Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method

• Published in: Journal of power sources Vol. 307; pp. 86 - 97
• Main Authors: Rahman, Md Ashiqur, Anwar, Sohel, Izadian, Afshin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2016
• Subjects: Battery management system; Electrochemical model; Lithium-ion batteries; Parameter identification; Particle swarm optimization; Lithium-ion batteries; Battery management system; Parameter identification; Electrochemical model; Particle swarm optimization
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2015.12.083

In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific parameters of the electrochemical model of a Lithium-Ion battery with LiCoO2 cathode chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these parameter changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified model parameters were used to generate the respective battery models for both healthy and degraded batteries. These models were then validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results. •Four critical electrochemical model parameters of a Li-Ion battery were identified.•A cost function was defined to minimize voltage error between model and test data.•Particle swarm optimization methodology was utilized to minimize the cost function.•Four sets of these critical model parameters were identified for the stated conditions.