Yuchen Song

TL;DR: A hybrid method of IND-AR model and PF algorithm are proposed, which has high accuracy in capacity fade prediction and RUL prediction of the proposed method and all the experiments results show great potential.

TL;DR: This paper proposes a joint lithium-ion battery state estimation approach that takes advantage of the data-driven least-square-support-vector-machine and model-based unscented-particle-filter and achieves the joint estimation with different time scales using the proposed hybrid joint state estimation method.

TL;DR: A self-adaptive life-cycle health state assessment method based on the on-line measurable parameters of lithium-ion battery that is adaptability and applicability in various electric vehicle applications is illustrated.

TL;DR: In this article, the authors proposed an iterative updated approach to improve the long-term prediction performance for a battery's RUL prediction, where a new estimator is output by the RVM, and the Kalman filter is applied to optimize this estimator with a physical degradation model.

TL;DR: An on-line estimator based on UPF algorithm is developed for battery SOH assessment and it is shown that the proposed method has a good adaptability for lithium-ion battery degradation with non-linear and non-Gaussian characteristics.