Assessment of Multi-Objective Optimization Algorithms for Parametric Identification of a Li-Ion Battery Model

The identification of intelligent models of Li-Ion batteries is a major issue in Electrical Vehicular Technology. On the one hand, the fitness of such models depends on the recursive evaluation of a set of nonlinear differential equations over a representative path in the state space, which is a time consuming task. On the other hand, battery models are intrinsically unstable, and small differences in the initial state or the system, as well as imprecisions in the parameter values, may trigger large differences in the output. Hence, learning battery models from data is a complex multi-modal problem and the parameters of these models must be determined with a high accuracy. In addition to this, producing a dynamical model of a battery is a multi-criteria problem, because the predictive capabilities of the model must be estimated in both the voltage and the temperature domains. In this paper, a selection of state-of-the-art Multi-Objective Optimization Algorithms (SPEA2, NSGA-II, OMOPSO, NSGA-III and MOEA/D) are assessed with regard to their suitability for identifying a model of a Li-Ion battery. The dominance relations that occur between the Pareto fronts are discussed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}ϵ-quality indicators. It is concluded that each of the standard implementations of these algorithms has different issues with this particular problem, MOEA/D and NSGA-III being the best overall alternatives.