CN-121791402-B - State prediction and equalization control method and system for hybrid energy storage system

CN121791402BCN 121791402 BCN121791402 BCN 121791402BCN-121791402-B

Abstract

The invention relates to the technical field of battery management, in particular to a state prediction and balance control method and system of a hybrid energy storage system, comprising the steps of collecting real-time electric data of a battery pack, and calculating the health state of each battery through an internal resistance model and a temperature history; and predicting the dynamic time constant based on the health degree and the rated time constant, and predicting the future voltage value by using an open circuit voltage model and a relaxation effect. And (3) calculating the comprehensive balance urgency degree by comparing the deviation of the future voltage and the group mean value and combining the battery health degree. And finally, inputting the urgency and the total current of the system into a dynamic decision model based on an activation energy model, wherein the dynamic decision model is used for non-linearly coupling the balance requirement and the system load through an exponential function, and adaptively outputting smooth and accurate balance current of each battery, so that accurate judgment of real inconsistency and preferential protection of an aged battery are realized. The invention improves the accuracy of state prediction and equalization control of the hybrid energy storage system.

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: 20260304

Claims (8)

1. The state prediction 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 total current of each battery in a battery pack of the hybrid energy storage system; Obtaining real-time estimated internal resistance of each battery according to the real-time current and the real-time voltage of each battery, obtaining a temperature aging coefficient through fitting battery accelerated aging test data, and obtaining the state health of each battery according to the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient and the rated internal resistance of the battery; Obtaining a predicted time constant of each battery according to the rated time constant of the battery and the state health of each battery, obtaining the current theoretical open-circuit voltage of each battery according to the real-time current and the real-time voltage of the battery, and obtaining the future voltage predicted value of each battery according to the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery and the preset predicted time span; Obtaining predicted equilibrium urgency of each battery according to the difference between the predicted value of the future voltage of each battery and the average value of the predicted values of the future voltages of all batteries and the state health of each battery; obtaining dynamic equilibrium activation energy of each battery according to theoretical reference equilibrium activation energy, total current of the battery pack, reference value of total current, predicted equilibrium urgency of each battery and reference value of predicted equilibrium urgency; The dynamic equilibrium activation energy of each battery is obtained according to theoretical reference equilibrium activation energy, total current of the battery pack, reference value of total current, predicted equilibrium urgency of each battery and reference value of predicted equilibrium urgency, and the dynamic equilibrium activation energy of each battery is specifically expressed as follows by a formula: In the formula, Represents the theoretical reference equilibrium activation energy, Indicating the total current of the battery pack, A reference value representing the total current is indicated, Represent the first The predicted equilibrium urgency of the battery cells, A reference value representing predicted urgency of equalization, Represent the first The dynamic equilibrium activation energy of the battery is saved, Is an absolute value symbol; The activation energy corresponding to the charge transfer resistor obtained in the electrochemical impedance spectrum test is used as theoretical reference equilibrium activation energy; The method comprises the steps of obtaining the balanced current of each battery according to the dynamic balanced activation energy of each battery, an energy reference value and the maximum balanced current allowed by a system, wherein the balanced current of each battery is specifically expressed as follows: In the formula, Represent the first The dynamic equilibrium activation energy of the battery is saved, The energy reference value is represented by a reference value, Indicating the maximum equilibrium current allowed by the system, Represent the first The balance current of the battery is controlled, An exponential function based on a natural constant is represented.
2. The method for controlling state prediction equalization of a hybrid energy storage system according to claim 1, wherein the state health of each battery is obtained according to the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient and the rated internal resistance of the battery, and the state health of each battery is specifically expressed as: In the formula, Indicating the rated internal resistance of the battery, Represent the first The internal resistance of the battery is estimated in real time, The temperature ageing coefficient is indicated as such, Represent the first The historical average temperature of the battery cells is, The reference temperature is indicated as such, Represents an exponential function with a base of a natural constant, Represent the first And the state health of the battery.
3. The method for controlling state prediction equalization of a hybrid energy storage system according to claim 1, wherein the step of obtaining the predicted time constant of each battery based on the rated time constant of the battery and the state health of the battery of each battery, and the step of obtaining the current theoretical open-circuit voltage of each battery based on the real-time current and the real-time voltage of the battery comprises the steps of: the predicted time constant of each battery is specifically expressed as follows: In the formula, Indicating the rated time constant of the battery, Represent the first The state of health of the battery cell, Represent the first A predicted time constant of the battery cell; the specific acquisition process of the current theoretical open-circuit voltage of each battery is as follows: According to the current and the voltage of the battery, the current estimated state of charge value of the battery is obtained by combining an ampere-hour integration method with a voltage correction method or adopting Kalman filtering, and according to the current estimated state of charge value of the battery, the current theoretical open-circuit voltage of each battery is obtained through an OCV-SOC curve.
4. The hybrid energy storage system state prediction equalization control method according to claim 1, wherein the future voltage predicted value of each battery is obtained according to a real-time voltage of each battery, a current theoretical open-circuit voltage of each battery, a predicted time constant of each battery and a preset predicted time span, and the future voltage predicted value of each battery is specifically expressed as: In the formula, Represent the first The real-time voltage of the battery cell, Represent the first The current theoretical open circuit voltage of the battery cell, Represent the first The predicted time constant of the battery cell is calculated, Representing a pre-set predicted time span of time, Represent the first A future voltage predicted value of the battery cell, Representing natural constants.
5. The method for controlling state prediction equalization of a hybrid energy storage system according to claim 1, wherein the predicted equalization urgency of each battery is obtained according to a difference between a future voltage prediction value of each battery and a mean value of future voltage prediction values of all batteries and a state health of each battery, and the predicted equalization urgency of each battery is specifically expressed by a formula: In the formula, Represent the first A future voltage predicted value of the battery cell, Representing the average of future voltage predictions for all cells, Represent the first The state of health of the battery cell, Represent the first The predicted equilibrium urgency of the battery cells, Is an absolute value sign.
6. A state predictive equalization control system for a hybrid energy storage system, employing the state predictive equalization control method for a hybrid energy storage system 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 total current of each battery in the battery pack of the hybrid energy storage system; The health evaluation module is used for obtaining real-time estimated internal resistance of each battery according to the real-time current and the real-time voltage of each battery, obtaining a temperature aging coefficient through fitting battery accelerated aging test data, and obtaining the state health of each battery according to the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient and the rated internal resistance of the battery; The voltage prediction module is used for obtaining a predicted time constant of each battery according to the rated time constant of the battery and the state health of the battery of each battery, obtaining the current theoretical open-circuit voltage of each battery according to the real-time current and the real-time voltage of the battery, and obtaining the future voltage predicted value of each battery according to the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery and the preset predicted time span; the urgency degree analysis module is used for obtaining the predictive equilibrium urgency degree of each battery according to the difference between the future voltage predicted value of each battery and the average value of the future voltage predicted values of all batteries and the battery state health degree of each battery; The balancing current decision module is used for obtaining dynamic balancing activation energy of each battery according to theoretical reference balancing activation energy, total current of the battery pack, a reference value of total current, predicted balancing urgency of each battery and a reference value of predicted balancing urgency, and obtaining balancing current of each battery according to the dynamic balancing activation energy of each battery, the energy reference value and the maximum balancing current allowed by the system.
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 hybrid energy storage system state prediction balancing control method according to any one of claims 1-6 when executing the computer program.
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 hybrid energy storage system state prediction equalization control method according to any of claims 1-6.

Description

State prediction and equalization control method and system for hybrid energy storage system Technical Field The invention relates to the technical field of battery management, in particular to a state prediction and equalization control method and system for a hybrid energy storage system. Background The battery energy storage system is a core component of a modern energy system, but the inconsistency of capacity, internal resistance and aging speed of each monomer in the battery pack is inevitable due to the differences of manufacturing process, material characteristics and using conditions. In the series charge and discharge process, the inconsistency can lead to the gradual divergence of the charge states of all the single batteries, cause the overcharge or overdischarge of part of the batteries, severely restrict the available capacity of the battery pack, accelerate the overall aging and bring potential safety hazards. To address this problem, voltage threshold-based equalization control techniques (including passive equalization and active equalization) are currently commonly employed to maintain consistency by monitoring battery voltage and energy redistribution. However, the traditional equalization method has three fundamental defects that firstly, the instantaneous terminal voltage is taken as a judgment basis, the influence of abrupt change of working current and temperature fluctuation is extremely easy to cause frequent false triggering of equalization action of a system, energy is wasted, inconsistency is possibly aggravated, secondly, the method completely ignores the health state difference of battery individuals, the aged 'short-plate' battery cannot be identified and protected, service life attenuation of the whole battery is accelerated, and finally, the control strategy is stiff, the self-adaptive adjustment cannot be carried out according to the unbalanced severity and the real-time load of the system by adopting a fixed threshold value and equalization current, so that the response is slow and the efficiency is low, and switching oscillation and electromagnetic interference are easy to generate near the threshold value. In summary, in the prior art, due to the single decision parameter and failure to fuse the dynamic working condition and the historical health status information of the battery, the control precision is low, the intelligent degree is not enough, the malfunction cannot be avoided, the prospective protection of the weak battery is difficult to be realized, and finally, the key bottleneck for restricting the improvement of the overall performance, the safety and the service life of the battery pack is formed. Disclosure of Invention The invention provides a state prediction equalization control method and a state prediction equalization control system for a hybrid energy storage system, which are used for solving the problems that the prior battery equalization control method is frequent in misoperation, low in equalization efficiency, incapable of protecting an aged battery and capable of accelerating the whole group of service life attenuation due to the fact that the battery health state is ignored and a control strategy is stiff due to the dependence on a single voltage parameter. The aim of the invention can be achieved by the following technical scheme: the first aspect of the present invention provides a state prediction equalization control method for a hybrid energy storage system, including: acquiring real-time voltage, real-time current and total current of each battery in a battery pack of the hybrid energy storage system; Obtaining real-time estimated internal resistance of each battery according to the real-time current and the real-time voltage of each battery, obtaining a temperature aging coefficient through fitting battery accelerated aging test data, and obtaining the state health of each battery according to the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient and the rated internal resistance of the battery; Obtaining a predicted time constant of each battery according to the rated time constant of the battery and the state health of each battery, obtaining the current theoretical open-circuit voltage of each battery according to the real-time current and the real-time voltage of the battery, and obtaining the future voltage predicted value of each battery according to the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery and the preset predicted time span; Obtaining predicted equilibrium urgency of each battery according to the difference between the predicted value of the future voltage of each battery and the average value of the predicted values of the future voltages of all batteries and the state health of each battery; The dynamic equil