;

[0037] Among them, the battery health status performance value is applied to represent the trend chart of the battery health status change. The horizontal axis of the trend chart representing the battery health status change represents time, and the vertical axis represents the battery health status performance value obtained at the corresponding time node.

[0038] Adopting the technical solution provided by the present invention, compared with the known public technology, the following

[0039] Advantages are as follows:

[0040] The present invention provides a battery charge and discharge optimization method based on BMS. During the execution of this method, through setting logic, it can assist the BMS system to optimize the charge and discharge behavior of the battery in real time. Compared with the existing BMS system, the optimization response efficiency for the battery charge and discharge is higher, and it can better adapt to the scenario where the battery charge and discharge interference is frequent and complex. At the same time, during the optimization of the battery charge and discharge, it can also synchronously evaluate the current health state of the battery, and establish a visualization graph based on the evaluation result, further assisting the battery user to digitally read the real-time monitoring state of the battery based on the established visualization graph. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a structural schematic diagram of a battery charge and discharge optimization method based on BMS. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0044] The following further describes the present invention with reference to the embodiments.

[0045] Embodiment 1:

[0046] The battery charge and discharge optimization method based on BMS in this embodiment, as Figure 1 shown, includes:

[0047] During the battery charge and discharge process, the BMS collects the historical state parameters of the battery charge and discharge, creates a database, and stores the collected historical state parameters of the battery charge and discharge by applying the database;

[0048] During the battery charging process, the battery charging state parameters are collected in real time. Based on the accumulation of the real-time collected battery charging state parameters, the real-time battery storage performance is analyzed. Based on the analysis result of the real-time battery storage performance, the application of the battery charging optimization logic is determined.

[0049] During the battery charging process, when collecting the battery charging state parameters in real time, it is simultaneously monitored whether the collected battery charging state parameters meet any one of the battery charging optimization operation trigger thresholds. When any one of the battery charging state parameters meets the battery charging optimization operation trigger threshold, the battery charging state parameters collected before the compliance determination are discarded, and the collection of the battery charging state parameters continues. The collected battery charging state parameters are used to analyze the real-time battery storage performance.

[0050] The battery charging state parameters include: battery cell temperature, overall battery temperature, battery charging duration, battery cell charging voltage, overall battery charging voltage, SOC change curve, SOH change curve.

[0051] When analyzing the real-time battery storage performance, three groups of the latest collected battery charging state parameters are applied. The real-time battery storage performance analysis logic is expressed as:

[0052]

[0053] In the formula: H is the storage performance shown by two groups of continuously collected battery charging state parameters; n is the total number of battery cells in the battery pack; is the ratio of the battery cell temperature of the i-th battery cell in two groups of continuously collected battery charging state parameters; is the voltage ratio of the i-th battery cell in two groups of continuously collected battery charging state parameters; is the ratio of the battery pack temperature in two groups of continuously collected battery charging state parameters; is the ratio of the battery pack voltage in two groups of continuously collected battery charging state parameters; m is the total number of nodes in the SOC change curve; is the ratio of the slopes of the curves where the j-th node and the j + 1-th node are located; MAX(l SOH ) and MIN(l SOH ) are the maximum and minimum values in the SOH change curve; T is the cumulative charging duration in two groups of continuously collected battery charging state parameters;

[0054] Among them, based on the real-time battery storage performance analysis logic formula, the earliest two groups and the latest two groups of the three groups of the latest collected battery charging state parameters are respectively applied to perform two calculations of the storage performance H, and the calculation results are denoted as H 1 、H 2 ,H 1 ≥H 2Indicates that the battery's charge storage performance is stable, H 1 <H 2 Indicates that the battery's charge storage performance is decaying, and the battery charging optimization logic is applied;

[0055] Through the above logical formula, provide a specified analysis logic for the battery's charge storage performance, and provide necessary evaluation data support for the final evaluation of the battery's health state in this method.

[0056] The battery charging optimization logic is: set the battery charging voltage safety threshold. When the battery's charge storage performance decays, control the current charging voltage of the battery to continuously decrease at a specified frequency and not exceed the minimum value of the battery charging voltage safety threshold;

[0057] Among them, the frequency applied when the battery charging voltage decreases is user-defined. When the real-time charge storage performance analysis results of the battery are stable for two consecutive times, the battery charging voltage continuously rises based on the original decreasing frequency and does not exceed the maximum value of the battery charging voltage safety threshold;

[0058] During the battery discharge process, when collecting the battery discharge state parameters in real time, synchronously monitor whether the collected battery discharge state parameters meet any one of the battery discharge optimization operation trigger thresholds. When any one of the battery discharge state parameters meets the battery discharge optimization operation trigger threshold, analyze the real-time discharge performance of the battery using the battery discharge state parameters collected before the determination;

[0059] The battery discharge state parameters include: battery cell temperature, battery overall temperature, battery discharge duration, battery cell discharge voltage, battery overall discharge voltage, SOC change curve, SOH change curve, SOC extreme value record;

[0060] When analyzing the real-time discharge performance of the battery, apply three sets of the latest collected battery discharge state parameters. The real-time discharge performance analysis logic of the battery is expressed as:

[0061]

[0062] In the formula: F is the discharge performance shown by two sets of continuously collected battery discharge state parameters; MAX(soc) and MIN(soc) are the maximum and minimum values in the extreme value record; MAX(c 1 、c 2 、c 3 、...) is the maximum temperature of the battery cell in two sets of continuously collected battery discharge state parameters; MAX(C) is the maximum temperature of the battery pack in two sets of continuously collected battery discharge state parameters; is the average voltage of the battery pack among two sets of continuously collected battery discharge state parameters; a is the total of the battery cell voltage values among two sets of continuously collected battery discharge state parameters; vb is the b-th voltage of the battery cell; SIMM(L SOC ,L SOH ) is the similarity between the SOC change curve and the SOH change curve; T is the cumulative battery discharge duration among two sets of continuously collected battery discharge state parameters;

[0063] Among them, based on the real-time discharge performance analysis logic formula of the battery, the earliest two sets and the latest two sets among three sets of the latest collected battery discharge state parameters are respectively applied to perform two calculations of the discharge performance F, and the calculation results are denoted as F1 and F2. F1 < F2 indicates that the discharge performance of the battery decays, and F1 ≥ F2 indicates that the discharge performance of the battery is stable;

[0064] Through the above logic formula, a specified analysis logic is provided for the battery discharge performance, and necessary logic operation parameter support is provided for the battery health assessment logic.

[0065] During the battery discharge process, the battery discharge state parameters are collected in real time. Based on the accumulation of the real-time collected battery discharge state parameters, the real-time discharge performance of the battery is analyzed. Based on the analysis result of the real-time discharge performance of the battery, the application of the battery discharge optimization logic is determined;

[0066] According to the decision result, the battery charging optimization logic and the battery discharge optimization logic are applied to optimize the charging and discharging process of the battery;

[0067] Record the optimization information of the battery charging and discharging process, evaluate the battery health status based on the optimization information, and establish a trend chart representing the change of the battery health status based on the evaluation result of the battery health status;

[0068] Output and real-time update of the trend chart representing the change of the battery health status;

[0069] The battery health status assessment logic indicates:

[0070]

[0071] In the formula: G is the battery health status performance value; H is the battery energy storage performance; F is the battery discharge performance;

[0072] Among them, the smaller the battery health status performance value G is, the healthier the battery is. On the contrary, it means the battery is less healthy. The continuously calculated battery health status performance values G are denoted as G 1 、G 2 、G 3 、... Through the comparison of G 1 、G 2 、G 3 、..., determine whether the battery is healthy;

[0073] The determination logic of whether the battery is healthy is expressed as:

[0074] Gx - 2 ≥ Gx - 1 ≥ Gx;

[0075] In the formula: Gx - 2 、Gx - 1 、Gx represent the battery health state performance values obtained from the latest three groups;

[0076] Among them, the battery health state performance value is applied to represent the trend chart of the battery health state change. The horizontal axis of the trend chart representing the battery health state change represents time, and the vertical axis represents the battery health state performance value obtained at the corresponding time node.

[0077] In this embodiment, through the execution of the method in the above embodiment, based on the BMS system, the BMS system is assisted to optimize and improve the charging and discharging of the battery, so that the BMS system can serve the optimization service in the battery charging and discharging process with higher response efficiency;

[0078] At the same time, the real-time health state of the battery is represented in the form of a trend chart, which is convenient for the battery user to read the real-time health state of the battery, so as to make an adaptive plan and maintenance for the battery more predictably.

[0079] Embodiment 2:

[0080] At the specific implementation level, on the basis of Embodiment 1, this embodiment further specifically describes the BMS-based battery charging and discharging optimization method in Embodiment 1 with reference to Figure 1 shown as follows:

[0081] The battery charging and discharging historical state parameters include: battery cell temperature, battery overall temperature, battery charging and discharging duration, battery cell charging and discharging voltage, battery overall charging and discharging voltage, SOC extreme value record, SOC change curve, SOH change curve. The battery charging and discharging historical state parameters all come from the BMS. The battery charging and discharging historical state parameters perform the acquisition operation based on the specified acquisition period. In the database, when storing the battery charging and discharging historical state parameters, they are stored separately based on the acquisition period of the battery charging and discharging historical state parameter source;

[0082] Among them, after the battery charging and discharging historical state parameters are stored in the database, the battery charging and discharging historical state parameters stored in each separately stored interval in the database are traversed, and the battery cell temperature, battery overall temperature, battery charging and discharging duration, battery cell charging and discharging voltage, and battery overall charging and discharging voltage parameters in the battery charging and discharging historical state parameters are retrieved, and the battery charging and discharging optimization operation trigger threshold is set with reference to the retrieved parameters;

[0083] The trigger threshold for optimizing battery charge and discharge operations is not unique, and the trigger threshold for optimizing battery charge and discharge operations is user-defined by the system user.

[0084] The battery discharge optimization logic is as follows: Set the extreme threshold of the battery discharge voltage. When the battery discharge performance decays, control the current battery discharge voltage to continuously rise at a specified frequency and not exceed the maximum value of the extreme threshold of the battery discharge voltage.

[0085] Among them, the frequency applied when the battery discharge voltage decreases is user-defined by the user. When the real-time discharge performance analysis results of the battery are stable in terms of battery storage performance for two consecutive times, the battery discharge voltage continuously decreases based on the original decreasing frequency and does not exceed the minimum value of the extreme threshold of the battery discharge voltage.

[0086] In this embodiment, through the above settings, the necessary execution logic and data support are provided for the execution of the method in Embodiment 1, ensuring the problem of executing the method steps in Embodiment 1, further optimizing the BMS management during the battery charge and discharge process, and thus achieving the battery charge and discharge optimization effect.

[0087] In summary, during the execution of the method in the above embodiments, through the set logic, it can assist the BMS system to optimize the charge and discharge behavior of the battery in real time. Compared with the existing BMS system, the optimization response efficiency for battery charge and discharge is higher, and it can better adapt to the scenario where the battery charge and discharge interference is frequent and complex. At the same time, during the battery charge and discharge optimization process, it can also synchronously evaluate the current health status of the battery and establish a visualization graph based on the evaluation results to further assist the battery user to digitally read the real-time monitoring status of the battery based on the established visualization graph.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A battery charging and discharging optimization method based on BMS, characterized in that: include: The battery charging and discharging history state parameters are collected by BMS during the battery charging and discharging process, a database is created, and the database is used to store the collected battery charging and discharging history state parameters; During the battery charging process, the battery charging status parameters are collected in real time, and the real-time battery storage performance is analyzed based on the accumulation of the real-time collected battery charging status parameters. Based on the analysis results of the real-time battery storage performance, the application of battery charging optimization logic is decided; During the battery discharge process, the battery discharge status parameters are collected in real time, and the real-time discharge performance of the battery is analyzed based on the accumulation of the real-time collected battery discharge status parameters. Based on the analysis results of the real-time discharge performance of the battery, the application of the battery discharge optimization logic is decided; According to the decision results, the battery charging optimization logic and battery discharging optimization logic are applied to optimize the battery charging and discharging process; Record the optimization information of the battery charging and discharging process, evaluate the battery health status based on the optimization information, and establish a trend chart showing the change of the battery health status based on the battery health status evaluation result; Output and real-time update of trend graphs showing changes in battery health status; The battery charge and discharge history state parameters include: battery cell temperature, battery overall temperature, battery charge and discharge time, battery cell charge and discharge voltage, battery overall charge and discharge voltage, SOC extreme value record, SOC change curve, SOH change curve, the battery charge and discharge history state parameters are all derived from BMS, the battery charge and discharge history state parameters are collected based on a specified collection cycle, and in the database, when the battery charge and discharge history state parameters are stored, they are differentiated and stored based on the collection cycle of the battery charge and discharge history state parameters source; Among them, after the battery charge and discharge historical state parameters are stored in the database, the battery charge and discharge historical state parameters stored in each storage interval of the database are traversed, and the battery single cell temperature, battery overall temperature, battery charge and discharge time, battery single cell charge and discharge voltage, and battery overall charge and discharge voltage parameters in the battery charge and discharge historical state parameters are retrieved, and the battery charge and discharge optimization operation trigger threshold is set with reference to the retrieved parameters; The battery charge and discharge optimization operation triggering threshold is not unique, and the battery charge and discharge optimization operation triggering threshold is customized by the system end user; During the battery charging process, when collecting battery charging status parameters in real time, synchronously monitor whether the collected battery charging status parameters meet any battery charging optimization operation trigger threshold. When any battery charging status parameter meets the battery charging optimization operation trigger threshold, the battery charging status parameters collected before the judgment is met are discarded, and the collection of battery charging status parameters continues, and the collected battery charging status parameters are used to analyze the real-time battery storage performance; The battery charging state parameters include: battery cell temperature, battery overall temperature, battery charging time, battery cell charging voltage, battery overall charging voltage, SOC change curve, SOH change curve; When performing the analysis operation of the real-time storage performance of the battery, three groups of the latest collected battery charging state parameters are applied. The real-time storage performance analysis logic of the battery is expressed as follows: Where: H is the storage performance of two groups of continuously collected battery charging state parameters; n is the total number of battery cells in the battery pack; is the ratio of the battery cell temperature of the ith battery cell in two sets of continuously collected battery charging state parameters; is the voltage ratio of the ith battery cell in two sets of continuously collected battery charging state parameters; is the ratio of the battery pack temperature in two sets of battery charging state parameters collected continuously; is the ratio of the battery pack voltage in two sets of continuously collected battery charging state parameters; m is the total number of nodes in the SOC change curve; is the ratio of the slope of the curve where the jth node is located to the slope of the curve where the j+1th node is located; MAX(l SOH )、MIN(l SOH ) are the maximum and minimum values ​​in the SOH change curve; T is the cumulative charging time in two sets of battery charging state parameters collected continuously; Among them, based on the real-time battery storage performance analysis logic formula, the earliest two groups and the most recent two groups of the three groups of the latest collected battery charging state parameters are respectively applied to perform two calculations of the storage performance H. The results are recorded as H1 and H2. H1≥H2 indicates that the battery storage performance is stable, and H1<H2 indicates that the battery storage performance is attenuated. The decision-making battery charging optimization logic is applied; The battery charging optimization logic is: setting a battery charging voltage safety threshold, and when the battery storage performance decays, controlling the current charging voltage of the battery to continuously decrease at a specified frequency and not exceed the minimum value of the battery charging voltage safety threshold; Among them, the frequency of application of the battery charging voltage when it is reduced is customized by the user. When the real-time storage performance analysis results of the battery are stable for two consecutive times, the battery charging voltage continues to increase based on the original reduction frequency and does not exceed the maximum value of the battery charging voltage safety threshold.

2. The battery charging and discharging optimization method based on BMS according to claim 1, characterized in that: During the battery discharge process, when collecting the battery discharge state parameters in real time, synchronously monitor whether the collected battery discharge state parameters meet any battery discharge optimization operation trigger threshold. When any battery discharge state parameter meets the battery discharge optimization operation trigger threshold, use the battery discharge state parameters collected before the compliance determination to analyze the battery real-time discharge performance; The battery discharge state parameters include: battery cell temperature, battery overall temperature, battery discharge time, battery cell discharge voltage, battery overall discharge voltage, SOC change curve, SOH change curve, and SOC extreme value record.

3. The battery charging and discharging optimization method based on BMS according to claim 1, characterized in that: When performing the analysis operation of the real-time discharge performance of the battery, three groups of the latest collected battery discharge state parameters are applied. The real-time discharge performance analysis logic of the battery is expressed as follows: Where: F is the discharge performance of two sets of continuously collected battery discharge state parameters; MAX(soc) and MIN(soc) are the maximum and minimum values ​​in the extreme value records; MAX(c1, c2, c3, ...) is the maximum temperature of the battery cell in the two sets of continuously collected battery discharge state parameters; MAX(C) is the maximum temperature of the battery pack in the two sets of continuously collected battery discharge state parameters; is the average voltage of the battery pack in two sets of battery discharge state parameters collected continuously; a is the total value of the battery cell voltage in two sets of battery discharge state parameters collected continuously; vb is the bth voltage of the battery cell; SIMM (L SOC ,L SOH ) is the similarity between the SOC change curve and the SOH change curve; T is the cumulative battery discharge time in the two sets of continuously collected battery discharge state parameters; Among them, based on the real-time discharge performance analysis logic formula of the battery, the earliest two and the latest two groups of three groups of newly collected battery discharge state parameters are respectively applied to perform two discharge performance F calculations, and the results are recorded as F1 and F2. F1<F2 indicates that the discharge performance of the battery is attenuated, and F1≥F2 indicates that the discharge performance of the battery is stable.

4. The battery charging and discharging optimization method based on BMS according to claim 3 is characterized in that: The battery discharge optimization logic is: setting a battery discharge voltage limit threshold, and when the battery discharge performance decays, controlling the current discharge voltage of the battery to rise continuously at a specified frequency and not exceed the maximum value of the battery discharge voltage limit threshold; Among them, the frequency at which the battery discharge voltage is reduced is customized by the user. When the real-time discharge performance analysis results of the battery show that the battery storage performance is stable for two consecutive times, the battery discharge voltage is continuously reduced based on the original reduction frequency and does not exceed the minimum value of the battery discharge voltage limit threshold.

5. The battery charging and discharging optimization method based on BMS according to claim 1, characterized in that: The battery health status evaluation logic represents: In the formula: G is the battery health status performance value; H is the battery storage performance; F is the battery discharge performance; Among them, the smaller the battery health status performance value G is, the healthier the battery is, and vice versa, the unhealthier the battery is. The continuously obtained battery health status performance values ​​G are recorded as G1, G2, G3, ..., and the health of the battery is determined by comparing G1, G2, G3, ...

6. The battery charging and discharging optimization method based on BMS according to claim 5, characterized in that: The logic for determining whether the battery is healthy is expressed as: G x-2 ≥G x-1 ≥G x ; Where: Gx-2, Gx-1, Gx represent the latest three groups of battery health status performance values; Among them, the battery health status performance value is applied to a trend graph representing the change of the battery health status. The horizontal axis of the trend graph representing the change of the battery health status represents time, and the vertical axis represents the battery health status performance value obtained at the corresponding time node.

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