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Lithium-ion battery (LIB) sources have played an essential role inself-sustained transportation energy systems and have been widely deployed inthe last few years. To realize reliable battery maintenance, identifying itselectrochemical parameters is necessary. However, the battery model containsmany parameters while the measurable states are only the current and voltage,inducing the identification inherently an ill-conditioned problem. A parameteridentification approach is proposed, including the experiment, model, andalgorithm. Electrochemical parameters are first grouped manually based on thephysical properties and assigned to two sequenced tests for identification. Thetwo tests named the quasi-static test and the dynamic test, are compressed ontime for practical implementation. Proper optimization models and asensitivity-oriented stepwise (SSO) optimization algorithm are developed tosearch for the optimal parameters efficiently. Typically, the Sobol method isapplied to conduct the sensitivity analysis. Based on the sensitivity indexes,the SSO algorithm can decouple the mixed impacts of different parameters duringthe identification. For validation, numerical experiments on a typical NCM811battery at different life stages are conducted. The proposed approach savesabout half the time finding the proper parameter value. The identificationaccuracy of crucial parameters related to battery degradation can exceed 95\%.Case study results indicate that the identified parameters can not only improvethe accuracy of the battery model but also be used as the indicator of thebattery SOH.

Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems