In order to ensure thermal safety and extended cycle life of Lithium-ion batteries (LIBs) used in electric vehicles (EVs), a typical thermal management scheme was proposed as a reference design for the power battery pack. Through the development of the model for theoretical analysis and numerical simulation combined with the thermal management test bench, the designed scheme could be evaluated. In particular, the three-dimensional transient thermal model was used as the type of model. The test result verified the accuracy and the rationality of the model, but it also showed that the reference design could not reach the qualified standard of thermal performance of the power battery pack. Based on the heat dissipation strategy of liquid cooling, a novel improved design solution was proposed. The results showed that the maximum temperature of the power battery pack dropped by 1 °C, and the temperature difference was reduced by 2 °C, which improved the thermal performance of the power battery pack and consequently provides guidance for the design of the battery thermal management system (BTMS).

The prognosis of lithium-ion batteries for their remaining useful life is an essential technology in prognostics and health management (PHM). In this paper, we propose a novel hybrid prediction method based on particle filter (PF) and extreme learning machine (ELM). First, we use ELM to simulate the battery capacity degradation trend. Second, PF is applied to update the random parameters of the ELM in real-time. An extreme learning machine prognosis model, based on particle filter (PFELM), is established. In order to verify the validity of this method, our proposed approach is compared with the standard ELM, the multi-layer perceptron prediction model, based on PF (PFMLP), as well as the neural network prediction model, based on bat-particle filter (BATPFNN), using the batteries testing datasets of the National Aeronautics and Space Administration (NASA) Ames Research Center. The results show that our proposed approach has better ability to simulate battery capacity degradation trends, better robustness, and higher Remaining Useful Life (RUL) prognosis accuracy than the standard ELM, the PFMLP, and the BATPFNN under the same conditions.

Battery sorting is an important process in the production of lithium battery module and battery pack for electric vehicles (EVs). Accurate battery sorting can ensure good consistency of batteries for grouping. This study investigates the mechanism of inconsistency of battery packs and process of battery sorting on the lithium-ion battery module production line. Combined with the static and dynamic characteristics of lithium-ion batteries, the battery parameters on the production line that can be used as a sorting basis are analyzed, and the parameters of battery mass, volume, resistance, voltage, charge/discharge capacity and impedance characteristics are measured. The data of batteries are processed by the principal component analysis (PCA) method in statistics, and after analysis, the parameters of batteries are obtained. Principal components are used as sorting variables, and the self-organizing map (SOM) neural network is carried out to cluster the batteries. Group experiments are carried out on the separated batteries, and state of charge (SOC) consistency of the batteries is achieved to verify that the sorting algorithm and sorting result is accurate.

The battery pack is the core component of a new energy vehicle (NEV), and reducing the impact of vibration induced resonance from the ground is a prerequisite for the safety of an NEV. For a high-performance battery pack design, a clear understanding of the structural dynamics of the key part of battery pack, such as the battery module, is of great significance. Additionally, a proper computational model for simulations of battery module also plays a key role in correctly predicting the dynamic response of battery packs. In this paper, an experimental modal analysis (EMA) was performed on a typical commercial battery module, composed of twelve 37Ah lithium nickel manganese cobalt oxide (NMC) prismatic cells, to obtain modal parameters such as mode shapes and natural frequencies. Additionally, three modeling methods for a prismatic battery module were established for the simulation modal analysis. The method of simplifying the prismatic cell to homogenous isotropic material had a better performance than the detailed modeling method, in predicting the modal parameters. Simultaneously, a novel method that can quickly obtain the equivalent parameters of the cell was proposed. The experimental results indicated that the fundamental frequency of battery module was higher than the excitation frequency range (0–150 Hz) from the ground. The mode shapes of the simulation results were in good agreement with the experimental results, and the average error of the natural frequency was below 10%, which verified the validity of the numerical model.