A novel interval‐based approach for quantifying practical parameter identifiability of a lithium‐ion battery model. (23rd January 2020)

• Record Type: Journal Article
• Authors: Zhou, Wei; Huang, Ranjun; Liu, Kan; Zhang, Weigang
• Is Part Of: International journal of energy research. Volume 44:Number 5(2020)
• Page Start: 3558
• Page End: 3573
• Publication Date: 2020-01-23
• Subjects: battery management system -- confidence interval -- parameter estimation -- practical identifiability -- profile likelihood; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.5118
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Practical identifiability of battery model parameters, on which both modeling accuracy and robustness rely, is considered as a very important prerequisite for advanced onboard monitoring and control of Lithium‐ion batteries. In this paper, a novel confidence‐interval‐based approach is proposed for the quantification and assessment of the practical identifiability of a widely used second order battery equivalent circuit model (ECM). This method utilizes profile likelihood and likelihood ratio subset statistic to calculate each parameter's confidence interval, based on which a normalized index is further derived for facilitating quantification and fast comparison of the identifiability degree among different parameters. Using this approach, the practical identifiability of the second order ECM under lab‐collected experimental data is successfully evaluated, and the influences of several real‐world factors are systematically examined through extensive simulations. The results show that the open circuit voltage and ohmic internal resistance have a much larger degree of identifiability in all the investigated conditions. Some practically useful insights on performing battery parameter identification are also provided. Abstract : This paper proposes a novel interval‐based approach for the quantification of the practical identifiability of a widely used second order battery equivalent circuit model (ECM). This method utilizes profile likelihood and likelihood ratio subset statistic to calculate each parameter's confidence interval, based on which a normalized index is further derived to quantify the identifiability degree of different parameters. Using this approach, the practical identifiability of the second order ECM under different experimental and simulated data are successfully evaluated, and some practically useful insights on performing battery parameter identification are also provided.