Optimal Discretization Approach to the Enhanced Single-Particle Model for Li-Ion Batteries

Enhanced single-particle models (eSPMs) have been extensively studied in the development of advanced battery management systems for their accuracy and capability of tracking physical quantities, as well as for the reduced computational load. This article proposes an optimal discretization approach to model reduction for the eSPM using a particle swarm optimization algorithm. The battery diffusion dynamics were solved using different finite difference approaches, that is, an even discretization approach (baseline model) and an uneven discretization approach (optimized model). Because of the structure of the eSPM, internal nodes locations of the solid phase and the electrolyte phase are separately optimized. For the solid phase, a weighted multiobjective cost function is considered for achieving accurate surface and bulk concentration, aiming for accurate terminal-voltage and state-of-charge prediction. For the electrolyte phase, the optimization aims for accurate concentration prediction at the boundary of the electrolyte. The optimally reduced uneven discretization model can predict the battery dynamics accurately and with an improved computational cost: 1) the maximum voltage and SOC prediction errors demonstrated under dynamic current profiles are less than 2.73 mV and 0.37%, respectively, and 2) the number of states reduces by at least 11 times, leading to about a 64% reduction in the computation time.

Automated summary

This paper proposes an optimal discretization approach for model reduction of enhanced single-particle models (eSPMs), aiming to accurately predict battery dynamics and reduce computational costs. Through optimized uneven discretization, the model can predict battery dynamics accurately with improved computational cost efficiency.