Lithium-ion batteries play a vital role in human society. Therefore, it is of critical significance to reliably predict the evolution of State of Health (SOH) degradation patterns in order to improve the high accuracy and stability of lithium-ion battery SOH prediction. This paper proposes a novel SOH predication method by combing the four-vector intelligent metaheuristic (FVIM) with the CNN-LSTM-Attention basic model. The model adopts the collaborative architecture of a convolutional neural network and time series module, strengthens the cross-level feature interaction by introducing a multi-level attention mechanism, then uses the FVIM optimization algorithm to optimize the key parameters to realize the overall model architecture. By analyzing the charging voltage curve of lithium-ion batteries, the health factors with high correlation are extracted, and the correlation between the health factors and battery capacity is verified using two correlation coefficients. After the model is verified on a single NASA battery aging dataset, the model is compared with other models under the same relevant parameters and environmental settings to verify the high-precision prediction of the model. During the analysis and comparison process, CNN-LSTM-Attention-FVIM achieved a high fitting ability for battery SOH prediction estimation, with the mean absolute error (MAE) and root mean square error (RMSE) within 0.99% and 1.33%, respectively, reflecting the model’s high generalization ability and high prediction performance.

Short vehicle speed prediction is important in predictive energy management strategies, and the accuracy of the prediction is beneficial for energy-saving performance. However, the nonlinear feature of the speed series hinders the improvement of prediction accuracy. In this study, a novel hybrid model that combines an autoregressive integrated moving average (ARIMA) and a long short-term memory (LSTM) model is proposed to handle the nonlinear part efficiently. Generally, the ARIMA component filters out linear trends from the speed series data, and the parameters of the ARIMA are determined with the analysis. Then the LSTM handles the residual normalized nonlinear items, which is the residual of ARIMA. Finally, the two parts of the prediction results are superimposed to obtain the final speed prediction results. To assess the performance of the hybrid model (ARIMA-LSTM), two tested driving cycles and two typical driving scenarios are subjected to rigorous analysis. The results demonstrate that the combined prediction model outperforms individual methods ARIMA and LSTM in dealing with complex, nonlinear variations, and exhibits significantly improved performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE). The proposed hybrid model provides a further improvement for the accuracy prediction of vehicle traveling processes.

State of charge (SOC) of ultracapacitor plays an important role in the energy management optimization of hybrid energy storage system for electric vehicles. In addition to the perfection of the model and the SOC estimation algorithm, the parameter identification method and temperature factor should also be considered. In this paper, an ultracapacitor test platform is established, the characteristic parameters of ultracapacitor at full temperature range are obtained. This paper uses the forgetting factor recursive least squares algorithm (FFRLS) to identify the parameters of the second-order equivalent circuit model of ultracapacitor online. The extended Kalman filter (EKF) algorithm is used to estimate the SOC of ultracapacitor cell. The results show that: (1) FFRLS algorithm can identify $R_0$, $R_1$, $R_2$, $C_1$, and $C_2$ values of ultracapacitor at full temperature range. Under the hybrid pulse power characterization working condition, the average mean absolute error between the estimated voltage and the actual voltage is about 0.0132 V. (2) EKF algorithm has a good adaptability to estimate SOC of ultracapacitor under different temperatures and working conditions. The SOC estimation error under different working conditions is low. From the perspective of mean square error, the estimation error at −20 °C is the lowest. (3) FFRLS and EKF joint estimation algorithm with good robustness and reliability can be used to estimate the SOC of ultracapacitor under different temperatures and working conditions. This study can provide a useful guidance for the parameter identification and SOC estimation of ultracapacitor for electric vehicle at different temperatures.