Li-ion Battery State of Health Estimation based on an improved Single Particle model

Nima Lotfi, Jie Li, R. Landers, Jonghyun Park
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引用次数: 26

Abstract

Health-conscious battery management is one of the main facilitators for widespread commercialization of Li-ion batteries as the primary power source in electrified transportation and portable electronics and as the backup source in stationary energy storage systems. The majority of the existing Battery Management Systems (BMSs) define battery State of Health (SOH) in terms of internal resistance increase or battery capacity decay and use various open-loop criteria based on the battery cycle number and/or operating conditions to determine its SOH. However, considering the wide range of operating conditions and current profiles for Li-ion batteries, the use of a closed-loop SOH estimation approach based on the measureable quantities of the battery along with a battery model is of great importance. In this work, the battery internal resistance increase which can be attributed to various chemical and mechanical degradation mechanisms is considered as the measure of the battery SOH. In order to estimate the SOH, a modified reduced-order electrochemical model based on the Single Particle (SP) Li-ion battery model is proposed to improve the traditional SP model accuracy. This model not only incorporates an analytical expression for the electrolyte-phase potential difference, it is also capable of accurately predicting the battery performance over a wide range of operating currents by considering the effects of the unmodeled dynamics. Finally, this model integrated with an adaptive output-injection observer to estimate the SP model states and the output model uncertainties, can be used to estimate the internal resistance increase during the battery lifetime. The modeling and estimation results are validated via a comparison to the full-order electrochemical model simulations.
基于改进单粒子模型的锂离子电池健康状态估计
健康电池管理是锂离子电池广泛商业化的主要促进因素之一,锂离子电池是电气化运输和便携式电子设备的主要电源,也是固定能量存储系统的备用电源。现有的大多数电池管理系统(bms)根据内阻增加或电池容量衰减来定义电池的健康状态(SOH),并使用基于电池循环次数和/或操作条件的各种开环标准来确定其SOH。然而,考虑到锂离子电池广泛的工作条件和电流分布,使用基于电池可测量量和电池模型的闭环SOH估计方法是非常重要的。本文将电池内阻的增加作为电池SOH的衡量指标,内阻的增加可归因于各种化学和机械降解机制。为了估计SOH,提出了一种基于单粒子(SP)锂离子电池模型的改进的降阶电化学模型,以提高传统SP模型的精度。该模型不仅包含了电解质相电位差的解析表达式,而且考虑了未建模动力学的影响,能够在大范围工作电流下准确预测电池的性能。最后,该模型结合自适应输出注入观测器来估计SP模型的状态和输出模型的不确定性,可用于估计电池寿命期间的内阻增量。通过与全阶电化学模型仿真的比较,验证了建模和估计结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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