The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is of great importance to the health management of electric vehicles and hybrid electric vehicles. However, fluctuation and nonlinearity occur during battery degradation, resulting in difficulties in both model adaptability and RUL prediction accuracy. To face the challenge, we propose a sequence decomposition and deep learning integrated prognostic approach for the RUL prediction of LIBs. Complementary ensemble empirical mode decomposition and principal component analysis are applied to separate the local fluctuations and the global degradation trend from the battery aging data. The long short-term memory neural network combined with fully connected layers is designed as a transfer learning model. The hyperparameter optimization and finetuning strategy of the model is developed based on offline training data. In addition, to further realize the reasonable and effective LIB second-life applications, the RUL corresponding to different failure thresholds is predicted. The performance of the proposed integrated approach in degradation modeling and RUL prediction is evaluated on three publicly available LIB datasets with different degradation characteristics, as well as compared with other prediction algorithms under the same conditions. The illustrative results demonstrate that the proposed approach can achieve accurate, adaptive, and robust prediction for both capacity trajectory and RUL.

Global principal component analysis (PCA) has been successfully introduced for modeling distributed parameter systems (DPSs). In spite of the merits, this method is not feasible due to parameter variations and multiple operating domains. A novel multimode spatiotemporal modeling method based on the locally weighted PCA (LW-PCA) method is developed for large-scale highly nonlinear DPSs with parameter variations, by separating the original dataset into tractable subsets. This method implements the decomposition by making full use of the dependence among subset densities. First, the spatiotemporal snapshots are divided into multiple different Gaussian components by using a finite Gaussian mixture model (FGMM). Once the components are derived, a Bayesian inference strategy is then applied to calculate the posterior probabilities of each spatiotemporal snapshot belonging to each component, which will be regarded as the local weights of the LW-PCA method. Second, LW-PCA is adopted to calculate each locally weighted snapshot matrix, and the corresponding local spatial basis functions (SBFs) can be generated by the PCA method. Third, all the local temporal models are estimated using the extreme learning machine (ELM). Thus, the local spatiotemporal models can be produced with local SBFs and corresponding temporal model. Finally, the original system can be approximated using the sum form of each local spatiotemporal model. Unlike global PCA, which uses global SBFs to construct a global spatiotemporal model, LW-PCA approximates the original system by multiple local reduced SBFs. Numerical simulations verify the effectiveness of the developed multimode spatiotemporal model.

As the only power source of pure electric vehicles, lithium-ion batteries play an important role in vehicle powertrain systems. However, lithium-ion batteries have a significant reduction in capacity and power capability at low temperatures, which results in a greatly shortened driving range and poor acceleration of the vehicle. In this study, a rule-based battery external heating control strategy was developed to heat the battery during driving. An electrothermal film was affixed to the surface of each cell as an external heating material that was powered by the battery. An equivalent circuit model combined with a thermal model was established to simulate the electrical and thermal dynamics of the system with sufficiently high accuracy, and control rules were developed based on the model. The optimal solution was obtained by adopting the dynamic programming algorithm to optimize the trade-off between temperature rise and energy consumption and maximize the total driving range under different low temperature driving conditions. Simulation results on the experimentally validated model show that the vehicle with the proposed control algorithm increased the total driving range by 18.6% to 220% for different driving conditions at cold to extremely cold temperatures compared with the vehicle without external heating. Furthermore, the rule-based control showed a 1.1% to 4.4% improvement compared with the maximum (constant) power heating method.

This paper proposes a sensitivity-based group-wise parameter identification algorithm for the electrical model of Li-ion battery. A global sensitivity analysis method is first performed in the entire parameter space to evaluate the identifiability of the model parameters. Then, the parameters are sorted and grouped by the global sensitivity indices. Finally, a group-wise method embedded with the Levenberg-Marquardt algorithm is followed to identify the parameters. Numerical simulation results and comparisons demonstrate that the proposed group-wise identification algorithm can serve as a reliable tool for extracting parameters.