随着锂离子电池(Lithium-ion batteries,LIB)在电动汽车、储能电站和备用电源等领域的广泛应用,准确、及时地估计电池健康状态(State of health,SOH)是确保电池系统运行可靠性和安全性的关键因素.锂离子电池内部复杂的电化学反应和多变的外部使用条件,使得实现精准的健康状态估计具有挑战.随着人工智能、大数据分析等技术的快速发展,电池 SOH评估的方法也逐渐多样化.首先介绍电池的老化机理和 SOH概念,随后介绍了实验法、基于模型、数据驱动和融合方法,详细分析了每种方法的特点,并比较了在实际应用中相应的优势和局限性.最后,对SOH估算的未来趋势进行了展望.
Research Progress on State-of-health Estimating Method for Lithium-ion Batteries
With the widespread application of lithium-ion batteries in electric vehicles,energy storage stations,it is the key to ensure the reliability and safety of the battery to accurately estimate the health state of the battery.The complex electrochemical reaction inside lithium-ion battery and the changeable external use conditions make it challenging to realize accurate health state estimation and life prediction.With the rapid development of technologies such as artificial intelligence and big data analysis,the methods of battery health assessment have been gradually diversified.First,the aging mechanism and SOH concept of batteries are introduced in this article.Next,different SOH estimation methods are categorized into four classes:experiment-based,model-based,data-driven,and hybrid methods.The characteristics of each method are analyzed in detail,and the corresponding advantages and limitations in practical applications are compared.Finally,the future trends of SOH estimation are prospected.
NETL
NSTL
万方数据
维普
