In the battery management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm with forgetting factor. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1 V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112–2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172–0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

Aiming at the phenomenon of battery capacity regeneration, which leads to inaccurate prediction of lithium-ion battery state of health (SOH), a new fusion method based on ensemble empirical mode decomposition (EEMD), Pearson correlation analysis (PCA), and improved long short-term memory (LSTM) network and Gaussian function-trust region (GS-TR) algorithm is introduced to predict battery SOH. First, the EEMD method is adopted to process the battery SOH data to eliminate the impact of capacity recovery. Second, the decomposed data signals are classified by the PCA method, and the signals classified as high frequency and low frequency are respectively predicted by the improved LSTM algorithm and the GS-TR algorithm. Finally, the prediction results of the improved LSTM and GS-TR algorithms are integrated. The proposed fusion method avoids the complexity of the hybrid neural network model and improves the prediction efficiency. Based on the average results of the three data sets from NASA, the RMSE result of the proposed algorithm is reduced by 9.56% compared with the improved LSTM with the EEMD algorithm and 37.57% compared with the improved LSTM without the EEMD algorithm. The results show that the proposed method has higher adaptability and prediction accuracy.

Battery thermal management system is critical to prevent the battery pack from such safety issues as overheating, thermal runaway, and spontaneous combustion. Many research works have been done to improve the thermal performance of the thermal management system by reducing the maximum temperature of the battery pack. However, the temperature difference and energy consumption were not discussed in most of the researches. This paper proposed a framework of optimal design of the battery thermal management system using surrogate model and multi-objective optimization methodology. The accuracy of this method was then validated through two cases. The proposed framework aims to find a way to design a battery pack with at least two types of the following objectives: the smallest maximum temperature, smallest temperature deviation, and the lowest energy consumption. The framework can be divided into five steps: the structural design of the battery thermal management system; the fluid–solid coupled heat transfer modeling using computational fluid dynamics (CFD) method; the design of experiments and selection of surrogate models; the multi-objective optimization algorithm based on Pareto optimal solution; and the experimental verification. The optimized designs showed significant improvement by decreasing both the temperature rise and the energy consumption.