The battery thermal management system is a key skill that has been widely used in power battery cooling and preheating. It can ensure that the power battery operates safely and stably at a suitable temperature. In this article, we summarize mainly summarizes the current situation for the research on the thermal management system of power battery, comprehensively compares and analyzes four kinds of cooling systems including air cooling, liquid cooling, phase-change materials and heat pipe, two types of heating systems including internal heating and external heating, and the corresponding characteristics of the coupled system in no less than two ways. It is found that liquid cooling system and its heating system, phase-change material cooling system and it is heating system, heat pipe cooling system, coupling cooling system and its heating system have great research prospects, it also provides a certain reference for future research directions.

Remaining useful life prediction is of great significance for battery safety and maintenance. The remaining useful life prediction method, based on a physical model, has wide applicability and high prediction accuracy, which is the research hotspot of the next generation battery life prediction method. In this study, the prediction methods of battery life were compared and analyzed, and the prediction methods based on the physical model were summarized. The prediction methods were classified according to their different characteristics including the electrochemical model, equivalent circuit model, and empirical model. By analyzing the emphasis of electrochemical process simplification, different electrochemical models were classified including the P2D model, SP model, and electrochemical fusion model. The equivalent circuit model was divided into the Rint model, Thevenin model, PNGV model, and RC model for the change of electronic components in the model. According to the different mathematical expressions of constructing the empirical model, it can be divided into the exponential model, polynomial model, exponential and polynomial mixed model, and capacity degradation model. Through the collocation of different filtering methods, the different efficiency of the models is described in detail. The research progress of various prediction methods as well as the changes and characteristics of traditional models were compared and analyzed, and the future development of battery life prediction methods was prospected.

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In order to further improve the power and fuel economy of hybrid vehicles, this paper proposes a method of hybrid vehicle powertrain matching by combining orthogonal tests with Cruise software, supplemented by the control strategy formulation of critical components of the whole vehicle on the MATLAB/Simulink platform. Considering the influence of vehicle engine, electric motor, battery and overall mass on the powertrain design, the L9(34)-type orthogonal table is selected for the orthogonal test design. After verifying the feasibility and accuracy of each design solution of the powertrain, the different design solutions are simulated for power and economic performance. Finally, the best performance indicators of the vehicle are as follows: the maximum speed is 183.35 km/h, the 0–100 km/h acceleration time is 6.87 s, and the maximum degree of climbing is 39.65 percent. The fuel consumption of 100 km is 3.47 L. The optimal solution was compared with the third-generation Harvard H6 and AITO M5 in terms of fuel saving and emission reduction, and it was found that for every 15,000 km driven, it is expected to save 469.5 L of fuel and 109.5 L of CO2, respectively, which can reduce fuel use and emission by about 1051.21 kg and 245.17 kg CO2, respectively. This simulation experiment can reduce the workload of traditional power system matching. It can provide ideas for power system matching and optimization for Corun CHS Technology Co., Ltd. (Foshan City, Guangdong Province, China) and offer a certain degree of reference for hybrid vehicle power system design and simulation.