Multi-parameter adaptive equalization control method and system for hybrid energy storage system

Abstract

The present application relates to the technical field of battery management and equalization control, and particularly relates to a multi-parameter adaptive equalization control method and system for a hybrid energy storage system, comprising: obtaining real-time voltage, current and temperature of a battery pack, combining temperature and current compensation coefficients calibrated by experiments and a battery rated internal resistance to calculate a dynamic voltage index of each battery. An Arrhenius formula is fitted through a cycle aging experiment at different temperatures to obtain a battery aging activation energy, and then a historical health index is calculated in combination with a historical average temperature and a cycle number. An equalization demand index is obtained by dividing the difference between the dynamic voltage index and the average value by the historical health index. Finally, the index is input as an error signal into an adaptive equalization current control module to generate a smooth equalization current of each battery in real time through proportional, integral and differential operations. The present application improves the accuracy of equalization control.

Description

Technical Field

[0001] This invention relates to the field of battery management and equalization control technology, and in particular to a multi-parameter adaptive equalization control method and system for hybrid energy storage systems. Background Technology

[0002] In the field of battery energy storage systems, especially in battery packs composed of multiple individual cells connected in series, there is a common problem of battery inconsistency due to differences in manufacturing processes, usage environments, and chemical properties. This inconsistency manifests as differences in voltage, capacity, internal resistance, and aging rate, which accumulate during cyclic charging and discharging, causing some batteries to be prematurely charged or discharged, leading to overcharging and over-discharging. This not only reduces the overall usable capacity but also accelerates battery aging, shortens lifespan, and may pose safety hazards.

[0003] Current widely used equalization control technologies are mainly based on threshold comparisons of individual cell voltages, achieving equalization through passive dissipation or active energy transfer. However, the core drawback of this approach is its reliance on a single parameter—real-time voltage measurement—for decision-making. Battery terminal voltage is susceptible to transient current and temperature disturbances, failing to accurately reflect the true state of charge and leading to misjudgments in equalization. Furthermore, existing methods adopt an indiscriminate strategy for all cells, ignoring individual differences in battery health and failing to provide special protection for rapidly aging "weak" cells. This may even accelerate their degradation, ultimately limiting the lifespan of the entire battery pack.

[0004] In summary, existing equalization control technologies suffer from low equalization accuracy and poor response due to their reliance on single parameters, inaccurate state estimation, rigid strategies, and lack of integration of battery dynamic operating conditions and historical health status. This makes it difficult to effectively maintain long-term battery pack consistency and extend overall lifespan. Therefore, there is an urgent need to develop an intelligent equalization control method that can integrate multi-source parameters, adaptively adjust, and possess battery health status sensing capabilities to overcome the current technological bottlenecks. Summary of the Invention

[0005] This invention provides a multi-parameter adaptive equalization control method and system for hybrid energy storage systems, which solves the problems of low equalization accuracy, high misjudgment rate and shortened battery life caused by relying on a single voltage parameter and ignoring the battery health status and dynamic changes in operating conditions in existing battery equalization control.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] The first aspect of this invention is to provide a multi-parameter adaptive equalization control method for a hybrid energy storage system, comprising:

[0008] Obtain the real-time voltage, real-time current, and real-time temperature of each cell in the battery pack of the hybrid energy storage system;

[0009] Obtain the temperature compensation coefficient and current compensation coefficient; based on the real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient, and current compensation coefficient of each battery, obtain the dynamic voltage index of each battery.

[0010] Cyclic aging experiments were conducted on the batteries at multiple different constant temperatures to obtain the capacity decay rate. Then, the experimental data were fitted using the Arrhenius formula based on the capacity decay rate to obtain the activation energy of the battery aging reaction. Based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery, the historical health index of each battery was obtained.

[0011] The balanced demand index of each battery is obtained by using the difference between the dynamic voltage index of each battery and the mean of the dynamic voltage index of all batteries, and the historical health index of each battery.

[0012] The balanced current of each battery is obtained by using the balanced demand index of each battery cell as an input signal for feedback adjustment.

[0013] Furthermore, obtaining the temperature compensation coefficient and the current compensation coefficient includes:

[0014] Through battery testing experiments, the measured voltage and the actual open-circuit voltage are obtained. By analyzing the relationship between the system deviation between the measured voltage and the actual open-circuit voltage and the current and temperature, respectively, the temperature compensation coefficient and the current compensation coefficient are obtained through curve fitting.

[0015] Furthermore, the dynamic voltage index of each battery is obtained based on its real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient, and current compensation coefficient, specifically expressed by the formula:

[0016]

[0017] In the formula, Indicates the first Real-time voltage of the battery. Indicates the first Real-time current of the battery Indicates the first The rated internal resistance of the battery. Indicates the first Real-time temperature of the battery. Indicates reference temperature. The temperature coefficient representing the battery voltage; Indicates the current compensation coefficient. Indicates the temperature compensation coefficient. Indicates the first The dynamic voltage index of the battery.

[0018] Furthermore, the Arrhenius formula is specifically expressed as:

[0019]

[0020] In the formula, This represents the activation energy of the battery aging reaction. This represents the pre-exponential factor, a constant related to the reaction; Represents the universal gas constant. Represents thermodynamic temperature. Indicates the capacity decay rate. This represents an exponential function with the natural constant as its base.

[0021] Furthermore, the historical health index of each battery is obtained based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery. This is specifically expressed by the following formula:

[0022]

[0023] In the formula, This represents the activation energy of the battery aging reaction. Represents the universal gas constant. Indicates the first Historical average temperature of batteries Indicates the first The number of charge-discharge cycles a battery has undergone. Represents the aging rate constant. Indicates the first Historical health index of batteries This represents an exponential function with the natural constant as its base.

[0024] Furthermore, the equilibrium demand index for each battery is obtained based on the difference between the dynamic voltage index of each battery and the mean of the dynamic voltage indices of all batteries, and the historical health index of each battery. This is specifically expressed by the formula:

[0025]

[0026] In the formula, Indicates the first The dynamic voltage index of the battery. This represents the average dynamic voltage index of all batteries. Indicates the first Historical health index of batteries Indicates the first The balanced demand index for batteries.

[0027] Furthermore, the process of using the balanced demand index of each battery cell as an input signal for feedback adjustment to obtain the balanced current of each battery cell is specifically expressed by the following formula:

[0028]

[0029] In the formula, Indicates the first The balanced demand index for batteries. Indicates the initial moment of system operation. Indicates the current moment. Indicates proportional gain. Indicates integral gain. Represents differential gain. Indicates the first The equalization current of the battery; among which, express Integral over time, express The derivative with respect to time;

[0030] The proportional gain, integral gain, and derivative gain were obtained through experimental optimization.

[0031] A second aspect of the present invention is to provide a multi-parameter adaptive equalization control system for a hybrid energy storage system, comprising:

[0032] Data acquisition module: used to acquire the real-time voltage, real-time current and real-time temperature of each cell in the hybrid energy storage system battery pack;

[0033] Dynamic voltage analysis module: used to obtain temperature compensation coefficient and current compensation coefficient; based on the real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient and current compensation coefficient of each battery, the dynamic voltage index of each battery is obtained.

[0034] Battery health assessment module: Used to conduct cyclic aging experiments on batteries at multiple different constant temperatures to obtain the capacity decay rate. Then, the experimental data is fitted using the Arrhenius formula based on the capacity decay rate to obtain the activation energy of the battery aging reaction. Based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery, the historical health index of each battery is obtained.

[0035] Battery equalization demand analysis module: used to obtain the equalization demand index of each battery based on the difference between the dynamic voltage index of each battery and the average dynamic voltage index of all batteries, and the historical health index of each battery.

[0036] Balanced current control module: Used to obtain the balanced current of each battery cell by using the balanced demand index of each battery cell as an input signal for feedback adjustment.

[0037] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-parameter adaptive equalization control method for a hybrid energy storage system.

[0038] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multi-parameter adaptive equalization control method for a hybrid energy storage system.

[0039] Compared with existing technologies, the beneficial effects of this invention are: obtaining the real-time voltage, real-time current, and real-time temperature of each cell in the hybrid energy storage system battery pack; obtaining the real-time voltage, current, and temperature of the battery provides a real and dynamic data foundation for all subsequent accurate calculations; obtaining temperature compensation coefficients and current compensation coefficients; obtaining the dynamic voltage index of each cell based on its real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient, and current compensation coefficient; calculating the dynamic voltage index can eliminate the interference of instantaneous current and temperature on voltage measurement, obtaining an accurate voltage reference closer to the true state of charge; conducting cyclic aging experiments on the battery at multiple different constant temperatures to obtain the capacity decay rate, and then fitting the experimental data using the Arrhenius formula based on the capacity decay rate to obtain the activation energy of the battery aging reaction; and obtaining the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each cell, and the dynamic voltage index of each cell. The number of charge-discharge cycles a battery has undergone yields a historical health index for each cell. Calculating this historical health index quantifies the degree of battery aging and identifies "weak" cells requiring special protection. Based on the difference between the dynamic voltage index of each cell and the average dynamic voltage index of all cells, along with the historical health index of each cell, a balancing demand index for each cell is obtained. Calculating this balancing demand index intelligently generates differentiated balancing priority commands by considering both current voltage deviation and battery health status. The balancing demand index of each cell is used as an input signal for feedback adjustment to obtain the balancing current for each cell. Generating this balancing current transforms intelligent commands into smooth, adaptive, and shock-free control actions, achieving efficient and gentle balancing execution. This solves the problems of low balancing accuracy, high misjudgment rate, and shortened battery pack life caused by relying on a single voltage parameter and ignoring battery health status and dynamic changes in operating conditions in existing battery balancing control systems. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This invention provides a flowchart illustrating the steps of a multi-parameter adaptive equalization control method for a hybrid energy storage system.

[0042] Figure 2 This invention provides a schematic flowchart of a multi-parameter adaptive equalization control system for a hybrid energy storage system. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0044] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0045] To address the problems existing in the background technology, a multi-parameter adaptive equilibrium control method and system for hybrid energy storage systems are designed, which has important practical significance.

[0046] like Figure 1 As shown, the first aspect of the present invention is to provide a multi-parameter adaptive equalization control method for a hybrid energy storage system, comprising the following steps:

[0047] Step S001: Obtain the real-time voltage, real-time current, and real-time temperature of each cell in the hybrid energy storage system battery pack.

[0048] It should be noted that, in order to achieve precise and adaptive intelligent balancing control of the battery pack, thereby addressing the misbalancing caused by a single voltage criterion and the bottleneck effect caused by ignoring differences in battery health, the system needs to collect the following three types of core data: First, to eliminate the interference of instantaneous operating conditions on voltage measurement and obtain a voltage reference closer to the true state of charge, the real-time voltage and real-time current of each battery cell need to be collected; second, to compensate for the impact of temperature on the battery open-circuit voltage and aging rate, the real-time temperature of each battery cell needs to be collected; finally, to quantify the historical degradation degree of the battery, identify the "bottleneck" battery, and provide priority weights for the balancing strategy, its historical cycle count and historical average temperature data also need to be obtained. The fusion of these multi-source data is the foundation for this method to shift from "passive response" to "intelligent prediction and protection".

[0049] Specifically, the real-time voltage, real-time current, and real-time temperature of each battery are collected.

[0050] Step S002: Obtain the temperature compensation coefficient and the current compensation coefficient; based on the real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient, and current compensation coefficient of each battery, obtain the dynamic voltage index of each battery.

[0051] It should be noted that, in order to eliminate the interference of voltage measurement values ​​caused by the ohmic internal resistance voltage drop due to instantaneous high current and open-circuit voltage drift caused by temperature changes during battery operation, and thus obtain a stable voltage estimation benchmark that is closer to the battery's true state of charge (SOC), the system needs to perform dynamic voltage index calculation. This step involves subtracting the voltage drop component calculated from current and internal resistance from the real-time measured voltage, and compensating for the voltage change component caused by temperature deviation from the reference value, ultimately outputting a condition-compensated "dynamic voltage index". This processing is to avoid "misjudgment" and "incorrect equalization" caused by directly using the disturbed terminal voltage for equalization decisions, and to provide accurate and reliable voltage state input for subsequent equalization demand analysis.

[0052] Specifically, through battery testing experiments, the measured voltage and the actual open-circuit voltage are obtained. By analyzing the relationship between the system deviation between the measured voltage and the actual open-circuit voltage and the current and temperature, respectively, temperature compensation coefficients and current compensation coefficients are obtained through curve fitting. The curve fitting here is performed using the least squares method; the least squares method is a well-known technique and will not be elaborated upon here.

[0053] The dynamic voltage index of each battery is obtained based on its real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient, and current compensation coefficient. The specific formula for the dynamic voltage index of each battery is as follows:

[0054]

[0055] In the formula, Indicates the first Real-time voltage of the battery. Indicates the first Real-time current of the battery Indicates the first Rated internal resistance of the battery (from the battery manufacturer's datasheet). Indicates the first Real-time temperature of the battery. Indicates reference temperature. Temperature coefficient representing battery voltage (from battery manufacturer's datasheet); Indicates the current compensation coefficient. Indicates the temperature compensation coefficient. Indicates the first The dynamic voltage index of the battery.

[0056] in, It represents the estimated ohmic voltage drop across the internal resistance of the battery cell caused by the current flowing through it. It quantifies the amount of interference that causes the measured voltage of the battery cell to deviate from its true internal potential (open circuit voltage) due to the instantaneous action of the operating current. This represents an estimate of the change in open circuit voltage (OCV) due to the battery's current temperature deviating from the reference temperature. It quantifies the systematic shift in the battery's internal chemical potential caused by temperature effects. The original model is complex, so we simplify it by ignoring the RC network (Resistor-Capacitor Network) and focusing on ohmic drop and temperature effects. The improvement lies in explicitly adding a temperature compensation term using the voltage temperature coefficient, making DVI closer to OCV.

[0057] Thus, the dynamic voltage index of each battery cell is obtained using the above method.

[0058] Step S003: Conduct cyclic aging experiments on the battery at multiple different constant temperatures to obtain the capacity decay rate. Then, fit the experimental data using the Arrhenius formula based on the capacity decay rate to obtain the activation energy of the battery aging reaction. Based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery, obtain the historical health index of each battery.

[0059] It's important to note that, in order to identify the "weakest link" cells in the battery pack that are aging faster due to historical cumulative losses, and to give them higher protection priority in the equalization control, thereby preventing the lifespan of the entire battery pack from being limited by the weakest cell, the system needs to calculate a historical health index. This step integrates the number of charge-discharge cycles the battery has undergone with historical average temperature data, and quantifies the "health score" of each battery based on the Arrhenius model, which describes the kinetics of battery aging. This process transforms the equalization strategy from "treating all batteries equally without discrimination" to "differentiated protection based on health status," providing key inputs reflecting the long-term "health" of the battery for core equalization decisions.

[0060] Specifically, the battery was subjected to cycle aging experiments at multiple different constant temperatures to obtain the capacity decay rate. Then, the experimental data was fitted using the Arrhenius formula based on the capacity decay rate to obtain the activation energy of the battery aging reaction; whereby the Arrhenius formula is specifically expressed as:

[0061]

[0062] In the formula, This represents the activation energy of the battery aging reaction. This indicates the pre-exponential factor (or frequency factor), which is a constant related to the reaction; Represents the universal gas constant. Represents thermodynamic temperature. Indicates the capacity decay rate. This represents an exponential function with the natural gas constant as its base. The specific value of the universal gas constant is 8.314. .

[0063] The historical health index of each battery is obtained based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery. The specific formula for the historical health index of each battery is as follows:

[0064]

[0065] In the formula, This represents the activation energy of the battery aging reaction. Represents the universal gas constant. Indicates the first Historical average temperature of batteries Indicates the first The number of charge-discharge cycles a battery has undergone. This represents the aging rate constant (the unit is the reciprocal of the number of charge-discharge cycles, representing the amount of capacity decay caused by each cycle, obtained through fitting from aging tests). Indicates the first Historical health index of batteries This represents an exponential function with the natural constant as its base.

[0066] in, This indicates the activation energy required for the battery aging reaction. The average energy that thermal motion can provide at a specific historical average temperature. The scale of comparison; the larger the ratio, the higher the reaction energy barrier relative to thermal energy, the more difficult the aging reaction is to occur, that is, the slower the theoretical aging rate of the battery under that temperature history, and the higher the historical health index of each battery; conversely, the smaller the ratio, the faster the theoretical aging rate, and the lower the historical health index of each battery. It is a linearized measure of the “total aging drive” or “cumulative aging effect” of each battery to date due to the number of cycles it has been used. The more severe the “total aging drive” or “cumulative aging effect”, the smaller the historical health index of each battery, and vice versa.

[0067] Thus, the historical health index of each battery is obtained through the above method.

[0068] Step S004: Based on the difference between the dynamic voltage index of each battery and the average dynamic voltage index of all batteries, and the historical health index of each battery, obtain the balanced demand index of each battery.

[0069] It's important to note that in order to generate an intelligent balancing command that reflects both the current voltage deviation of the battery and dynamically weights it based on its health status, thereby achieving priority protection and precise charge distribution for aging batteries, the system needs to calculate a balancing demand index. This step first calculates the average of the compensated dynamic voltage indices of all batteries, and then, for each battery, divides the difference between its dynamic voltage index and the average by its historical health index. The core logic of this operation is to use the health index as the denominator to "amplify" the voltage deviation of aging batteries, ensuring that even slight voltage deviations are identified as high-priority demands and trigger stronger balancing interventions. This transforms the traditional "egalitarian" balancing into a differentiated protection strategy that "supports the weak and suppresses the strong."

[0070] Specifically, the equilibrium demand index for each battery is obtained based on the difference between the dynamic voltage index of each battery and the mean of the dynamic voltage indices of all batteries, and the historical health index of each battery. The equilibrium demand index for each battery is expressed by the following formula:

[0071]

[0072] In the formula, Indicates the first The dynamic voltage index of the battery. This represents the average dynamic voltage index of all batteries. Indicates the first Historical health index of batteries Indicates the first The balanced demand index for batteries.

[0073] in, This represents the difference between the dynamic voltage index of each battery and the average dynamic voltage index of all batteries. A larger difference (greater than 0) indicates that the battery needs to be discharged to lower its voltage and achieve equalization; a smaller difference (less than 0) indicates that the battery needs to be charged to raise its voltage and achieve equalization. A lower historical health index for each battery indicates a less healthy battery, thus requiring a larger equalization demand index for adjustment; conversely, a higher historical health index indicates a healthier battery, thus requiring a smaller equalization demand index for adjustment.

[0074] Thus, the balanced demand index for each battery is obtained through the above method.

[0075] Step S005: Use the equalization demand index of each battery cell as an input signal for feedback adjustment to obtain the equalization current of each battery cell.

[0076] It should be noted that, in order to transform the balancing command reflecting the individual differences in battery needs into a smooth, stable, and rapid control action that can eliminate deviations, thereby avoiding the current surges, voltage oscillations, and electromagnetic interference problems caused by traditional on-off balancing, the system needs to perform PID calculation and execution of the balancing current. This step uses the balancing demand index as the input signal, employs a proportional element to quickly respond to the magnitude of the deviation, an integral element to continuously eliminate accumulated deviations, and a derivative element to suppress overshoot and oscillations, ultimately outputting a continuous and adaptive balancing current command. This process transforms the balancing execution process from a "crucial start-stop" to a "fine and smooth" continuous adjustment, effectively achieving the balancing goal while minimizing electrical and thermal stress on the battery, thereby improving balancing efficiency and extending battery life.

[0077] Specifically, the equalization demand index of each battery cell is used as an input signal for feedback adjustment to obtain the equalization current of each battery cell; wherein the equalization current of each battery cell is expressed by the formula:

[0078]

[0079] In the formula, Indicates the first The balanced demand index for batteries. Indicates the initial moment of system operation. Indicates the current moment. Indicates proportional gain. Indicates integral gain. Represents differential gain. Indicates the first The equalization current of the battery. Among them, express Integral over time, express The derivative with respect to time. The proportional gain, integral gain, and differential gain were obtained through experimental optimization.

[0080] in, The unit is defined as , The unit is defined as (Siemens, the unit of electrical conductivity) The unit is defined as ， The unit is defined as . This indicates the corrective force for the current instantaneous deviation; it directly generates a balanced current proportional to the combined deviation of the battery's voltage and health status at this moment. The larger the deviation, the stronger the instantaneous corrective force, achieving rapid control. This represents the corrective force against historical cumulative biases; it continuously accumulates and amplifies all historical biases of the battery from the past to the present. Even if the current bias is small, as long as historical imbalances have existed for a long time, this corrective force will continue to grow until all cumulative biases are completely eliminated, preventing long-term inconsistencies. This represents the suppression or boosting force against future deviation trends; it operates based on the current rate of change of the battery deviation. When the deviation increases rapidly, it generates a reverse suppressive force to "brake" it; when the deviation decreases rapidly, it generates a positive boosting force to "prevent hysteresis," effectively smoothing the control process and avoiding oscillations. This formula is derived from an improved PID control algorithm. Traditional equalization often uses switching control, leading to sudden current changes and voltage fluctuations. PID (Proportional-Integral-Derivative) control provides a smooth and adaptive equalization current, reducing stress. The integral term ensures long-term equalization, and the derivative term suppresses overshoot, thus achieving smoother equalization and longer battery life.

[0081] Thus, the balanced current of each battery is obtained through the above method.

[0082] To verify the effectiveness of this scheme, the following experimental data will be used for illustration.

[0083] Two experimental groups were set up: a control group for the traditional method and an experimental group for this scheme.

[0084] 1. Experimental objective.

[0085] Under accelerated aging conditions, the performance of the MAB-HESS method and the traditional voltage-based equalization method in maintaining battery pack consistency and delaying capacity decay were compared and verified.

[0086] 2. Experiment setup.

[0087] Test subjects: Two identical 4-cell 18650 lithium-ion battery packs connected in series (battery pack A and battery pack B).

[0088] Initial state: Both battery packs have been screened and have the same initial capacity and internal resistance (capacity: 2500mAh±10mAh; internal resistance: 30mΩ±2mΩ).

[0089] Environment: Constant temperature chamber, set at 45 degrees Celsius to accelerate battery aging.

[0090] Cyclic operation: 1000 charge-discharge cycles. Each cycle includes: 1C constant current charging to 16.8V, resting for 10 minutes, and 1C constant current discharging to 12.0V.

[0091] Equilibrium methods:

[0092] Battery pack A (control group): The traditional voltage balancing method is used. When the voltage difference between any battery and the average voltage of the pack exceeds 20mV, a fixed discharge current of 100mA is started to balance the high-voltage batteries.

[0093] Battery pack B (experimental group): The MAB-HESS method of this invention is used to calculate and control the balancing current in real time.

[0094] 3. Evaluation indicators.

[0095] Consistency effect: The average standard deviation of the battery pack voltage over the entire testing period. The smaller this value, the better the consistency within the pack.

[0096] Lifetime retention: The percentage of total usable capacity retained by the battery pack after 1000 cycles. The higher this value, the more effective the method is in extending lifespan.

[0097] 4. Experimental results and data analysis.

[0098]

[0099] Interpretation of Results:

[0100] In terms of balancing performance: The MAB-HESS method significantly reduces voltage inconsistency within the battery pack by approximately 66%. This is because traditional methods only observe the "apparent voltage," while MAB-HESS compensates for voltage measurement errors caused by current and temperature through (DVI) and intelligently adjusts balancing priorities by combining (EQI) with the state of health (HHI), thereby achieving more accurate charge redistribution.

[0101] Regarding lifespan extension: The MAB-HESS (Multi-parameter Adaptive Balancing method for Hybrid Energy Storage Systems) method enabled the battery pack to retain an additional 7% of its capacity after 1000 harsh cycles. This is a very significant improvement. The fundamental reason for this is:

[0102] A more balanced state reduces the risk of overcharging and over-discharging some batteries.

[0103] The key lies in the application of (HHI), which identifies batteries that age faster (e.g., batteries in high-temperature environments) and assigns them a higher balancing weight in (EQI), providing focused protection for these "weak" batteries. Traditional methods, on the other hand, treat all batteries "equally," leading to accelerated degradation of weak cells and ultimately dragging down the entire battery pack.

[0104] The smooth and balanced current provided by (PID control) is less impactful on the battery than the sudden large current of traditional switching, which is beneficial to its lifespan.

[0105] This experiment, through simple numerical comparison, clearly demonstrates that the MAB-HESS equalization control method proposed in this invention is significantly superior to traditional voltage equalization methods in maintaining battery pack consistency and extending system lifespan. This is attributed to the algorithm's innovative integration of multiple parameters, such as voltage, current, temperature, and historical data, into an adaptive control framework, achieving a leap from "passive response" to "intelligent prediction and protection." The experimental results fully validate the practicality, advancement, and inventiveness of this method.

[0106] This concludes the embodiment.

[0107] like Figure 2 As shown, a second aspect of the present invention is to provide a multi-parameter adaptive equalization control system for a hybrid energy storage system, comprising:

[0108] Data acquisition module 101: used to acquire the real-time voltage, real-time current and real-time temperature of each cell in the battery pack of the hybrid energy storage system;

[0109] Dynamic voltage analysis module 102: used to obtain temperature compensation coefficient and current compensation coefficient; based on the real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient and current compensation coefficient of each battery, to obtain the dynamic voltage index of each battery.

[0110] Battery health assessment module 103: used to conduct cyclic aging experiments on batteries at multiple different constant temperatures to obtain the capacity decay rate, and then use the Arrhenius formula to fit the experimental data according to the capacity decay rate to obtain the activation energy of the battery aging reaction; based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery and the number of charge-discharge cycles experienced by each battery, the historical health index of each battery is obtained.

[0111] Battery equalization demand analysis module 104: used to obtain the equalization demand index of each battery based on the difference between the dynamic voltage index of each battery and the average dynamic voltage index of all batteries, and the historical health index of each battery.

[0112] Balanced current control module 105: Used to obtain the balanced current of each battery by using the balanced demand index of each battery as an input signal for feedback adjustment.

[0113] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a multi-parameter adaptive equalization control method for a hybrid energy storage system.

[0114] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a multi-parameter adaptive equalization control method for a hybrid energy storage system.

[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0116] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A multi-parameter adaptive equalization control method for a hybrid energy storage system, characterized in that, include: Obtain the real-time voltage, real-time current, and real-time temperature of each cell in the battery pack of the hybrid energy storage system; Obtain the temperature compensation coefficient and the current compensation coefficient; The dynamic voltage index of each battery is obtained based on its real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient, and current compensation coefficient. Cyclic aging experiments were conducted on the batteries at multiple different constant temperatures to obtain the capacity decay rate. Then, the experimental data were fitted using the Arrhenius formula based on the capacity decay rate to obtain the activation energy of the battery aging reaction. Based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery, the historical health index of each battery was obtained. The balanced demand index for each battery is obtained by using the difference between the dynamic voltage index of each battery and the mean of the dynamic voltage index of all batteries, and the historical health index of each battery. The balanced current of each battery is obtained by using the balanced demand index of each battery as an input signal for feedback adjustment. The dynamic voltage index of each battery is obtained based on its real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient, and current compensation coefficient. This is expressed by the following formula: In the formula, Indicates the first Real-time voltage of the battery. Indicates the first Real-time current of the battery Indicates the first The rated internal resistance of the battery. Indicates the first Real-time temperature of the battery. Indicates reference temperature. The temperature coefficient representing the battery voltage; Indicates the current compensation coefficient. Indicates the temperature compensation coefficient. Indicates the first The dynamic voltage index of the battery; The historical health index of each battery is obtained based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery. This is specifically expressed by the following formula: In the formula, This represents the activation energy of the battery aging reaction. Represents the universal gas constant. Indicates the first Historical average temperature of batteries Indicates the first The number of charge-discharge cycles a battery undergoes. Represents the aging rate constant. Indicates the first Historical health index of batteries This represents an exponential function with the natural constant as its base.

2. The multi-parameter adaptive equalization control method for a hybrid energy storage system according to claim 1, characterized in that, The acquisition of temperature compensation coefficient and current compensation coefficient includes: Through battery testing experiments, the measured voltage and the actual open-circuit voltage are obtained. By analyzing the relationship between the system deviation between the measured voltage and the actual open-circuit voltage and the current and temperature, respectively, the temperature compensation coefficient and the current compensation coefficient are obtained through curve fitting.

3. The multi-parameter adaptive equalization control method for a hybrid energy storage system according to claim 1, characterized in that, The Arrhenius formula is specifically expressed as follows: In the formula, This represents the activation energy of the battery aging reaction. This represents the pre-exponential factor, a constant related to the reaction; Represents the universal gas constant. Represents thermodynamic temperature. Indicates the capacity decay rate. This represents an exponential function with the natural constant as its base.

4. The multi-parameter adaptive equalization control method for a hybrid energy storage system according to claim 1, characterized in that, The equilibrium demand index for each battery is obtained based on the difference between the dynamic voltage index of each battery and the mean of the dynamic voltage indices of all batteries, and the historical health index of each battery. This is expressed by the following formula: In the formula, Indicates the first The dynamic voltage index of the battery. This represents the average dynamic voltage index of all batteries. Indicates the first Historical health index of batteries Indicates the first The balanced demand index for batteries.

5. The multi-parameter adaptive equalization control method for a hybrid energy storage system according to claim 1, characterized in that, The process involves using the balanced demand index of each battery cell as an input signal for feedback adjustment to obtain the balanced current of each battery cell, specifically expressed by the formula: In the formula, Indicates the first The balanced demand index for energy-saving batteries. Indicates the initial moment of system operation. Indicates the current moment. Indicates proportional gain. Indicates integral gain. Represents differential gain. Indicates the first The equalization current of the battery; among which, express Integral over time, express The derivative with respect to time; The proportional gain, integral gain, and derivative gain were obtained through experimental optimization.

6. A multi-parameter adaptive equalization control system for a hybrid energy storage system, employing the multi-parameter adaptive equalization control method described in any one of claims 1-5, characterized in that, include: Data acquisition module: used to acquire the real-time voltage, real-time current and real-time temperature of each cell in the hybrid energy storage system battery pack; Dynamic voltage analysis module: used to obtain temperature compensation coefficient and current compensation coefficient; based on the real-time voltage, real-time current, real-time temperature, rated internal resistance, temperature compensation coefficient and current compensation coefficient of each battery, the dynamic voltage index of each battery is obtained. Battery health assessment module: Used to conduct cyclic aging experiments on batteries at multiple different constant temperatures to obtain the capacity decay rate. Then, the experimental data is fitted using the Arrhenius formula based on the capacity decay rate to obtain the activation energy of the battery aging reaction. Based on the activation energy of the battery aging reaction, the universal gas constant, the historical average temperature of each battery, and the number of charge-discharge cycles experienced by each battery, the historical health index of each battery is obtained. Battery equalization demand analysis module: used to obtain the equalization demand index of each battery based on the difference between the dynamic voltage index of each battery and the average dynamic voltage index of all batteries, and the historical health index of each battery. Balanced current control module: Used to obtain the balanced current of each battery cell by using the balanced demand index of each battery cell as an input signal for feedback adjustment.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-parameter adaptive equalization control method for a hybrid energy storage system according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the multi-parameter adaptive equalization control method for a hybrid energy storage system according to any one of claims 1-5.

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