State of Charge and State of Health estimation in large lithium-ion battery packs

被引：7

作者：

Bhaskar, Kiran ^{[1
]}

Kumar, Ajith ^{[2
]}

Bunce, James ^{[2
]}

Pressman, Jacob ^{[2
]}

Burkell, Neil ^{[2
]}

Miller, Nathan ^{[2
]}

Rahn, Christopher D. ^{[1
]}

机构：

[1] Penn State Univ, Dept Mech Engn, University Pk, PA 16802 USA

[2] Wabtec Corp, Erie, PA 16531 USA

来源：

2023 AMERICAN CONTROL CONFERENCE, ACC | 2023年

关键词：

SOC ESTIMATION; KALMAN FILTER; MANAGEMENT-SYSTEMS; SENSOR BIAS; OF-CHARGE; OBSERVER;

D O I：

10.23919/ACC55779.2023.10156326

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate, real-time state of charge (SoC) and state of health (SoH) estimation is essential for lithium-ion battery management systems to ensure safe and extended life of battery packs. For the large battery packs associated with battery electric locomotives and grid applications, computational efficiency is critical, especially for onboard implementation. This paper presents real-time SoC and batch least squares SoH and current sensor bias estimation using measured cell voltage and current from large battery packs. An online gradient-based SoH estimator, coupled with the online SoC estimator, provides real-time onboard health monitoring. The online and offline SoC-SoH algorithms are tested using data from a battery electric locomotive. The SoC-SoH estimation results show tightly clustered capacity, resistance, and current sensor bias estimates for an 11-cell module. The batch and online capacity estimates match to within 5% after the startup transients decay.

引用

页码：3075 / 3080

页数：6

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