Adaptive Exploration Harmony Search for Effective Parameter Estimation in an Electrochemical Lithium-Ion Battery Model

IEEE Access

Jungsoo Kim¹  Minho Kim¹  Soohee Han¹  Huiyong Chun¹  Jungwook Yu¹  Kwangrae Kim¹  Taegyun Kim¹ 

[1] Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang, South Korea;

关键词: Adaptive exploration harmony search;    electrochemical model;    lithium-ion battery;    meta-heuristic algorithm;    parameter estimation;    parameter identifiability;   

DOI  :  10.1109/ACCESS.2019.2940968

来源: DOAJ

【 摘 要 】

Electrochemical models of lithium-ion batteries are derived according to the laws of physics; therefore, the parameters represent specific physical quantities such as lithium diffusivities, particle volume fractions, and ion intercalation rates. It is important to estimate these parameters to identify the internal states of a lithium-ion battery for efficient and safe management. Until now, parameter estimation algorithms for electrochemical lithium-ion battery models have been developed without considering the unequal identifiability among the target parameters. Thus, it is highly likely that existing algorithms exhibit inefficient exploration and lead to a slow convergence rate and even large parameter estimation error. For more accurate parameter estimation of an electrochemical lithium-ion battery model, we propose a new adaptive exploration harmony search (AEHS) scheme that provides a wide search space for a longer period of time when estimating parameters with low identifiability. The proposed algorithm is based on improved harmony search; its bandwidth parameters for determining the level of exploration are adjusted according to the individual and joint variabilities computed from the distributions of previously estimated parameters. Such adaptive bandwidth parameters can reduce inefficient exploration and enable fast convergence, allowing exploration that achieves global optimality. Simulation results show that the proposed parameter estimation algorithm produces the highest convergence rate and the smallest parameter estimation error compared with existing schemes. The performance of the proposed scheme is also validated using real data generated from experiments.

【 授权许可】

Unknown