Channel parameters for the temperature distribution of a battery thermal management system with liquid cooling

The Li-ion battery is of paramount importance to electric vehicles (EVs). Propelled by the rapid growth of the EV industry, the performance of the battery is continuously improving. However, Li-ion batteries are susceptible to the working temperature and only obtain the optimal performance within an acceptable temperature range. Therefore, a battery thermal management system (BTMS) is required to ensure EVs’ safe operation. There are various basic methods for BTMS, including forced-air cooling, liquid cooling, phase change material (PCM), heat pipe (HP), thermoelectric cooling (TEC), etc. Every method has its unique application condition and characteristic. Furthermore, based on basic BTMS, more hybrid cooling methods adopting different basic methods are being designed to meet EVs’ requirements. In this work, the hybrid BTMS, as a more reliable and environmentally friendly method for the EVs, will be compared with basic BTMS to reveal its advantages and potential. By analyzing its cost, efficiency and other aspects, the evaluation criterion and design suggestions are put forward to guide the future development of BTMS.

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Optimization for Liquid Cooling Cylindrical Battery Thermal Management System Based on Gaussian Process Model

Journal of Thermal Science and Engineering Applications ◽

10.1115/1.4047526 ◽

2020 ◽

Vol 13 (2) ◽

Author(s):

Wei Li ◽

Akhil Garg ◽

Mi Xiao ◽

Liang Gao

Keyword(s):

Pressure Drop ◽

Gaussian Process ◽

Thermal Management ◽

Management System ◽

Process Model ◽

Liquid Cooling ◽

Battery Thermal Management ◽

Thermal Management System ◽

Battery Thermal Management System ◽

Gp Model

Abstract The power of electric vehicles (EVs) comes from lithium-ion batteries (LIBs). LIBs are sensitive to temperature. Too high and too low temperatures will affect the performance and safety of EVs. Therefore, a stable and efficient battery thermal management system (BTMS) is essential for an EV. This article has conducted a comprehensive study on liquid-cooled BTMS. Two cooling schemes are designed: the serpentine channel and the U-shaped channel. The results show that the cooling effect of two schemes is roughly the same, but the U-shaped channel can significantly decrease the pressure drop (PD) loss. The U-shaped channel is parameterized and modeled. A machine learning method called the Gaussian process (GP) model has been used to express the outputs such as temperature difference, temperature standard deviation, and pressure drop. A multi-objective optimization model is established using GP models, and the NSGA-II method is employed to drive the optimization process. The optimized scheme is compared with the initial design. The main findings are summarized as follows: the velocity of cooling water v decreases from 0.3 m/s to 0.22 m/s by 26.67%. Pressure drop decreases from 431.40 Pa to 327.11 Pa by 24.18%. The optimized solution has a significant reduction in pressure drop and helps to reduce parasitic power. The proposed method can provide a useful guideline for the liquid cooling design of large-scale battery packs.

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