Jingwen Wei

TL;DR: A novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism and the results show that the proposed SOH estimation method can provide an accurate and robustness result.

TL;DR: The experimental results show the superiority of the proposed Brownian motion based degradation model in battery health prognosis and it can provide accurate and robust SOH and RUL forecasting.

TL;DR: In this paper, an online estimation approach for battery SOC and parameters of a battery based on the IIM (invariant-imbedding-method) algorithm has been proposed, which can accurately capture the real-time characteristics of the battery, including the OCV hysteresis phenomena.

TL;DR: In this article, an online model-based estimation approach is proposed against uncertain dynamic load currents and environment temperatures to improve the battery state-of-energy estimation accuracy and reliability, and the proposed approach is verified by experiments conducted on a LiFePO4 lithium-ion battery under different operating currents and temperatures.

TL;DR: A particle filter based open circuit voltage online estimation method and comparison results show that prognostics via voltage-based state of charge has a lower prediction relative error under different current and temperature conditions, more suitable for the remaining-dischargeable-time forecast.