Lithium-ion Battery State-of-Health Estimation in Electric Vehicle Using Optimized Partial Charging Voltage Profiles

Meng Jinhao, Lei Cai, Daniel-Ioan Stroe, Luo Guangzhao, Xin Sui, Remus Teodorescu

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)

Abstract

Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method.
Original languageEnglish
JournalEnergy
Volume185
Pages (from-to)1054-1062
Number of pages9
ISSN0360-5442
DOIs
Publication statusPublished - Oct 2019

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Electric vehicles
Health
Electric potential
Sorting
Genetic algorithms
Charging (batteries)
Manganese
Cobalt
Aging of materials
Nickel
Lithium-ion batteries

Keywords

  • State of health estimation
  • Partial voltage range
  • Lithium-ion battery
  • Electric vehicle
  • Non-dominated sorting genetic algorithm

Cite this

@article{9b4019df5d764f2483c0dc09777b72bb,
title = "Lithium-ion Battery State-of-Health Estimation in Electric Vehicle Using Optimized Partial Charging Voltage Profiles",
abstract = "Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method.",
keywords = "State of health estimation, Partial voltage range, Lithium-ion battery, Electric vehicle, Non-dominated sorting genetic algorithm",
author = "Meng Jinhao and Lei Cai and Daniel-Ioan Stroe and Luo Guangzhao and Xin Sui and Remus Teodorescu",
year = "2019",
month = "10",
doi = "10.1016/j.energy.2019.07.127",
language = "English",
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pages = "1054--1062",
journal = "Energy",
issn = "0360-5442",
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Lithium-ion Battery State-of-Health Estimation in Electric Vehicle Using Optimized Partial Charging Voltage Profiles. / Jinhao, Meng; Cai, Lei; Stroe, Daniel-Ioan; Guangzhao, Luo; Sui, Xin; Teodorescu, Remus.

In: Energy, Vol. 185, 10.2019, p. 1054-1062.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Lithium-ion Battery State-of-Health Estimation in Electric Vehicle Using Optimized Partial Charging Voltage Profiles

AU - Jinhao, Meng

AU - Cai, Lei

AU - Stroe, Daniel-Ioan

AU - Guangzhao, Luo

AU - Sui, Xin

AU - Teodorescu, Remus

PY - 2019/10

Y1 - 2019/10

N2 - Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method.

AB - Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method.

KW - State of health estimation

KW - Partial voltage range

KW - Lithium-ion battery

KW - Electric vehicle

KW - Non-dominated sorting genetic algorithm

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