Balanced optimization method and system for battery life and charging efficiency

Abstract

The invention relates to the technical field of electrochemistry and energy storage, and discloses a battery life and charging efficiency equalization optimization method and system, and the method comprises the steps: extracting the capacity fading rate, internal resistance change trend and temperature sensitivity of a target battery; analyzing the real-time health state of the target battery and the battery chemical model, and establishing a dynamic reference charging curve of the target battery; defining a charging efficiency threshold value and an IDR safety limit value of the target battery; monitoring battery charging data in the charging process of the target battery in real time so as to analyze the current charging efficiency and the instantaneous attenuation rate of the target battery, and establishing a dual-target optimization function of the target battery; and the charging efficiency equalization parameters of the target battery are generated by using the dual-objective optimization function, and the charging efficiency equalization parameters comprise the optimized charging voltage, the optimized charging current and the optimized charging pulse interval, so that the service life of the battery can be prolonged, and the equalization optimization effect of the charging efficiency can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to a battery life and charging efficiency balanced optimization method and system, belonging to the field of electrochemistry and energy storage technology. BACKGROUND

[0002] Balanced optimization of battery life and charging efficiency refers to the process of minimizing the loss of battery life while maximizing charging speed (efficiency) through intelligent control of charging strategies (such as current, voltage, charging mode, etc.) during battery charging management, so as to achieve the best balance between the two. Through the collaborative work of intelligent software algorithms and hardware, our devices can enjoy the convenience brought by modern fast charging technology and also prolong the healthy cycle of the battery as much as possible, ultimately achieving the best result of "both fish and bear's palm".

[0003] The common way of balanced optimization of battery life and charging efficiency is a passive protection strategy based on fixed thresholds. The core of this approach is to set several fixed and universal safety thresholds, such as automatically reducing the charging power when the battery temperature exceeds 45℃, or pausing charging when the battery is charged to 80%, waiting for the user to manually or according to the use habit to fully charge at a specific time (such as before sleep). It passively protects the battery by simply executing a slowdown or stop operation when potential risks (such as high temperature, high capacity) are detected, trying to find a fixed balance point between charging speed and battery safety. This approach cannot dynamically adjust according to the real-time health status of the battery. When the battery is still new, it may be too conservative; when the battery has aged, this fixed threshold may not provide sufficient protection, thus failing to accurately achieve balanced optimization of battery life and charging efficiency. SUMMARY

[0004] The present application provides a battery life and charging efficiency balanced optimization method and system, which mainly aims to improve the balanced optimization effect of battery life and charging efficiency.

[0005] To achieve the above purpose, the present application provides a battery life and charging efficiency balanced optimization method, which comprises: obtaining the historical charging and discharging data of the target battery to extract the capacity attenuation rate, internal resistance change trend and temperature sensitivity of the target battery; analyzing the real-time health status of the target battery and the battery chemical model, and establishing a dynamic reference charging curve of the target battery based on the battery chemical model, the real-time health status, the capacity attenuation rate, the internal resistance change trend and the temperature sensitivity; defining the charging efficiency threshold and IDR safety limit of the target battery; monitoring battery charging data of the target battery charging process in real time to analyze a current charging efficiency and an instantaneous attenuation rate of the target battery, and establishing a double-objective optimization function of the target battery when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit value; generating a charging performance balancing parameter of the target battery by using the double-objective optimization function according to the real-time health state and the dynamic reference charging curve, wherein the charging performance balancing parameter includes an optimized charging voltage, an optimized charging current, and an optimized charging pulse interval.

[0006] Optionally, the extracting the capacity attenuation rate, the internal resistance change trend, and the temperature sensitivity of the target battery comprises: analyzing a cycle actual capacity, a dynamic internal resistance, and an ambient temperature of the target battery according to historical charging and discharging data of the target battery; fitting a capacity attenuation curve of the target battery based on the cycle actual capacity; analyzing a capacity attenuation rate of the target battery through the capacity attenuation curve; fitting an internal resistance growth model of the target battery according to the dynamic internal resistance to analyze an internal resistance change trend of the target battery; calculating a capacity temperature coefficient and an internal resistance temperature coefficient of the target battery based on the temperature sensitivity; analyzing the temperature sensitivity of the target battery through the capacity temperature coefficient and the internal resistance temperature coefficient.

[0007] Optionally, the analyzing the temperature sensitivity of the target battery through the capacity temperature coefficient and the internal resistance temperature coefficient comprises: defining a capacity temperature weight and an internal resistance temperature weight of the capacity temperature coefficient and the internal resistance temperature coefficient; analyzing activation energy of the target battery; calculating the temperature sensitivity of the target battery by using the following formula according to the capacity temperature coefficient, the internal resistance temperature coefficient, the capacity temperature weight, the internal resistance temperature weight, and the activation energy:

[0008] wherein, represents the temperature sensitivity of the target battery, represents the capacity temperature coefficient of the target battery, represents the internal resistance temperature coefficient of the target battery, represents the capacity temperature weight, represents the internal resistance temperature weight, represents the activation energy of the target battery, represents a reference temperature, represents the Boltzmann constant.

[0009] Optionally, the analyzing the real-time health status of the target battery and the battery chemistry model comprises: calculating the internal resistance type SOH and the capacity type SOH of the target battery; combining the internal resistance type SOH and the capacity type SOH, outputting the real-time health status of the target battery; defining the P2D model of the target battery; analyzing the SEI film thickness and the negative electrode current density of the target battery; based on the SEI film thickness and the negative electrode current density, establishing the SEI film growth model of the target battery; integrating the SEI film growth model into the P2D model to obtain the battery chemistry model of the target battery

[0010] Optionally, the establishing the SEI film growth model of the target battery based on the SEI film thickness and the negative electrode current density comprises: analyzing the battery state of charge of the target battery, and determining the SOC sensitivity coefficient of the battery state of charge to the target battery; determining the pre-exponential factor of the target battery; according to the SEI film thickness, the negative electrode current density, the battery state of charge, the SOC sensitivity coefficient and the pre-exponential factor, constructing the SEI film growth model of the target battery.

[0011] Optionally, the establishing the dynamic reference charging curve of the target battery based on the battery chemistry model, the real-time health status, the capacity attenuation rate, the internal resistance change trend and the temperature sensitivity comprises: based on the real-time health status, performing aging calibration on the battery chemistry model to obtain a calibrated battery chemistry model; integrating the capacity attenuation rate and the internal resistance change trend into the calibrated battery chemistry model to obtain a performance analysis battery chemistry model; according to the temperature sensitivity, establishing a temperature-internal resistance correction table and a temperature-charging efficiency correction table of the performance analysis battery chemistry model; based on the temperature-internal resistance correction table and the temperature-charging efficiency correction table, analyzing model correction parameters of the performance analysis battery chemistry model; establishing the dynamic reference charging curve of the target battery through the model correction parameters.

[0012] Optionally, the analyzing the current charging efficiency and the instantaneous attenuation rate of the target battery comprises: According to the battery charging data of the target battery, the input energy and the effective energy storage of the target battery are calculated; Based on the input energy and the effective energy storage, the current charging efficiency of the target battery is calculated; An attenuation model of the target battery is established; The attenuation characteristics of the battery charging data are extracted; According to the attenuation characteristics, the instantaneous attenuation rate of the target battery is analyzed by using the attenuation model.

[0013] Optionally, when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit, a bi-objective optimization function of the target battery is established, including: When the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit, the optimization target of the target battery is determined, wherein the optimization target includes maximizing charging performance and minimizing life loss; The decision variable of the target battery is analyzed; The constraint condition of the target battery is established, wherein the constraint condition includes hard constraint and soft constraint; Based on the optimization target, the decision variable and the constraint condition, the bi-objective optimization function of the target battery is established.

[0014] Optionally, according to the real-time health state and the dynamic reference charging curve, the charging performance balance parameter of the target battery is generated by using the bi-objective optimization function, including: Based on the bi-objective optimization function, a bi-objective Pareto solution set of the target battery is generated; According to the real-time health state and the dynamic reference charging curve, the bi-objective Pareto solution set is screened to obtain a screened Pareto solution set; The screened Pareto solution set is pruned in target space to obtain a pruned Pareto solution set; The pruned Pareto solution set is iterated to obtain a target Pareto solution set of the target battery, so as to output the charging performance balance parameter of the target battery.

[0015] In order to solve the above problems, the application also provides a battery life and charging performance balance optimization system, which comprises: A battery data analysis module is used to obtain the historical charging and discharging data of a target battery, so as to extract the capacity attenuation rate, internal resistance change trend and temperature sensitivity of the target battery; a charging curve construction module configured to analyze a real-time health state of the target battery and a battery chemistry model, and to establish a dynamic reference charging curve of the target battery based on the battery chemistry model, the real-time health state, the capacity fade rate, the internal resistance change trend, and the temperature sensitivity; a battery limit value determination module configured to define a charging efficiency threshold value and an IDR safety limit value of the target battery; an optimization function construction module configured to monitor battery charging data of the target battery in real time to analyze a current charging efficiency and an instantaneous fade rate of the target battery, and to establish a double-objective optimization function of the target battery when the current charging efficiency is lower than the charging efficiency threshold value or the instantaneous fade rate is higher than the IDR safety limit value; a battery equalization optimization module configured to generate charging efficiency equalization parameters of the target battery by using the double-objective optimization function according to the real-time health state and the dynamic reference charging curve, wherein the charging efficiency equalization parameters include an optimized charging voltage, an optimized charging current, and an optimized charging pulse interval.

[0016] Firstly, the method breaks through the limitation of the traditional fixed charging curve, can dynamically generate the optimal reference charging curve according to the real-time health state, the capacity fade rate, the internal resistance change trend, and the temperature sensitivity of the battery, and ensures that the charging strategy is always accurately matched with the current actual condition of the battery, thereby fundamentally avoiding the overcharging or undercharging problem caused by battery aging or environmental changes. Secondly, by defining the charging efficiency threshold value and the IDR safety limit value, and combining the real-time monitoring data to trigger the double-objective optimization function, the application realizes the active protection and fine management of the charging process. When the battery performance index deviates from the safety range, the system can respond in time, and generate the charging efficiency equalization parameters including the optimized charging voltage, the current, and the pulse interval, which not only effectively suppresses the instantaneous fade of the battery during the charging process, significantly prolongs the cycle life of the battery, but also maximizes the charging efficiency and energy recovery rate under the premise of ensuring the safety of the battery. Therefore, the application can improve the equalization optimization effect of the battery life and charging efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 a flowchart of the battery life and charging efficiency equalization optimization method provided by an embodiment of the application; Figure 2 a module schematic diagram for realizing the battery life and charging efficiency equalization optimization method provided by an embodiment of the application.

[0018] The object implementation, functional features, and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0019] It is to be understood that the specific embodiments described herein are merely illustrative of the present application and do not limit the scope of the present application.

[0020] The battery life and charging efficiency balanced optimization method provided by the embodiments of the present application can be executed by software or hardware installed in a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0021] Referring to Figure 1 FIG. 1 shows a flowchart of a battery life and charging efficiency balanced optimization method provided by an embodiment of the present application. In this embodiment, the battery life and charging efficiency balanced optimization method includes the following steps. S1, obtaining historical charge and discharge data of a target battery to extract a capacity attenuation rate, an internal resistance change trend, and temperature sensitivity of the target battery.

[0022] It should be explained that the target battery refers to a specific object focused and studied in the current analysis, optimization, or prediction task. The historical charge and discharge data refer to a set of quantitative information related to the charging and discharging process of the target battery from the beginning of use to the current time, including but not limited to voltage, current, power / charge state, power charge and discharge cycle times, discharge rate / load curve, and other data.

[0023] The historical charge and discharge data of the present application extracts the capacity attenuation rate, internal resistance change trend, and temperature sensitivity of the target battery, which can closely match the actual health status and aging characteristics of each battery, fundamentally improving the accuracy of management.

[0024] In detail, the extraction of the capacity attenuation rate, internal resistance change trend, and temperature sensitivity of the target battery includes: According to the historical charge and discharge data of the target battery, the actual cycle capacity, dynamic internal resistance, and environmental temperature of the target battery are analyzed. Based on the actual cycle capacity, a capacity attenuation curve of the target battery is fitted. Through the capacity attenuation curve, the capacity attenuation rate of the target battery is analyzed. According to the dynamic internal resistance, an internal resistance growth model of the target battery is fitted to analyze the internal resistance change trend of the target battery. Based on the temperature sensitivity, the capacity temperature coefficient and internal resistance temperature coefficient of the target battery are calculated. Analyze temperature sensitivity of the target battery through the capacity temperature coefficient and the internal resistance temperature coefficient.

[0025] wherein, the cycle actual capacity refers to the actual amount of electricity that the battery can store or release at a specific cycle number, the dynamic internal resistance refers to the real-time impedance exhibited by the battery during the charging and discharging process, including ohmic resistance (instantaneous response) and polarization resistance (relaxation response), the ambient temperature refers to the temperature of the surrounding environment when the battery is working, the capacity attenuation curve refers to a trend curve describing the attenuation of the actual capacity of the battery with the increase of the cycle number, the capacity attenuation rate refers to the percentage or absolute value of the capacity attenuation per cycle number, the internal resistance growth model refers to a mathematical model describing the growth of the internal resistance of the battery with the cycle number or time, the internal resistance change trend refers to the change direction and rate (such as rising, falling, or stable) of the internal resistance with time or cycle number, the capacity temperature coefficient refers to the relative change rate of the battery capacity per 1°C change in temperature, the internal resistance temperature coefficient refers to the relative change rate of the battery capacity per 1°C change in temperature, and the temperature sensitivity refers to the dependence of the performance (capacity, internal resistance, and aging rate) of the battery on temperature.

[0026] Optionally, fitting the capacity attenuation curve of the target battery based on the cycle actual capacity can be fitted using a polynomial model.

[0027] Optionally, analyzing the capacity attenuation rate of the target battery through the capacity attenuation curve can be derived by differentiating the capacity attenuation curve.

[0028] Optionally, among the capacity temperature coefficient and the internal resistance temperature coefficient calculated based on the temperature sensitivity, the capacity temperature coefficient is calculated by calculating the capacity retention rate at each temperature point, and the internal resistance temperature coefficient can be analyzed by hybrid pulse power characteristic (HPPC) test.

[0029] Further, analyzing the temperature sensitivity of the target battery through the capacity temperature coefficient and the internal resistance temperature coefficient includes: defining the capacity temperature weight and the internal resistance temperature weight of the capacity temperature coefficient and the internal resistance temperature coefficient; analyzing the activation energy of the target battery; calculating the temperature sensitivity of the target battery according to the capacity temperature coefficient, the internal resistance temperature coefficient, the capacity temperature weight, the internal resistance temperature weight, and the activation energy, using the following formula:

[0030] wherein, represents the temperature sensitivity of the target battery, represents the capacity temperature coefficient of the target battery, represents a temperature coefficient of internal resistance of a target battery, represents a capacity temperature weight, represents an internal resistance temperature weight, represents an activation energy of a target battery, represents a reference temperature, represents a Boltzmann constant.

[0031] The capacity temperature weight refers to a parameter for quantifying the sensitivity of battery capacity to temperature change, the internal resistance temperature weight refers to a parameter for quantifying the sensitivity of battery internal resistance to temperature change, the activation energy refers to an energy barrier that must be overcome by reactant molecules in the electrochemical reaction of the battery, and the Boltzmann constant refers to a basic physical constant with a value of 1.380649 x 10-23 J / K.

[0032] It should be explained that in the calculation formula of the temperature sensitivity, the capacity temperature weight and the internal resistance temperature weight adjust the contribution ratio of capacity and internal resistance, adapt to different battery types (such as power batteries focusing on capacity and energy storage batteries focusing on internal resistance), and improve the calculation accuracy of the temperature sensitivity.

[0033] S2, analyzing the real-time health state and the battery chemical model of the target battery, and establishing a dynamic reference charging curve of the target battery based on the battery chemical model, the real-time health state, the capacity attenuation rate, the internal resistance change trend and the temperature sensitivity.

[0034] The present application analyzes the real-time health state and the battery chemical model of the target battery to provide a basis for later reference charging curve analysis.

[0035] In detail, the analysis of the real-time health state and the battery chemical model of the target battery comprises: calculating the internal resistance type SOH and the capacity type SOH of the target battery; combining the internal resistance type SOH and the capacity type SOH, outputting the real-time health state of the target battery; defining the P2D model of the target battery; analyzing the SEI film thickness and the negative electrode current density of the target battery; establishing the SEI film growth model of the target battery based on the SEI film thickness and the negative electrode current density; integrating the SEI film growth model into the P2D model to obtain the battery chemical model of the target battery.

[0036] ​P2D model refers to a mathematical model describing the internal electrochemical reactions of a lithium-ion battery, SEI film thickness refers to the physical thickness of the solid electrolyte interface film (SEI) on the negative electrode surface, negative electrode current density refers to the lithium ion insertion / extraction current per unit area of the negative electrode active material, SEI film growth model refers to a kinetic equation describing the thickening of the SEI film over time / cycles, battery chemistry model refers to a comprehensive model integrating the P2D basic framework and aging sub-models (such as SEI growth, lithium deposition), internal resistance type SOH refers to the state of health measured by the degree of internal resistance growth (100% represents the initial internal resistance, 0% corresponds to the end-of-life internal resistance), capacity type SOH refers to the state of health measured by the proportion of remaining available capacity (100% is the initial capacity, 80% is the end-of-life), real-time health status refers to a battery health evaluation index that comprehensively considers capacity and internal resistance decay.

[0037] Optionally, the P2D model of the target battery can be determined by Fick's law and Nernst-Planck equation.

[0038] Optionally, the analysis of the SEI film thickness of the target battery can be observed by TEM / SEM.

[0039] Further, the SEI film growth model of the target battery is established based on the SEI film thickness and the negative electrode current density, comprising: analyzing the state of charge of the target battery and determining the SOC sensitivity coefficient of the target battery to the state of charge; determining the pre-exponential factor of the target battery; According to the SEI film thickness, the negative electrode current density, the state of charge, the SOC sensitivity coefficient and the pre-exponential factor, the SEI film growth model of the target battery is constructed by the following formula, wherein the SEI film growth model is as follows:

[0040] wherein, SEI film thickness, pre-exponential factor, exponential function, activation energy of the target battery, universal gas constant, absolute temperature, negative electrode current density, SOC sensitivity coefficient, state of charge.

[0041] wherein the battery state of charge refers to the percentage of the current remaining capacity of the battery to the maximum available capacity, reflecting the charging and discharging degree of the battery, the SOC sensitive coefficient refers to the amplification effect of SOC on the growth rate of SEI, the pre-exponential factor refers to the reaction rate constant, indicating the basic growth rate of SEI per unit current density and per unit time, and the universal gas constant refers to a basic thermodynamic constant, which can be 8.314 J· The absolute temperature refers to the temperature in Kelvin (K), and the exponential function refers to the core term of the Arrhenius equation, which describes the exponential influence of temperature on the reaction rate.

[0042] Optionally, the determination of the SOC sensitive coefficient of the battery state of charge for the target battery is to cycle the battery at 50% and 80% of the battery state of charge respectively at the same temperature, measure the capacity attenuation difference, and fit the SOC sensitive coefficient.

[0043] It should be explained that the SOC sensitive coefficient introduced in the formula of the SEI film growth model quantifies the accelerated growth of SEI at high SOC, thereby improving the accuracy of the SEI film growth model.

[0044] Based on the battery chemical model, the real-time state of health, the capacity attenuation rate, the internal resistance change trend and the temperature sensitivity, the dynamic reference charging curve of the target battery is established, which ensures that the charging strategy is always accurately matched with the current actual condition of the battery, and fundamentally avoids the overcharging or undercharging problem caused by battery aging or environmental changes.

[0045] In detail, based on the battery chemical model, the real-time state of health, the capacity attenuation rate, the internal resistance change trend and the temperature sensitivity, the dynamic reference charging curve of the target battery is established, which includes: Based on the real-time state of health, the battery chemical model is aged and calibrated to obtain a calibrated battery chemical model; The capacity attenuation rate and the internal resistance change trend are integrated into the calibrated battery chemical model to obtain a performance analysis battery chemical model; According to the temperature sensitivity, a temperature-internal resistance correction table and a temperature-charging efficiency correction table of the performance analysis battery chemical model are established; Based on the temperature-internal resistance correction table and the temperature-charging efficiency correction table, the model correction parameters of the performance analysis battery chemical model are analyzed; Through the model correction parameters, the dynamic reference charging curve of the target battery is established.

[0046] ​The calibration battery chemical model refers to a mathematical model obtained by correcting and adjusting key parameters in a battery based on a basic battery chemical model (usually an idealized model in a new battery state) and a real-time health state of the battery, which can more accurately reflect the current aging level of the battery, the performance analysis battery chemical model refers to a mathematical model that can more comprehensively predict and evaluate the performance (such as power, endurance, and heat generation) of the battery in different use scenarios by further integrating the capacity attenuation rate and the internal resistance change trend of the battery into the calibration battery chemical model, the temperature-internal resistance correction table refers to a data table quantifying the influence of temperature on the internal resistance of the battery, the temperature-charging efficiency correction table refers to a data table quantifying the influence of temperature on the charging efficiency of the battery, the model correction parameter refers to a dynamically adjustable coefficient calculated by the temperature-internal resistance correction table and the temperature-charging efficiency correction table for integrating the influence of temperature into the performance analysis battery chemical model, and the dynamic reference charging curve refers to a reference trajectory of the optimal charging voltage and current changing over time at a specific time (at a specific SOH and a specific temperature) generated for the target battery after comprehensively integrating the calibrated chemical model, the performance analysis model, and all model correction parameters.

[0047] Optionally, the aging calibration of the battery chemical model based on the real-time health state can find a sensitive parameter for aging in the battery chemical model, and the sensitive parameter is corrected by using the real-time health state and a preset aging mapping relationship.

[0048] S3, define the charging efficiency threshold and IDR safety limit value of the target battery.

[0049] The charging efficiency threshold of the target battery defined in the application and the IDR safety limit value realize active protection and fine management of the charging process. The charging efficiency threshold refers to the minimum acceptable efficiency value of the battery system in the charging process, and the IDR safety limit value refers to the maximum capacity attenuation percentage allowed in a unit of time, which is used to prevent irreversible battery damage caused by a single charging process.

[0050] S4, real-time monitoring of battery charging data of the target battery charging process is used to analyze the current charging efficiency and instantaneous attenuation rate of the target battery, and a double-objective optimization function of the target battery is established when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit value.

[0051] It should be explained that the battery charging data refers to a set of physical quantities collected in real time during the charging process, reflecting the state of the battery and the charging behavior, such as electrical parameters, thermodynamic parameters, chemical state parameters, and aging indicators.

[0052] The analysis of the current charging efficiency and the instantaneous attenuation rate of the target battery provides a basis for later parameter optimization.

[0053] In detail, the analysis of the current charging efficiency and the instantaneous attenuation rate of the target battery comprises: According to the battery charging data of the target battery, the input energy and the effective energy storage of the target battery are calculated; Based on the input energy and the effective energy storage, the current charging efficiency of the target battery is calculated; An attenuation model of the target battery is established; The attenuation characteristics of the battery charging data are extracted; According to the attenuation characteristics, the instantaneous attenuation rate of the target battery is analyzed by using the attenuation model.

[0054] The input energy refers to the total electric energy actually input to the target battery by an external charging device (such as a charging pile or a vehicle-mounted charger) within a given charging time period, the effective energy storage refers to the chemical energy actually stored by the target battery through electrochemical reaction within the same charging time period, the current charging efficiency refers to the percentage of the effective energy storage of the target battery to the input energy within a specific charging cycle, the attenuation model refers to a regular model for describing and predicting the degradation of battery performance (such as capacity, internal resistance) with use (such as cycle number, storage time) or specific events (such as high-rate charging), the attenuation characteristics refer to quantitative indicators extracted from specific battery charging data, which can reflect the current aging state and instantaneous performance change, such as voltage characteristics, current characteristics, temperature characteristics, etc., and the instantaneous attenuation rate refers to the rate of irreversible capacity attenuation of the battery due to the applied voltage and current at any instant during the charging process.

[0055] Optionally, in the calculation of the input energy and the effective energy storage of the target battery according to the battery charging data of the target battery, the input energy is calculated by integrating the charging power with respect to time, and the effective energy storage is obtained by integrating the battery terminal voltage with respect to the charging capacity (current with respect to time).

[0056] Optionally, the calculation of the current charging efficiency of the target battery based on the input energy and the effective energy storage can be calculated by (effective energy storage / input energy) * 100%.

[0057] When the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit, the establishment of the double-objective optimization function of the target battery can achieve the balance of the battery life and the charging efficiency of the target battery.

[0058] In detail, when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit, the double-objective optimization function of the target battery is established, including: When the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit, the optimization target of the target battery is determined, wherein the optimization target includes maximizing charging performance and minimizing life loss; The decision variable of the target battery is analyzed; The constraint condition of the target battery is established, wherein the constraint condition includes hard constraint and soft constraint; The double-objective optimization function of the target battery is established based on the optimization target, the decision variable and the constraint condition.

[0059] Wherein, the maximum charging performance refers to minimizing the time required for the battery to charge from the current SOC to the target SOC (usually 80%) by optimizing the charging strategy under the premise of ensuring safety, the minimum life loss refers to inhibiting the irreversible damage of the battery aging mechanism by controlling the charging parameters, the decision variable refers to the set of controllable parameters that can be dynamically adjusted in the charging strategy, including constant current phase current, switching voltage of constant current to constant voltage and charging pulse interval, the hard constraint refers to the absolute physical limit that cannot be violated, the soft constraint refers to the performance boundary that allows temporary or slight deviation, and the double-objective optimization function refers to a multi-criteria decision model that optimizes charging speed and life at the same time.

[0060] Optionally, the double-objective optimization function of the target battery is established based on the optimization target, the decision variable and the constraint condition by NSGA-III algorithm.

[0061] S5, according to the real-time health state and the dynamic reference charging curve, the charging performance balancing parameters of the target battery are generated by using the double-objective optimization function, wherein the charging performance balancing parameters include optimized charging voltage, optimized charging current and optimized charging pulse interval.

[0062] According to the real-time health state and the dynamic reference charging curve, the charging performance balancing parameters of the target battery are generated by using the double-objective optimization function to realize the balance of battery life and charging performance of the target battery.

[0063] In detail, according to the real-time health state and the dynamic reference charging curve, the charging performance balancing parameters of the target battery are generated by using the double-objective optimization function, including: A double-objective Pareto solution set of the target battery is generated based on the double-objective optimization function; screening the dual-objective Pareto solution set according to the real-time health state and the dynamic reference charging curve to obtain a screened Pareto solution set; pruning the screened Pareto solution set in a target space to obtain a pruned Pareto solution set; iterating the pruned Pareto solution set to obtain a target Pareto solution set of the target battery, so as to output a charging performance balance parameter of the target battery.

[0064] The dual-objective Pareto solution set refers to a set of all decision variables (i.e., charging voltage, current, and pulse interval) that satisfy Pareto optimality in a multi-objective optimization problem. The screened Pareto solution set refers to a solution set obtained by filtering the initial Pareto solution set according to the real-time health state and the dynamic reference charging curve. The pruned Pareto solution set refers to a more refined solution set obtained by removing "similar performance" or "redundant" solutions through target space pruning technology based on the screened solution set. The target Pareto solution set refers to the final and most refined Pareto solution set determined after iteration optimization, which is used to output the charging performance balance parameter. The charging performance balance parameter refers to a specific and optimized charging strategy selected and output from the target Pareto solution set, which includes a set of specific numerical values: optimized charging voltage, optimized charging current, and optimized charging pulse interval. The optimized charging voltage refers to the highest allowable charging voltage dynamically adjusted according to the real-time state of the battery. The optimized charging current refers to the dynamic charging current value determined based on multi-objective optimization. The optimized charging pulse interval refers to the duration of the rest phase in pulse charging, which is used to alleviate polarization effect and lithium deposition.

[0065] Optionally, the screening of the dual-objective Pareto solution set according to the real-time health state and the dynamic reference charging curve to obtain a screened Pareto solution set can be implemented through a multi-level constraint filtering algorithm, including hard constraint filtering based on the dynamic reference charging curve and soft constraint based on the real-time health state.

[0066] Optionally, the pruning of the screened Pareto solution set in a target space to obtain a pruned Pareto solution set traverses the entire screened Pareto solution set and calculates the distance (e.g., Euclidean distance) between every two adjacent solutions. If the distance between two solutions in the target space (charging time vs. decay rate) is less than a preset threshold, one of them (e.g., the better one) is retained and the other is removed.

[0067] Firstly, the method breaks through the limitation of traditional fixed charging curve, can generate the optimal reference charging curve according to the real-time health status of the battery, capacity attenuation rate, internal resistance change trend and temperature sensitivity and other key parameters, ensures that the charging strategy is always accurately matched with the current actual condition of the battery, fundamentally avoids the overcharge or undercharge problem caused by battery aging or environmental change, secondly, by defining the charging efficiency threshold and IDR safety limit value, and combining the real-time monitoring data to trigger the double target optimization function, the application realizes the active protection and fine management of the charging process, when the battery performance index deviates from the safety range, the system can respond immediately, and the charging efficiency balancing parameter containing the optimized charging voltage, current and pulse interval is generated, which not only effectively inhibits the instantaneous attenuation of the battery in the charging process, significantly prolongs the cycle life of the battery, and maximizes the charging efficiency and energy recovery rate under the premise of ensuring the safety of the battery. Therefore, the application can improve the balanced optimization effect of battery life and charging efficiency.

[0068] As Figure 2 shown, it is a function module diagram of a battery life and charging efficiency balancing optimization system according to the application.

[0069] The battery life and charging efficiency balancing optimization system 200 can be installed in an electronic device. According to the realized functions, the battery life and charging efficiency balancing optimization system can include a battery data analysis module 201, a charging curve construction module 202, a battery limit value determination module 203, an optimization function construction module 204 and a battery balancing optimization module 205. The modules of the application can also be called units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0070] In the embodiment of the application, the functions of each module / unit are as follows: The battery data analysis module 201 is used to obtain the historical charge and discharge data of the target battery, so as to extract the capacity attenuation rate, internal resistance change trend and temperature sensitivity of the target battery; The charging curve construction module 202 is used to analyze the real-time health status and battery chemical model of the target battery, and establish a dynamic reference charging curve of the target battery based on the battery chemical model, the real-time health status, the capacity attenuation rate, the internal resistance change trend and the temperature sensitivity; The battery limit value determination module 203 is used to define the charging efficiency threshold and IDR safety limit value of the target battery; The optimization function construction module 204 is configured to monitor battery charging data of the target battery charging process in real time to analyze current charging efficiency and instantaneous attenuation rate of the target battery, and to establish a double-target optimization function of the target battery when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit value. The battery equalization optimization module 205 is configured to generate charging efficiency equalization parameters of the target battery by using the double-target optimization function according to the real-time health state and the dynamic reference charging curve, wherein the charging efficiency equalization parameters include an optimized charging voltage, an optimized charging current, and an optimized charging pulse interval.

[0071] In detail, the modules in the battery life and charging efficiency equalization optimization system 200 in the embodiments of the present application use the same technical means as the battery life and charging efficiency equalization optimization method in the above-mentioned Figure 1 application, and can produce the same technical effects, which will not be described here again.

[0072] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0073] Finally, it should be noted that in the above embodiments, each embodiment can be combined with or independent of each other, and deleting any one of them does not affect the technical implementation of the other embodiments. The above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for balancing optimization of battery life and charging efficiency, characterized by, The method comprises: acquiring historical charge-discharge data of a target battery to extract a capacity attenuation rate, an internal resistance change trend, and a temperature sensitivity of the target battery; analyzing a real-time health status of the target battery and a battery chemistry model, and establishing a dynamic reference charging curve of the target battery based on the battery chemistry model, the real-time health status, the capacity attenuation rate, the internal resistance change trend, and the temperature sensitivity; defining a charging efficiency threshold and an IDR safety limit of the target battery; real-time monitoring of battery charging data of the target battery in a charging process to analyze a current charging efficiency and an instantaneous attenuation rate of the target battery, and establishing a double-objective optimization function of the target battery when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit; generating charging performance balancing parameters of the target battery by using the double-objective optimization function according to the real-time health status and the dynamic reference charging curve, wherein the charging performance balancing parameters comprise an optimized charging voltage, an optimized charging current, and an optimized charging pulse interval.

2. The method of claim 1, wherein, The extraction of the capacity attenuation rate, the internal resistance change trend, and the temperature sensitivity of the target battery comprises: analyzing a cycle actual capacity, a dynamic internal resistance, and an ambient temperature of the target battery according to historical charge-discharge data of the target battery; fitting a capacity attenuation curve of the target battery based on the cycle actual capacity; analyzing a capacity attenuation rate of the target battery through the capacity attenuation curve; fitting an internal resistance growth model of the target battery according to the dynamic internal resistance to analyze an internal resistance change trend of the target battery; calculating a capacity temperature coefficient and an internal resistance temperature coefficient of the target battery based on the temperature sensitivity; analyzing the temperature sensitivity of the target battery through the capacity temperature coefficient and the internal resistance temperature coefficient.

3. The method of claim 2, wherein the battery life and charge efficiency balancing optimization is performed by a processor of the battery charger. The analysis of the temperature sensitivity of the target battery through the capacity temperature coefficient and the internal resistance temperature coefficient comprises: defining a capacity temperature weight and an internal resistance temperature weight of the capacity temperature coefficient and the internal resistance temperature coefficient; analyzing an activation energy of the target battery; calculating the temperature sensitivity of the target battery by using the following formula according to the capacity temperature coefficient, the internal resistance temperature coefficient, the capacity temperature weight, the internal resistance temperature weight, and the activation energy: wherein, represents the temperature sensitivity of the target battery, represents the capacity temperature coefficient of the target battery, represents the internal resistance temperature coefficient of the target battery, represents the capacity temperature weight, represents the internal resistance temperature weight, represents the activation energy of the target battery, represents the reference temperature, represents the Boltzmann constant.

4. The method of claim 3, wherein the battery life and charge efficiency balancing optimization is performed by a battery management system (BMS) of the battery pack. The analysis of the real-time health status of the target battery and the battery chemistry model comprises: calculating an internal resistance type SOH and a capacity type SOH of the target battery; outputting a real-time health status of the target battery in combination with the internal resistance type SOH and the capacity type SOH; defining a P2D model of the target battery; analyzing an SEI film thickness and a negative electrode current density of the target battery; establishing an SEI film growth model of the target battery based on the SEI film thickness and the negative electrode current density; integrating the SEI film growth model into the P2D model to obtain a battery chemistry model of the target battery.

5. The method of claim 4, wherein the battery life and charge efficiency balancing optimization is performed by a battery management system (BMS) of the battery pack. The establishment of the SEI film growth model of the target battery based on the SEI film thickness and the negative electrode current density comprises: analyzing a battery state of charge of the target battery, and determining a SOC sensitivity coefficient of the battery state of charge to the target battery; determining a pre-finger factor of the target battery; constructing an SEI film growth model of the target battery according to the SEI film thickness, the negative electrode current density, the battery state of charge, the SOC sensitivity coefficient, and the pre-finger factor.

6. The method of claim 5, wherein the battery life and charge performance balancing optimization is performed by a battery management system (BMS) of the battery pack. establishing a dynamic reference charging curve of the target battery based on the battery chemistry model, the real-time health state, the capacity attenuation rate, the internal resistance change trend, and the temperature sensitivity, including: performing aging calibration on the battery chemistry model based on the real-time health state to obtain a calibrated battery chemistry model; integrating the capacity attenuation rate and the internal resistance change trend into the calibrated battery chemistry model to obtain a performance analysis battery chemistry model; establishing a temperature-internal resistance correction table and a temperature-charging efficiency correction table of the performance analysis battery chemistry model according to the temperature sensitivity; analyzing model correction parameters of the performance analysis battery chemistry model based on the temperature-internal resistance correction table and the temperature-charging efficiency correction table; establishing the dynamic reference charging curve of the target battery through the model correction parameters.

7. The method of claim 6, wherein the battery life and charge efficiency balancing optimization is performed by a processor of the battery charger. the analysis of the current charging efficiency and the instantaneous attenuation rate of the target battery, including: calculating the input energy and the effective energy storage of the target battery according to the battery charging data of the target battery; calculating the current charging efficiency of the target battery based on the input energy and the effective energy storage; establishing an attenuation model of the target battery; extracting attenuation features of the battery charging data; analyzing the instantaneous attenuation rate of the target battery using the attenuation model according to the attenuation features.

8. The method of claim 7, wherein the battery life and charge efficiency balancing optimization is performed by a battery management system (BMS) of the battery pack. the establishment of the bi-objective optimization function of the target battery when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit, including: determining the optimization target of the target battery when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit, wherein the optimization target includes maximizing charging efficiency and minimizing life loss; analyzing decision variables of the target battery; establishing constraint conditions of the target battery, wherein the constraint conditions include hard constraints and soft constraints; establishing the bi-objective optimization function of the target battery based on the optimization target, the decision variables, and the constraint conditions.

9. The method of claim 8, wherein the battery life and charge efficiency balancing optimization is performed by a battery management system (BMS) of the battery pack. the generation of the charging efficiency balancing parameters of the target battery using the bi-objective optimization function according to the real-time health state and the dynamic reference charging curve, including: generating a bi-objective Pareto solution set of the target battery based on the bi-objective optimization function; screening the bi-objective Pareto solution set according to the real-time health state and the dynamic reference charging curve to obtain a screened Pareto solution set; performing target space pruning on the screened Pareto solution set to obtain a pruned Pareto solution set; The pruned Pareto solution set is iterated to obtain a target Pareto solution set of the target battery, so as to output a charging performance balancing parameter of the target battery.

10. A battery life and charge efficiency balancing optimization system, characterized by, The system comprises: a battery data analysis module configured to acquire historical charge-discharge data of a target battery, so as to extract a capacity attenuation rate, a trend of internal resistance change, and temperature sensitivity of the target battery; a charging curve construction module configured to analyze a real-time health state and a battery chemistry model of the target battery, and to establish a dynamic reference charging curve of the target battery based on the battery chemistry model, the real-time health state, the capacity attenuation rate, the trend of internal resistance change, and the temperature sensitivity; a battery limit value determination module configured to define a charging efficiency threshold and an IDR safety limit value of the target battery; an optimization function construction module configured to monitor battery charging data of a charging process of the target battery in real time, so as to analyze a current charging efficiency and an instantaneous attenuation rate of the target battery, and to establish a double-objective optimization function of the target battery when the current charging efficiency is lower than the charging efficiency threshold or the instantaneous attenuation rate is higher than the IDR safety limit value; a battery balancing optimization module configured to generate a charging performance balancing parameter of the target battery by using the double-objective optimization function according to the real-time health state and the dynamic reference charging curve, wherein the charging performance balancing parameter comprises an optimized charging voltage, an optimized charging current, and an optimized charging pulse interval.

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