Thermal Analysis of LMO/Graphite Batteries Using Equivalent Circuit Models

电池(电) 荷电状态 电压 计算机科学 等效电路 汽车工程 热的 控制理论(社会学) 电气工程 工程类 控制(管理) 功率(物理) 热力学 物理 人工智能
作者
Nadjiba Mahfoudi,M’hamed Boutaous,Shihe Xin,Serge Buathier
出处
期刊:Batteries [Multidisciplinary Digital Publishing Institute]
卷期号:7 (3): 58-58 被引量:5
标识
DOI:10.3390/batteries7030058
摘要

An efficient thermal management system (TMS) of electric vehicles requires a high-fidelity battery model. The model should be able to predict the electro-thermal behavior of the battery, considering the operating conditions throughout the battery’s lifespan. In addition, the model should be easy to handle for the online monitoring and control of the TMS. Equivalent circuit models (ECMs) are widely used because of their simplicity and suitable performance. In this paper, the electro-thermal behavior of a prismatic 50 Ah LMO/Graphite cell is investigated. A dynamic model is adopted to describe the battery voltage, current, and heat generation. The battery model parameters are identified for a single cell, considering their evolution versus the state of charge and temperature. The needed experimental data are issued from the measurements carried out, thanks to a special custom electrical bench able to impose a predefined current evolution or driving cycles, controllable by serial interface. The proposed battery parameters, functions of state of charge (SOC), and temperature (T) constitute a set of interesting and complete data, not available in the literature, and suitable for further investigations. The thermal behavior and the dynamic models are validated using the New European Driving Cycle (NEDC), with a large operating time, higher than 3 h. The measurement and model prediction exhibit a temperature difference less than 1.2 °C and a voltage deviation less than 3%, showing that the proposed model accurately predicts current, voltage, and temperature. The combined effects of temperature and SOC provides a more efficient modeling of the cell behavior. Nevertheless, the simplified model with only temperature dependency remains acceptable. Hence, the present modeling constitutes a confident prediction and a real step for an online control of the complete thermal management of electrical vehicles.
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