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research-article

Battery State of Health Monitoring by Estimation of Side Reaction Current Density via Retrospective-Cost Subsystem Identification

[+] Author and Article Information
Xin Zhou

Graduate Student Research Assistant, Student Member of ASME Department of Mechanical Engineering University of Michigan G029 Walter E. Lay Automotive Laboratory, 1231 Beal Ave. Ann Arbor, Michigan 48109
zhouxin@umich.edu

Dennis S. Bernstein

Professor Department of Aerospace Engineering University of Michigan 3020 FXB Building, 1320 Beal Ave., Ann Arbor, Michigan 48109
dsbaero@umich.edu

Jeffrey L. Stein

Professor, Member of ASME Department of Mechanical Engineering University of Michigan 2292 GG Brown Laboratories, 2350 Hayward Str., Ann Arbor, Michigan 48109
stein@umich.edu

Tulga Ersal

Assistant Research Scientist, Member of ASME Department or Mechanical Engineering University of Michigan G029 Walter E. Lay Automotive Laboratory, 1231 Beal Ave. Ann Arbor, Michigan, 48109
tersal@umich.edu

1Corresponding author.

ASME doi:10.1115/1.4036030 History: Received March 30, 2016; Revised January 08, 2017

Abstract

This paper introduces a new method to monitor battery state of health (SOH) by estimating the side reaction current density as a direct SOH indicator. The estimation is formulated as an inaccessible subsystem identification problem, where the side reaction current density is treated as the output of an inaccessible battery health subsystem. Inaccessiblity in this context refers to the fact that the inputs and outputs of the subsystem are not measurable in-situ. This subsystem is identified using retrospective-cost subsystem identification (RCSI), and the output of the identified subsystem provides an estimate for the side reaction current density. Using an example parameter set for a LiFePO4 battery, simulations are performed to obtain estimates under various current profiles. These simulations show promising results in identifying the battery health subsystem and estimating the side reaction current density with RCSI under ideal conditions. Robustness of the algorithm under non-ideal conditions is analyzed. Estimation of the side reaction current density using RCSI is shown to be sensitive to non-ideal conditions that cause errors in the measurement or estimation of the battery voltage. A method for quantitatively assessing the impact of non-ideal conditions on the side reaction current estimation accuracy is provided. The proposed estimation technique, including the method for estimating the side reaction current density and the framework analyzing its robustness, can also be applied to other parameter sets and other battery chemistries to monitor the SOH change resulting from any electrochemical-based degradation mechanism that consumes cyclable Li-ions.

Copyright (c) 2017 by ASME
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