Model parameter identification for lithium-ion batteries using adaptive multi-context cooperatively co-evolutionary parallel differential evolution algorithm

被引:6
作者
Tang, Ruoli [1 ]
Zhang, Shihan [1 ]
Zhang, Shangyu [1 ]
Zhang, Yan [2 ]
机构
[1] Wuhan Univ Technol, Sch Naval Architecture Ocean & Energy Power Engn, Wuhan, Hubei, Peoples R China
[2] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Lithium-ion battery; Parameters identification of battery; Differential evolution; Large scale optimization problem; Cooperatively co-evolution; CHARGE ESTIMATION; ONLINE ESTIMATION; STATE; OPTIMIZATION;
D O I
10.1016/j.est.2022.106432
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Lithium-ion battery (LIB) has a polarization phenomenon during charging and discharging processes, and the internal chemistry of LIB is characterized by severe time-varying nonlinearity. Therefore, the equivalent circuit model (ECM) is usually used for LIB research. To address the problems of low identification accuracy and local optimization in the offline identification of battery parameters, this paper proposes a novel adaptive multi context cooperatively co-evolutionary parallel differential evolution (AMCC-PDE) algorithm to identify parameters of LIB. Firstly, the data segment to be identified is divided into a plurality of segments according to the state of charge (SOC), and each segment is called a unit data segment (UDS). In addition, the UDS has a parameter group (PG) for the first-order RC model. Secondly, according to the differential equation of the first-order RC model, the PG (R0+, R0 , D1, R1, U1, init) of each UDS and the open-circuit voltage (UOCV) at each sampling point are considered as variables to be optimized. Then, such an optimization problem is transformed into a large-scale global optimization (LSGO) problem. In addition, to trade off the relationship between population diversity and convergence speed, a novel parallel mutation strategy is proposed. Finally, an AMCC-PDE algorithm is proposed to solve the above LSGO parameters identification model. In both of the DST and FUDS datasets, the identification error at each point obtained by AMCC-PDE is lower than 2 mV, which shows the effectiveness of AMCCPDE and indicates that the algorithm can avoid destroying the variable-coupling relationship.
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页数:16
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