State-of-health estimation of lithium-ion battery based on fractional impedance model and interval capacity

Lithium-ion batteries are being used in electric vehicles with very demanding duty schedules. The estimation of battery state of health is very important, so that it has become a research hotspot. This paper deals with the problem of lithium-ion battery state-of-health estimation based on a simplified fractional impedance model and the battery's interval capacity. A simplified fractional impedance model based on the Grunwald-Letnikov definition is introduced, and the least-squares genetic algorithm is utilized to identify the model parameters with a voltage-tracing error rate less than 0.2%. In order to validate the battery ageing performance, a battery test-bench has been established, and an accelerated ageing experiment has been carried out. Based on the identified model parameters and interval capacity combination with a voltage range from 3.95 V to 4.15 V, a back propagation neural network is introduced to estimate the battery state of health with an error margin of [-1.5%, 1.5%]. The effectiveness of the proposed method is verified through simulations and experiments.