Accurate prediction of remaining useful life (RUL) is of critical significance to the safety and reliability of lithium-ion batteries, which can offer efficient early warning signals for failure. Due to the complicated aging mechanism and realistic noise operation environment, direct predicting RUL with the measured data recorded in practice is challenging. In this work, a novel hybrid approach to forecasting battery future capacity and RUL is proposed by combining the improved variational modal decomposition (VMD), particle filter (PF) and gaussian process regression (GPR). The VMD algorithm is employed to decompose the recorded battery capacity data into an aging trend sequence and several residual sequences, where the number of modal layers is produced by the proposed posterior feedback confidence (PFC) method. The prediction models of PF and GPR algorithm are then respectively established to predict the aging trend sequence and residual sequences. Future capacity and RUL prediction experiments for battery pack and battery cells are performed to verify the effectiveness of the proposed hybrid approach, and the compared experiment results demonstrate that the proposed approach offers wide generality and reduced errors.

The implementation of a precise and low-computational state-of-health (SOH) estimation algorithm for lithium-ion batteries represents a critical challenge in the practical application of electric vehicles (EVs). The complicated physicochemical property and the forceful dynamic nonlinearity of the degradation mechanism require data-driven methods to substitute mechanistic modeling approaches to evaluate the lithium-ion battery SOH. In this study, an incremental capacity analysis (ICA) and improved broad learning system (BLS) network-based SOH estimation technology for lithium-ion batteries are developed. First, the IC curves are drawn based on the voltage data of the constant current charging phase and denoised by the smoothing spline filter. Then, the Pearson correlation coefficient method is used to select the critical health indicators from the features extracted from the IC curves. Finally, the lithium-ion battery SOH is assessed by the SOH estimation model established by an optimized BLS network, where the BLS network is formed through its L2 regularization parameter and the enhancement nodes’ shrinkage scale filtrated by a particle swarm optimization algorithm. The experimental results demonstrate that the proposed method can effectively evaluate the SOH with strong robustness as well as stability to the degradation and disturbance of in-service and retired lithium-ion batteries.

It is imperative to accurately predict the remaining useful life (RUL) of lithium-ion batteries to ensure the reliability and safety of related industries and facilities. In view of the noise sequence embedded in the measured aging data of lithium-ion batteries and the strong nonlinear characteristics of the aging process, this study proposes a method for predicting lithium-ion batteries’ RUL based on the wavelet threshold denoising and transformer model. To specify, firstly, the wavelet threshold denoising method is adopted to preprocess the measured discharging capacity data of lithium-ion batteries to eliminate some noise signals. Second, based on the denoised data, the transformer model output’s full connection layer is applied to replace the decoder layer for establishing the RUL prediction model of lithium-ion batteries. Finally, the discharging capacity of each charging–discharging cycle is predicted iteratively, and then the RUL of lithium-ion batteries can be calculated eventually. Two groups of lithium-ion batteries’ aging data from the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland and the laboratory at Anqing Normal University (AQNU) are employed to verify the proposed method, individually. The experimental results demonstrate that this method can overcome the impacts of data measurement noise, effectively predict the RUL of lithium-ion batteries, and present a sound generalization ability and high accuracy.

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

The precise estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is one of the core problems for battery management systems. To address the problem that it is difficult to accurately evaluate SOH because of the LIB capacity regeneration phenomenon, this paper proposes an approach for LIB SOH estimation using isobaric energy analysis and improved long short-term memory neural network (LSTM NN). Specifically, at first, the isobaric energy curve is plotted by analyzing the battery energy variation during the constant current charging stage. Then, the mean peak value of the isobaric energy curve is extracted as a health factor to characterize the battery SOH aging. Eventually, the LIB SOH estimation model is developed using the improved LSTM NN. In this regard, the improved LSTM NN refers to the selection of the number of hidden layers and the learning rate of the LSTM NN using the particle swarm algorithm (PSO). To verify the precision of the proposed method, validation experiments are performed based on four battery aging data with different charging multipliers. The experimental results indicate that the proposed method can effectively estimate the LIB SOH. Meanwhile, the proposed method is compared with other conventional machine learning algorithms, which demonstrates that the proposed method has better estimation performance.