Lei Cai

TL;DR: An optimization process with nondominated sorting genetic algorithm II (NSGA-II) is proposed to establish a more efficient SOH estimator with support vector regression (SVR) and the short-term features from the current pulse test and the degradation features in this article are the knee points at the transfer instants of the voltage.

TL;DR: A new method is proposed by using the Support Vector Machine (SVM) technique for accurately estimating the battery SOH by utilizing the features from the terminal voltage response of the Li-ion battery under current pulse test.

TL;DR: A novel evolutionary framework is proposed to estimate the Lithium-ion battery state of health, which uniformly optimizes the two key processes of establishing a data driven estimator.

TL;DR: A novel method optimizing single and multiple voltage ranges during the EV charging process is proposed and three Nickel Manganese Cobalt-based batteries from EV, which have been aged under calendar ageing for 360 days are used to validate the proposed method.

TL;DR: A novel ensemble learning framework to estimate the battery SOH, which can boost the performance of the data driven SOH estimation through a well-designed integration of the weak learners is proposed.