CN-121770103-B - Multi-parameter self-adaptive equalization control method and system for hybrid energy storage system

Abstract

The invention relates to the technical field of battery management and equalization control, in particular to a multi-parameter self-adaptive equalization control method and system of a hybrid energy storage system. And fitting Arrhenius formulas through cyclic aging experiments at different temperatures to obtain the battery aging activation energy, and calculating the historical health index by combining the historical average temperature and the cycle times. And dividing the difference value between the dynamic voltage index and the average value by the historical health index to obtain the balanced demand index. And finally, inputting the index as an error signal into an adaptive equalization current control module, and generating the smooth equalization current of each battery in real time through proportional, integral and differential operations. The invention improves the accuracy of the balance control.

Inventors

• LI ZHIPENG
• ZHANG LISONG
• Dai Benqian
• HE TING
• Gao Yushuan
• Lou Fangxi
• JIANG NAN
• XU HAITAO
• LI SHUXUE
• YANG LIYONG
• LI GUANGSHAN
• BA TEER
• SUN GUOHUI
• WANG XIAOHUI

Assignees

• 西安热工研究院有限公司
• 华能伊敏煤电有限责任公司

Dates

Publication Date

20260512

Application Date

20260305

Claims (8)

1. 1. The multi-parameter self-adaptive equalization control method of the hybrid energy storage system is characterized by comprising the following steps of: Acquiring real-time voltage, real-time current and real-time temperature of each battery in a battery pack of the hybrid energy storage system; acquiring a temperature compensation coefficient and a current compensation coefficient according to the real-time voltage, the real-time current, the real-time temperature, the rated internal resistance, the temperature compensation coefficient and the current compensation coefficient of each battery, and acquiring a dynamic voltage index of each battery; Performing a cyclic aging experiment on the battery at a plurality of different constant temperatures to obtain a capacity attenuation rate, and then fitting experimental data by utilizing Arrhenii Wu Sigong according to the capacity attenuation rate to obtain the activation energy of the battery aging reaction; obtaining an equilibrium demand index of each battery according to the difference value between the dynamic voltage index of each battery and the average value of the dynamic voltage indexes of all batteries and the historical health index of each battery; The balance current of each battery is obtained by taking the balance demand index of each battery as an input signal to carry out feedback regulation; The dynamic voltage index of each battery is obtained according to the real-time voltage, the real-time current, the real-time temperature, the rated internal resistance, the temperature compensation coefficient and the current compensation coefficient of each battery, and the dynamic voltage index is expressed as follows by a formula: In the formula, Represent the first The real-time voltage of the battery cell, Represent the first The real-time current of the battery is saved, Represent the first The rated internal resistance of the battery is reduced, Represent the first The real-time temperature of the battery cell, The reference temperature is indicated as such, A temperature coefficient representing a battery voltage; Representing the current compensation coefficient of the current, Representing the temperature compensation coefficient of the temperature of the liquid crystal, Represent the first A dynamic voltage index of the battery; The historical health index of each battery is obtained according to the activation energy of the aging reaction of the battery, the universal gas constant, the historical average temperature of each battery and the charge and discharge cycle times of each battery, and is specifically expressed as the following formula: In the formula, Represents the activation energy of the aging reaction of the battery, The general gas constant is indicated as such, Represent the first The historical average temperature of the battery cells is, Represent the first The number of charge and discharge cycles experienced by the battery, Indicating the aging rate constant of the product, Represent the first The historical health index of the battery cell, An exponential function based on a natural constant is represented.
2. 2. The method of claim 1, wherein the obtaining the temperature compensation coefficient and the current compensation coefficient comprises: and obtaining a temperature compensation coefficient and a current compensation coefficient through curve fitting by respectively relating the system deviation between the measured voltage and the real open-circuit voltage to the current and the temperature.
3. 3. The multi-parameter adaptive equalization control method of a hybrid energy storage system of claim 1, wherein said arrhenius formula is expressed as: In the formula, Represents the activation energy of the aging reaction of the battery, Indicating a pre-finger factor, which is a constant related to the reaction; The general gas constant is indicated as such, The thermodynamic temperature is indicated as being the temperature of the fluid, Representing the rate of capacity fade and, An exponential function based on a natural constant is represented.
4. 4. The multi-parameter adaptive equalization control method of a hybrid energy storage system according to claim 1, wherein the equalization demand index of each battery is obtained according to the difference between the dynamic voltage index of each battery and the average value of the dynamic voltage indexes of all batteries and the historical health index of each battery, and is specifically expressed as: In the formula, Represent the first The dynamic voltage index of the battery cell, Representing the average of the dynamic voltage indices of all cells, Represent the first The historical health index of the battery cell, Represent the first Balance demand index of battery cells.
5. 5. The multi-parameter adaptive equalization control method of a hybrid energy storage system according to claim 1, wherein the equalization current of each battery is obtained by performing feedback adjustment by using an equalization demand index of each battery as an input signal, and the method is specifically expressed as: In the formula, Represent the first The balance demand index of the battery cell, Indicating the initial moment of operation of the system, Indicating the current time of day and, The proportional gain is indicated as such, The integral gain is represented as such, The differential gain is represented by a value of, Represent the first The balance current of the battery is controlled, wherein, Representation of The integral of the time over which the time has passed, Representation of Derivative with respect to time; wherein the proportional gain, the integral gain and the differential gain are obtained through experimental optimization.
6. 6. A multi-parameter adaptive equalization control system for a hybrid energy storage system, employing the multi-parameter adaptive equalization control method of any of claims 1-5, comprising: the data acquisition module is used for acquiring the real-time voltage, the real-time current and the real-time temperature of each battery in the battery pack of the hybrid energy storage system; The dynamic voltage analysis module is used for acquiring a temperature compensation coefficient and a current compensation coefficient, and acquiring a dynamic voltage index of each battery according to the real-time voltage, the real-time current, the real-time temperature, the rated internal resistance, the temperature compensation coefficient and the current compensation coefficient of each battery; The battery health evaluation module is used for carrying out a cyclic aging experiment on the battery at a plurality of different constant temperatures to obtain a capacity attenuation rate, and then fitting experimental data by utilizing an Arrhenii Wu Sigong formula according to the capacity attenuation rate to obtain the activation energy of the battery aging reaction; The battery balance demand analysis module is used for obtaining the balance demand index of each battery according to the difference value between the dynamic voltage index of each battery and the average value of the dynamic voltage indexes of all batteries and the historical health index of each battery; and the balance current control module is used for carrying out feedback adjustment by taking the balance demand index of each battery as an input signal to obtain the balance current of each battery.
7. 7. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing a multi-parameter adaptive equalization control method of a hybrid energy storage system of any of claims 1-5 when the computer program is executed by the processor.
8. 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 a multi-parameter adaptive equalization control method of a hybrid energy storage system according to any of claims 1-5.

Description

Multi-parameter self-adaptive equalization control method and system for hybrid energy storage system Technical Field The invention relates to the technical field of battery management and equalization control, in particular to a multi-parameter self-adaptive equalization control method and system of a hybrid energy storage system. Background In the field of battery energy storage systems, in particular to a battery pack formed by connecting a plurality of single batteries in series, the problem of battery inconsistency caused by differences in manufacturing process, use environment and chemical characteristics is common. The inconsistency is represented by differences of voltage, capacity, internal resistance and aging rate, and the inconsistency is accumulated continuously in the cyclic charge and discharge process, so that partial batteries are fully charged or discharged in advance, and overcharging and overdischarging are caused, the overall usable capacity is reduced, the aging of the batteries is accelerated, the service life is shortened, and potential safety hazards are possibly brought. The equalization control technology widely used at present is mainly based on threshold comparison of monomer voltages, and equalization is realized through passive dissipation or active energy transfer. However, a core drawback of this type of approach is that decisions are made dependent only on a single parameter, the voltage, being measured in real time. The voltage of the battery terminal is easy to be interfered by instantaneous current and temperature, and the true state of charge cannot be accurately reflected, so that the equalization misjudgment is caused. In addition, the existing method adopts an indiscriminate strategy for all batteries, ignores individual differences of the health states of the batteries, cannot provide special protection for the short-plate battery with quicker aging, can accelerate the degradation of the short-plate battery, and finally restricts the service life of the whole battery pack. In summary, the existing equalization control technology has low equalization precision and poor response due to single parameters and inaccurate state estimation, stiff strategy and unfused dynamic working condition and historical health state of the battery, and is difficult to effectively maintain long-term consistency of the battery pack and prolong the whole service life. Therefore, development of an intelligent balance control method capable of fusing multi-source parameters, self-adapting and sensing the health state of the battery is needed to break through the bottleneck of the prior art. Disclosure of Invention The invention provides a multi-parameter self-adaptive equalization control method and a multi-parameter self-adaptive equalization control system for a hybrid energy storage system, which are used for solving the problems of low equalization precision, high misjudgment rate and shortened service life of a battery pack caused by depending on single voltage parameter and neglecting dynamic changes of the state of health and working condition of the battery in the conventional battery equalization control. The aim of the invention can be achieved by the following technical scheme: The first aspect of the present invention provides a multi-parameter adaptive equalization control method for a hybrid energy storage system, including: Acquiring real-time voltage, real-time current and real-time temperature of each battery in a battery pack of the hybrid energy storage system; acquiring a temperature compensation coefficient and a current compensation coefficient according to the real-time voltage, the real-time current, the real-time temperature, the rated internal resistance, the temperature compensation coefficient and the current compensation coefficient of each battery, and acquiring a dynamic voltage index of each battery; Performing a cyclic aging experiment on the battery at a plurality of different constant temperatures to obtain a capacity attenuation rate, and then fitting experimental data by utilizing Arrhenii Wu Sigong according to the capacity attenuation rate to obtain the activation energy of the battery aging reaction; obtaining an equilibrium demand index of each battery according to the difference value between the dynamic voltage index of each battery and the average value of the dynamic voltage indexes of all batteries and the historical health index of each battery; And carrying out feedback regulation by taking the balance demand index of each battery as an input signal to obtain the balance current of each battery. Further, the acquiring the temperature compensation coefficient and the current compensation coefficient includes: and obtaining a temperature compensation coefficient and a current compensation coefficient through curve fitting by respectively relating the system deviation between the measured voltage and the real open-circuit voltage to the current a