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

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.

Automated summary

This paper proposes a novel adaptive multi-context cooperatively co-evolutionary parallel differential evolution (AMCC-PDE) algorithm to identify parameters of lithium-ion batteries (LIB). The algorithm divides the data segment according to the state of charge (SOC) and optimizes the parameters and open-circuit voltage (UOCV) using a first-order RC model. Experimental results show that the AMCC-PDE algorithm can effectively identify parameters and maintain the variable-coupling relationship.