A Novel State of Health Estimation of Lithium-ion Battery Energy Storage System Based on Linear Decreasing Weight-Particle Swarm Optimization Algorithm and Incremental Capacity-Differential Voltage Method

Accurate estimation of battery state of health (SOH) under energy storage conditions is a key and difficult technology in the use of lithium-ion batteries, which is related to the health and safety of batteries, use efficiency, and product replacement. Because of the complex working conditions of en...

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Veröffentlicht in:International journal of electrochemical science Jg. 17; H. 7; S. 220754
Hauptverfasser: Wu, Zhuoyan, Yin, Likun, Xiong, Ran, Wang, Shunli, Xiao, Wei, Liu, Yi, Jia, Jun, Liu, Yanchao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.07.2022
Schlagworte:
back propagation neural network
electrochemical and degradation mechanisms
incremental capacity-differential voltage method
linear decreasing weight-particle swarm optimization algorithm
single particle model
state of health
back propagation neural network
single particle model
incremental capacity-differential voltage method
linear decreasing weight-particle swarm optimization algorithm
electrochemical and degradation mechanisms
state of health
ISSN:1452-3981, 1452-3981
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Zusammenfassung:Accurate estimation of battery state of health (SOH) under energy storage conditions is a key and difficult technology in the use of lithium-ion batteries, which is related to the health and safety of batteries, use efficiency, and product replacement. Because of the complex working conditions of energy storage batteries, it is necessary to use a method to estimate SOH, which takes into account both the internal electrochemical mechanism and the battery degradation mechanism. Based on this requirement, a SOH estimation method for energy storage lithium-ion batteries based on linear decreasing weight-particle swarm optimization (LDW-PSO) algorithm and incremental capacity-differential voltage (IC-DV) method is proposed. The algorithm uses the LDW-PSO method to identify the maximum solid-phase lithium-ion concentration of positive and negative electrodes in the single particle (SP) model, quantifies degradation modes by the IC-DV method, and takes the above parameters as the input of the back propagation neural network (BPNN) to estimate the SOH of energy storage lithium-ion batteries. At 25 ℃, the working condition of an actual energy storage power station is used for simulation and verification. The experimental results show that the maximum estimation error, the mean estimation error, and the mean square error (MSE) of the battery SOH in test data are 0.0474%, 0.0261%, and 8.87×10-8, respectively, and the maximum estimation error, the mean estimation error, and the MSE of the battery SOH in the same batch with the same degradation path are 0.0077, 0.0012, and 5.24×10-6, respectively. The above results provide a theoretical and experimental basis for the problem that the SOH of lithium-ion batteries cannot be effectively estimated under complex working conditions of energy storage power stations.
ISSN:1452-3981
1452-3981
DOI:10.20964/2022.07.41