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.

Battery impedance provides rich information that facilitates battery state estimation and failure diagnosis, yet the current impedance measurement techniques are quite laborious and difficult to implement. This motivates us to propose a comprehensively optimized binary sequence (COBS) for the fast measurement of broadband battery impedance responses. The minimum signal-to-noise ratio requirement of each frequency component of the excitation signal is first derived by analyzing the quantitative relationship between the impedance deviation and the sampling error. The optimal amplitudes and phase angles of all frequency components are then resolved by applying a comprehensive optimization framework to form the COBS for battery impedance response extraction. The effectiveness of the proposed method is verified experimentally in comparison with two prior arts. The results demonstrate the superior performance in measuring broadband battery impedance, where the total normalized root-mean-square errors are below 0.49%.

The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). First, to address the limitations of traditional extreme learning machines (ELMs) in capturing non-linear features and their poor generalization ability, the concepts of auto encoders (AEs) and hybrid kernel functions are introduced to enhance ELM, resulting in the establishment of the DHKELM model for SOH prediction. Next, to tackle the challenge of parameter selection for DHKELM, an optimal point set strategy, the Gompertz growth model, and a Levy flight strategy are employed to optimize the parameters of DHKELM using IBKA before model training. Finally, the performance of IBKA-DHKELM is validated using two distinct datasets from NASA and CALCE, comparing it against ELM, DHKELM, and BKA-DHKELM. The results show that IBKA-DHKELM achieves the smallest error, with an RMSE of only 0.0062, demonstrating exceptional non-linear fitting capability, high predictive accuracy, and good robustness.