Parameter Identification of Electrochemical Model using Data-Driven Framework for High Capacity NMC Battery

This paper presents a comprehensive study on the electrochemical modelling of a Lithium-ion battery with nickel manganese cobalt (NMC) chemistry. The SIFD numerical solution for single particle model is derived to investigate the battery’s performance under different operational conditions. The parameters for 50 Ah NMC batteries have been identified by using particle swarm optimization. Experimental data from a 50 Ah NMC battery at 1/20 C, 1/3 C, and 1 C and cycle data up to 90% SOH are used to estimate parameters. The validity of the model is evaluated by comparing the results of proposed scheme with experimental data and PyBaMM. We get a maximum cell error of 20 mV and 60 mV, which is 0.47% and 1.428% of the nominal voltage. The diffusion coefficient D sk in solid electrodes tends to reduce when the number of cycles increases, which directly affects the battery performance. Furthermore, the volume of active material ( e k ) in both k = n , p become reduce when the number of cycles increases.