An overview of online implementable SOC estimation methods for Lithium-ion batteries

Meng Jinhao, Mattia Ricco, Luo Guangzhao, Maciej Jozef Swierczynski, Daniel-Ioan Stroe, Ana-Irina Stroe, Remus Teodorescu

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

15 Citationer (Scopus)

Resumé

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories,
that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn
OriginalsprogEngelsk
TitelProceedings of 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP)
Antal sider8
ForlagIEEE Press
Publikationsdatomaj 2017
Sider573-580
ISBN (Elektronisk)978-1-5090-4489-4
DOI
StatusUdgivet - maj 2017
Begivenhed2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP) - Brasov, Rumænien
Varighed: 25 maj 201727 maj 2017

Konference

Konference2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP)
LandRumænien
ByBrasov
Periode25/05/201727/05/2017

Fingerprint

Open circuit voltage
Spectroscopy
Lithium-ion batteries
Costs
Industry

Citer dette

Jinhao, M., Ricco, M., Guangzhao, L., Swierczynski, M. J., Stroe, D-I., Stroe, A-I., & Teodorescu, R. (2017). An overview of online implementable SOC estimation methods for Lithium-ion batteries. I Proceedings of 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP) (s. 573-580). IEEE Press. https://doi.org/10.1109/OPTIM.2017.7975030
Jinhao, Meng ; Ricco, Mattia ; Guangzhao, Luo ; Swierczynski, Maciej Jozef ; Stroe, Daniel-Ioan ; Stroe, Ana-Irina ; Teodorescu, Remus. / An overview of online implementable SOC estimation methods for Lithium-ion batteries. Proceedings of 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). IEEE Press, 2017. s. 573-580
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title = "An overview of online implementable SOC estimation methods for Lithium-ion batteries",
abstract = "With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.",
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Jinhao, M, Ricco, M, Guangzhao, L, Swierczynski, MJ, Stroe, D-I, Stroe, A-I & Teodorescu, R 2017, An overview of online implementable SOC estimation methods for Lithium-ion batteries. i Proceedings of 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). IEEE Press, s. 573-580, 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), Brasov, Rumænien, 25/05/2017. https://doi.org/10.1109/OPTIM.2017.7975030

An overview of online implementable SOC estimation methods for Lithium-ion batteries. / Jinhao, Meng; Ricco, Mattia; Guangzhao, Luo; Swierczynski, Maciej Jozef; Stroe, Daniel-Ioan; Stroe, Ana-Irina; Teodorescu, Remus.

Proceedings of 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). IEEE Press, 2017. s. 573-580.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AU - Jinhao, Meng

AU - Ricco, Mattia

AU - Guangzhao, Luo

AU - Swierczynski, Maciej Jozef

AU - Stroe, Daniel-Ioan

AU - Stroe, Ana-Irina

AU - Teodorescu, Remus

PY - 2017/5

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N2 - With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.

AB - With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.

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Jinhao M, Ricco M, Guangzhao L, Swierczynski MJ, Stroe D-I, Stroe A-I et al. An overview of online implementable SOC estimation methods for Lithium-ion batteries. I Proceedings of 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). IEEE Press. 2017. s. 573-580 https://doi.org/10.1109/OPTIM.2017.7975030