最大限度利用电动汽车二次电池的技术挑战和解决方案综述

IF 4.6 4区 化学 Q2 ELECTROCHEMISTRY Batteries Pub Date : 2024-02-27 DOI:10.3390/batteries10030079
F. Salek, S. Resalati, Meisam Babaie, P. Henshall, Denise Morrey, Lei Yao
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引用次数: 0

摘要

随着电动汽车(EV)在道路上行驶的数量不断增加,电池寿命到期的数量也随之增加。然而,这些电池仍有 75-80% 的剩余容量,这就为电力密集度较低的应用创造了二次利用的机会。利用这些二次寿命电池(SLBs)需要进行特定的准备工作,包括根据电池的健康状况(SoH)对电池进行分级;考虑最终用途要求重新包装;以及根据经过验证的理论模型开发精确的电池管理系统(BMS)。在本文中,我们对 SLB 的数学建模和实验分析进行了技术回顾,以应对 BMS 开发中的现有挑战。我们的综述显示,最近的研究大多侧重于环境和经济方面,而不是技术挑战。综述建议使用具有 2RC 和 3RC 的等效电路模型,这些模型在估计锂离子电池第二次寿命期间的性能方面表现出良好的准确性。此外,电化学阻抗谱(EIS)测试可提供有关锂离子电池降解历史和条件的宝贵信息。为解决历时老化机制问题,建议采用电化学模型,而不是经验模型,因为它们既有效又高效。此外,为了进行可靠的预测,建议使用合成负载数据生成基于真实应用场景的循环老化测试剖面图。人工智能算法有望预测 SLB 周期老化衰减参数,为实验室测试节省大量时间。我们的研究强调了关注技术挑战的重要性,以促进 SLB 在建筑储能系统和电动汽车充电站等固定应用中的有效利用。
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A Review of the Technical Challenges and Solutions in Maximising the Potential Use of Second Life Batteries from Electric Vehicles
The increasing number of electric vehicles (EVs) on the roads has led to a rise in the number of batteries reaching the end of their first life. Such batteries, however, still have a capacity of 75–80% remaining, creating an opportunity for a second life in less power-intensive applications. Utilising these second-life batteries (SLBs) requires specific preparation, including grading the batteries based on their State of Health (SoH); repackaging, considering the end-use requirements; and the development of an accurate battery-management system (BMS) based on validated theoretical models. In this paper, we conduct a technical review of mathematical modelling and experimental analyses of SLBs to address existing challenges in BMS development. Our review reveals that most of the recent research focuses on environmental and economic aspects rather than technical challenges. The review suggests the use of equivalent-circuit models with 2RCs and 3RCs, which exhibit good accuracy for estimating the performance of lithium-ion batteries during their second life. Furthermore, electrochemical impedance spectroscopy (EIS) tests provide valuable information about the SLBs’ degradation history and conditions. For addressing calendar-ageing mechanisms, electrochemical models are suggested over empirical models due to their effectiveness and efficiency. Additionally, generating cycle-ageing test profiles based on real application scenarios using synthetic load data is recommended for reliable predictions. Artificial intelligence algorithms show promise in predicting SLB cycle-ageing fading parameters, offering significant time-saving benefits for lab testing. Our study emphasises the importance of focusing on technical challenges to facilitate the effective utilisation of SLBs in stationary applications, such as building energy-storage systems and EV charging stations.
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来源期刊
Batteries
Batteries Energy-Energy Engineering and Power Technology
CiteScore
4.00
自引率
15.00%
发文量
217
审稿时长
7 weeks
期刊最新文献
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