A Single Particle Model with Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation

Abstract

State of Health (SOH) estimation of lithium ion batteries is critical for Battery Management Systems (BMSs) in Electric Vehicles (EVs). Many estimation techniques utilize a battery model; however, the model must have high accuracy and high computational efficiency. Conventional electrochemical full-order models can accurately capture battery states, but they are too complex and computationally expensive to be used in a BMS. A Single Particle (SP) model is a good alternative that addresses this issue; however, existing SP models do not consider degradation physics. In this work, an SP-based degradation model is developed by including Solid Electrolyte Interface (SEI) layer formation, coupled with crack propagation due to the stress generated by the volume expansion of the particles in the active materials. A model of lithium ion loss from SEI layer formation is integrated with an advanced SP model that includes electrolytic physics. This low-order model quickly predicts capacity fade and voltage profile changes as a function of cycle number and temperature with high accuracy, allowing for the use of online estimation techniques. Lithium ion loss due to SEI layer formation, increase in battery resistance, and changes in the electrodes' open circuit potential operating windows are examined to account for capacity fade and power loss. In addition to the low-order implementation to facilitate on-line estimation, the model proposed in this paper provides quantitative information regarding SEI layer formation and crack propagation, as well as the resulting battery capacity fade and power dissipation, which are essential for SOH estimation in a BMS.

Department(s)

Mechanical and Aerospace Engineering

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research

Comments

The authors gratefully acknowledge the financial support from the National Science Foundation (award number CMMI-1538415).

Keywords and Phrases

Battery management systems; Capacity degradation; On-line estimation; Power loss; Single particle model; State of health

International Standard Serial Number (ISSN)

0306-2619

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Elsevier Ltd, All rights reserved.

Publication Date

01 Feb 2018

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