Automotive Innovation, January 2021, DOI: 10.1007/s42154-020-00128-8

Joint Estimation of Inconsistency and State of Health for Series Battery Packs


Yunhong Che1, Aoife Foley2,3, Moustafa El‑Gindy4, Xianke Lin4, Xiaosong Hu1 and Michael G. Pecht5

1 Department of Automotive Engineering, Chongqing University, Chongqing 400044, China
2 School of Mechanical & Aerospace Engineering, Queen’s University, Belfast BT9 5AH, UK
3 School of Engineering, Trinity College Dublin, University of Dublin, College Green, Dublin 2, Ireland
4 Department of Automotive, Mechanical and Manufacturing Engineering, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
5 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA

Abstract:

Battery packs are applied in various areas (e.g., electric vehicles, energy storage, space, mining, etc.), which requires the state of health (SOH) to be accurately estimated. Inconsistency, also known as cell variation, is considered a signifcant evaluation index that greatly afects the degradation of battery pack. This paper proposes a novel joint inconsistency and SOH estimation method under cycling, which flls the gap of joint estimation based on the fast-charging process for electric vehicles. First, fifteen features are extracted from current change points during the partial charging process. Then, a joint estimation system is designed, where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency. A wrapper is used to select the optimal feature subset, and Gaussian process regression is implemented to estimate the SOH. Finally, the estimation performance is assessed by the test data. The results show that the inconsistency evaluation can refect the aging conditions, and the inconsistency does afect the aging process. The wrapper selection method improves the accuracy of SOH estimation by about 75.8% compared to the traditional flter method when only 10% of data is used for model training. The maximum absolute error and root mean square error are 2.58% and 0.93%, respectively.

This article is available online here.

[Home Page] [Articles Page]
Copyright © 2021 by CALCE and the University of Maryland, All Rights Reserved