Current pulses are convenient to be actively implemented by a battery management system. However, the short-term features (STF) from current pulses originate from various sensors with uneven qualities, which hinder one powerful and strong learner with STF for the battery state of health (SOH) estimation. This article, thus, proposes an optimized weak learner formulation procedure for lithium-ion battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto front-based selection strategy is designed to select the representative solutions from the nondominated solutions fed by a knee point driven evolutionary algorithm, which guarantees both the diversity and accuracy of the weak learners. Afterward, the weak learners, whose coefficients are obtained by self-adaptive differential evolution, are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of the SOH estimation. The validation of the proposed method is proved by LiFePO ${_4}$ /C batteries under accelerated cycling ageing test including one mission profile providing primary frequency regulation service to the grid and one constant current profile.

Pseudorandom sequence (PRS) enables fast electrochemical impedance spectroscopy (EIS) measurement with multifrequencies superposition. One limitation is the low signal-to-noise ratio (SNR) and the interference among the broadband frequencies. In this thread, a delicate data processing procedure is an urgent requirement for EIS measurement with PRS. Motivated by this, a rapid impedance extraction method is proposed for lithium-ion (Li-ion) batteries in this work for EIS measurement, which integrates the power spectrum and frequency properties of the PRS excitation signal to the impedance measurement. All the intrinsic connections of the impedance, frequency, and power spectrum are utilized to form a novel fusion mechanism so that the battery impedance can be effectively extracted by an automatic selection procedure. Experimental validation on an 18650 Li-ion battery confirms the good performance of the proposed method at varying SOCs and temperatures.

Early prediction of the lithium-ion (Li-ion) battery degradation trajectory is of great importance to arrange the maintenance of battery energy storage systems (BESSs). Although extensive data driven methods have achieved a super good performance in state of health (SOH) and remaining useful life (RUL) prediction, the nonlinear characteristics of the Li-ion battery degradation trajectory still prevent an accurate prediction once very limited cycling data known in advance. To solve this issue, this paper proposes an empirical-informed model for the degradation trajectory prediction with only few data from the Li-ion battery's early cycling stage, which integrates the experience based knowledge to train the data driven model. A novel experience based model is proposed to describe the battery degradation curve, which further guides the training procedure of the long-short term memory (LSTM) network. In addition, XGBoost is selected to use a perceptually important point (PIP) based feature for providing the reference capacities. In this way, the proposed method can implement an end-to-end early prediction of the battery trajectory using only partial charging voltage as the input. The performance of the proposed method is verified on three datasets.