Battery prognostics at different operating conditions

Rechargeable batteries become one of the most popular energy storage devices. For battery state of health prediction, discharge rate and temperature are two crucial factors that significantly affect battery discharge capacity fade. Battery discharge capacity fade modeling at different operating conditions is still an ongoing research direction. In this paper, two new battery discharge capacity fade models are proposed. In the first model, a relationship between capacity fading and discharge rate is formulated. The second model derives a relation between capacity fading and temperature. Then, two Bayesian updating procedures are respectively designed to model a unit-to-unit capacity fade variance and incorporate on-line data of a single operating battery into the two battery fade models. At last, two case studies are provided to illustrate how the proposed two discharge capacity fade models work. Results show that the proposed two new models can accurately predict battery state of health at different discharge rates and different temperatures.