A two stage, adaptive-optimized Li-ion battery parameters estimation strategy

Abstract

This paper explores the effects of optimization following the adaptive parameters estimation (APE) on Li-ion battery parameter estimates. The universal adaptive stabilizer (UAS) based APE method has been proven to be less expensive both computationally and in terms of required experimental effort. However, the APE method requires the user to make an appropriate initial guess of upper, lower bounds and their respective confidence levels for each parameter. Making such initial guesses are not trivial, and the effects of bad initial guesses are proposed to be mitigated by adopting a well-known particle swarm optimization (PSO) technique to further optimize parameters estimates, after APE terminates. The estimates obtained from the APE method narrow the search interval and thus reduce the computational effort of the PSO technique to identify the optimum estimates of Li-ion battery parameters. In this work, the APE followed by PSO based scheme is developed and validated through simulation results on a well known, and experimentally validated Li-ion battery model.