CN-122000506-A - Method, device, equipment, medium and product for managing energy storage battery pack

CN122000506ACN 122000506 ACN122000506 ACN 122000506ACN-122000506-A

Abstract

The embodiment of the disclosure discloses a management method, a device, equipment, a medium and a product of an energy storage battery pack, which comprise the steps of determining corresponding adjustment strategies of battery units according to corresponding working modes of the battery units, dynamically adjusting currents of the corresponding battery units according to the adjustment strategies to determine current adjustment results, determining temperature change results according to preset operation parameters, ambient temperature and the current adjustment results, determining input data corresponding to the energy storage battery pack according to the current adjustment results and the temperature change results, determining target charge states according to target neural network models based on the input data, and dynamically managing charge and discharge processes of the battery units in the energy storage battery pack according to the target charge states. The technical scheme realizes the fine and intelligent management of the charging and discharging of each unit in the energy storage battery pack.

Inventors

Wu Guanru
YUAN HUAN
XU ZIQIANG
FU GUOSHAN
XU LIHAO
KONG WEIYUAN
ZHANG SIQI
XIA WEIDONG
LI XIANG
WANG WENDI
ZHOU DONGXU
XU HONGHUA
CHANG FEI
SUN SHAOBIN
ZHAO LEI

Assignees

国网江苏省电力有限公司南京供电分公司
国网江苏省电力有限公司双创中心

Dates

Publication Date: 20260508
Application Date: 20260206

Claims (10)

1. A method of managing an energy storage battery, the energy storage battery comprising at least one cell, the method comprising: Determining a corresponding adjustment strategy of each battery unit according to the corresponding working mode of each battery unit, wherein the adjustment strategy is a strategy for adjusting voltage and/or current; Dynamically adjusting the current of the corresponding battery unit according to each adjustment strategy to determine a current adjustment result, wherein the current adjustment result comprises the adjusted current of each battery unit; Determining a temperature change result according to a preset operation parameter, an ambient temperature and the current adjustment result, wherein the temperature change result comprises temperature change data corresponding to each battery unit respectively; Determining input data corresponding to the energy storage battery pack according to the current adjustment result and the temperature change result, wherein the input data comprises an average state of charge, an average temperature, an average current absolute value and a state of charge standard deviation; And determining a target state of charge by utilizing a target neural network model based on the input data so as to dynamically manage the charging and discharging processes of each battery unit in the energy storage battery pack according to the target state of charge.
2. The method according to claim 1, wherein the method further comprises: acquiring current information corresponding to each battery unit respectively; And determining the corresponding working modes of each battery unit according to the current information, wherein the working modes comprise a charging mode and a discharging mode.
3. The method of claim 2, wherein the tuning strategy comprises a constant current tuning strategy, a constant voltage tuning strategy, a constant current-constant voltage tuning strategy, a deep learning reinforcement strategy, and a proportional-integral-derivative PID controller; The determining the corresponding adjustment strategy of each battery unit according to the corresponding working mode of each battery unit comprises the following steps: for any one battery unit, if the working mode of the battery unit is the discharging mode, determining the proportional-integral-derivative PID controller as an adjustment strategy corresponding to the battery unit; and if the working mode of the battery unit is the charging mode, determining the constant current adjustment strategy, the constant voltage adjustment strategy, the constant current-constant voltage adjustment strategy and/or the deep learning reinforcement strategy as adjustment strategies corresponding to the battery unit according to the working state of the battery unit.
4. The method of claim 1, wherein said determining input data based on said current adjustment result and said temperature change result comprises: Calculating the charge state, the temperature and the current respectively corresponding to each battery unit according to the current adjustment result and the temperature change result; and determining the input data corresponding to the energy storage battery pack according to the charge states, the temperatures and the currents respectively corresponding to the battery cells.
5. The method according to claim 1, wherein the method further comprises: The method comprises the steps of acquiring historical input data, wherein the historical input data comprises a historical average state of charge, a historical average temperature, a historical average current absolute value, a historical state of charge standard deviation and a corresponding historical state of charge label, and the historical state of charge is determined according to a preset rule; Inputting the historical input data into a neural network model to determine a state of charge prediction value; calculating a loss value through a loss function according to the state of charge predicted value and the corresponding historical state of charge label; Updating the weight of the neural network model through back propagation according to the loss value; And iteratively executing the steps of calculating the model loss value and updating the weight until the difference between the state of charge predicted value and the historical state of charge label meets a preset convergence condition, and determining the neural network model meeting the convergence condition as a target neural network model.
6. The method of claim 5, wherein the target neural network model comprises an input node, a hidden layer, and an output node.
7. A management device for an energy storage battery, the energy storage battery comprising at least one battery cell, the device comprising: the system comprises an adjustment strategy determining module, a voltage and/or current adjusting module and a voltage and/or current adjusting module, wherein the adjustment strategy determining module is used for determining the corresponding adjustment strategy of each battery unit according to the corresponding working mode of each battery unit; The current adjustment result determining module is used for dynamically adjusting the current of the corresponding battery unit according to each adjustment strategy so as to determine a current adjustment result, wherein the current adjustment result comprises the current adjusted by each battery unit; the temperature change result determining module is used for determining a temperature change result according to a preset operation parameter, the ambient temperature and the current adjustment result, wherein the temperature change result comprises temperature change data corresponding to each battery unit respectively; The input data determining module is used for determining input data corresponding to the energy storage battery pack according to the current adjustment result and the temperature change result, wherein the input data comprises an average state of charge, an average temperature, an average current absolute value and a state of charge standard deviation; And the management module is used for determining a target state of charge by utilizing a target neural network model based on the input data so as to dynamically manage the charging and discharging processes of each battery unit in the energy storage battery pack according to the target state of charge.
8. An electronic device, comprising: At least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of managing an energy storage battery set according to any one of claims 1-6.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a method of managing an energy storage battery according to any one of claims 1-6.
10. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the method of management of an energy storage battery according to any of claims 1-6.

Description

Method, device, equipment, medium and product for managing energy storage battery pack Technical Field The embodiment of the disclosure relates to the technical field of battery management, in particular to a management method, a device, equipment, a medium and a product of an energy storage battery pack. Background With the wide application of renewable energy sources, lithium ion batteries have become the mainstream energy storage technology in modern power systems, and are widely applied to portable electronic devices, electric automobiles and grid-level energy storage systems. Because of the advantages of high energy density, low self-discharge rate, long cycle life and the like, lithium ion batteries have an irreplaceable position in various scenes. However, during actual operation, improper operation such as overcharge and overdischarge may cause irreversible degradation of battery performance, and even raise safety risks. Therefore, the safe and efficient operation of the lithium ion battery is ensured, and the real-time monitoring and control of the internal state of the lithium ion battery are relied on. The balanced management of State of Charge (SoC) and the dynamic regulation of temperature and the minimization of energy loss are core functions of a Battery Management System (BMS), and the control accuracy directly relates to the service life of a Battery, the energy utilization efficiency and the operational reliability of the system. Traditional BMS design relies on physical experiments, but has long experimental period, high cost and difficulty in covering all complex working conditions, so that a dynamic simulation technology based on a mathematical model becomes a key means for optimizing BMS design. These simulation tools typically employ an equivalent circuit model (Equivalent Circuit Model, abbreviated ECM) or an electrochemical model (e.g., pseudo-Two-dimensional model, pseudo-Two-Dimensional Model, abbreviated P2D) to numerically solve for voltage, current, soC, and temperature variations of the simulated battery, helping engineers evaluate the impact of different control strategies on battery performance. In the prior art, there are various BMS simulation tools and methods for modeling battery behavior and optimizing control strategies. These existing schemes provide a basis for BMS simulation, but the problems of single strategy support, simplified temperature model and insufficient intelligence are common, and the battery performance under multi-strategy cooperation cannot be comprehensively estimated. Disclosure of Invention The embodiment of the disclosure provides a management method, a device, equipment, a medium and a product of an energy storage battery pack, which realize the fine and intelligent management of charging and discharging of each unit in the energy storage battery pack. In a first aspect, there is provided a method of managing an energy storage battery pack, the energy storage battery pack including at least one battery cell, the method comprising: Determining a corresponding adjustment strategy of each battery unit according to the corresponding working mode of each battery unit, wherein the adjustment strategy is a strategy for adjusting voltage and/or current; Dynamically adjusting the current of the corresponding battery unit according to each adjustment strategy to determine a current adjustment result, wherein the current adjustment result comprises the adjusted current of each battery unit; Determining a temperature change result according to a preset operation parameter, an ambient temperature and the current adjustment result, wherein the temperature change result comprises temperature change data corresponding to each battery unit respectively; Determining input data corresponding to the energy storage battery pack according to the current adjustment result and the temperature change result, wherein the input data comprises an average state of charge, an average temperature, an average current absolute value and a state of charge standard deviation; And determining a target state of charge by utilizing a target neural network model based on the input data so as to dynamically manage the charging and discharging processes of each battery unit in the energy storage battery pack according to the target state of charge. In a second aspect, there is provided a management device for an energy storage battery pack including at least one battery cell, the device comprising: the system comprises an adjustment strategy determining module, a voltage and/or current adjusting module and a voltage and/or current adjusting module, wherein the adjustment strategy determining module is used for determining the corresponding adjustment strategy of each battery unit according to the corresponding working mode of each battery unit; The current adjustment result determining module is used for dynamically adjusting the current of the corresponding battery unit according to e