A Practical Data-Driven Battery State-of-Health Estimation for Electric Vehicles

In this article, to estimate the battery state of health (SOH) under realistic electric vehicle (EV) conditions, a robust and efficient data-driven algorithm is developed and validated through comprehensive battery life testing. More than 50 state-of-the-art EV battery cells have been tested under a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 70; no. 2; pp. 1973 - 1982
Main Authors: Khaleghi Rahimian, Saeed, Tang, Yifan
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Batteries
Battery management system (BMS)
battery state of health (SOH)
Charging
Cycles
data-driven algorithms
Datasets
Driving conditions
Electric potential
Electric vehicles
Estimation
Feature extraction
Heuristic algorithms
Li-ion batteries
Neural networks
neural networks (NNs)
Protocols
Rechargeable batteries
State of charge
Training
Voltage
ISSN:0278-0046, 1557-9948
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this article, to estimate the battery state of health (SOH) under realistic electric vehicle (EV) conditions, a robust and efficient data-driven algorithm is developed and validated through comprehensive battery life testing. More than 50 state-of-the-art EV battery cells have been tested under a variety of cycling conditions with different charging protocols, dynamic driving cycles, voltage ranges, pulse rates, and temperatures. Some of the cells have also been tested under a combination of cycling and storage conditions, constant current and multistep charging, and a periodic temperature variation that mimics real life conditions. Only partial data (voltage, current, and temperature) within a narrow state-of-charge range under a dynamic driving condition are required to extract the health indicators. A neural network is trained to find the mapping between the health features and the battery SOH. The life test data are divided into three groups. The first dataset ( 55% of data) is used for training and initial validation and testing, whereas the second and third datasets ( 45% of data) are entirely used for the final validation and testing to minimize the network overfitting. The results show that the SOH estimation root-mean-squared error for all datasets is less than 0.9%, signifying the fidelity and reliability of the proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3165295