基于真实内阻的锂-NMC 温度建模

Q2 Mathematics CFD Letters Pub Date : 2024-07-21 DOI:10.37934/cfdl.16.12.140148
Muhammad Fikri Irsyad Mat Razi, Zul Hilmi Che Daud, Zainab Asus, Izhari Izmi Mazali, Anuar Abu Bakar, Mohd Kameil Abdul Hamid
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引用次数: 0

摘要

锂离子电池(LIB)在充放电过程中会产生热量。充放电过程中产生的热量与电池内部直接相关。这些热量会导致电池温度升高。锂电池的工作温度非常重要,因为它会影响电池的性能和健康。通过实验收集电池的热行为是一个耗时、高成本且繁琐的过程。而通过电池热建模可以简化这一过程。本研究的目的是提供一种电池热模型,通过使用实验得出的实际内阻值,预测电池在宽温度范围内的热行为。在这项研究中,以 120 A(3C)和 160 A(4C)电流连续放电,加热容量为 40 Ah 的镍-锰-钴锂离子电池,直到达到设定的目标温度。然后以 40 A(1C)脉冲电流对电池放电,并测量电压响应。该过程重复进行,直至 80°C。根据电压响应数据计算出电池内阻,并将其作为基于发热方程的热模型的主要输入,以预测电池温度。结果表明,所开发的热模型能够精确预测电池的热行为,平均相对误差较小,约为 0.634 % 至 5.244%。这项研究的意义在于提供了一种电池模型,可以精确预测电池在宽温度范围内的热行为。这些信息对于设计更好的电池管理系统 (BMS) 以延长电池寿命、减缓电池退化率和避免安全风险非常重要。
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Li-NMC Temperature Modelling Based on Realistic Internal Resistance
Lithium-ion battery (LIB) produce heat when it is put under charging and discharging process. The heat generated during charging and discharging are directly related to the internal in the battery. This heat generation will cause the battery temperature to rise. The operating temperature for LIB is significantly important because its affect the performance and health of the battery. Gathering battery thermal behavior through experiment is a time consuming, high cost and a fussy process. The process can be made easier through battery thermal modelling. The purpose of this study is to provide a thermal battery model that can predict the battery thermal behavior at wide range of temperature by using realistic internal resistance value from experiment. In this study, a Nickel-Manganese-Cobalt Lithium-ion battery with capacity 40 Ah was discharged with 120 A (3C) and 160 A (4C) current continuously to heat up the battery until a set of targeted temperature achieved. The battery is then discharged with 40 A (1C) pulse current, and the voltage response is measured. The process was repeated until 80°C. From the voltage response data, the internal resistance for the battery was calculated and used as the main input in the thermal model based on heat generation equation to predict the battery temperature. The result shows that the developed thermal model managed to precisely predict battery thermal behaviour with a low average relative error of around 0.634 % to 5.244%. The significance of this study is to provide a battery model that can predict battery thermal behavior precisely at wide range of temperature. This information is important in designing a better battery management system (BMS) to prolong the battery lifetime, slowing degradation rate and avoid safety risk.
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来源期刊
CFD Letters
CFD Letters Chemical Engineering-Fluid Flow and Transfer Processes
CiteScore
3.40
自引率
0.00%
发文量
76
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