Model parameter identification for lithium-ion batteries using adaptive multi-context cooperatively co-evolutionary parallel differential evolution algorithm. (February 2023)

• Record Type: Journal Article
• Authors: Tang, Ruoli; Zhang, Shihan; Zhang, Shangyu; Zhang, Yan
• Is Part Of: Journal of energy storage. Volume 58(2023)
• Publication Date: 2023-02
• Subjects: Lithium-ion battery -- Parameters identification of battery -- Differential evolution -- Large scale optimization problem -- Cooperatively co-evolution; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.106432
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: 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 ( R 0 +, R 0 −, D 1, R 1, U 1, init ) of each UDS and the open-circuit voltage ( U OCV ) 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 AMCC-PDE and indicates that the algorithm can avoid destroying the variable-coupling relationship. Highlights: Lithium-ion battery parameter identification model based on large-scale global optimization Segmented parameter identification method based on battery dynamic properties Dynamic perception strategy for population updating speed Novel parallel mutation strategy for accelerating evolutionary speed Novel AMCC-PDE algorithm for efficiently optimizing 7069-dimensioanl problem