  Efficient parameter identification of electrochemical models is crucial for
accurate monitoring and control of lithium-ion cells. This process becomes
challenging when applied to complex models that rely on a considerable number
of interdependent parameters that affect the output response. Gradient-based
and metaheuristic optimization techniques, although previously employed for
this task, are limited by their lack of robustness, high computational costs,
and susceptibility to local minima. In this study, Bayesian Optimization is
used for tuning the dynamic parameters of an electrochemical equivalent circuit
battery model (E-ECM) for a nickel-manganese-cobalt (NMC)-graphite cell. The
performance of the Bayesian Optimization is compared with baseline methods
based on gradient-based and metaheuristic approaches. The robustness of the
parameter optimization method is tested by performing verification using an
experimental drive cycle. The results indicate that Bayesian Optimization
outperforms Gradient Descent and PSO optimization techniques, achieving
reductions on average testing loss by 28.8% and 5.8%, respectively. Moreover,
Bayesian optimization significantly reduces the variance in testing loss by
95.8% and 72.7%, respectively.
