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).

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 state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.