Introduction

Bowen Li; Carlos Fernandez; Chunmei Yu; Donglei Liu; Daniel Ioan Stroe; Huan Li; Kailong Liu; Lili Xia; Peng Yu; Shunli Wang; Weihao Shi; Weikang Ji; Wenhua Xu; Xianyong Xiao; Xiaoyong Yang

doi:10.1016/B978-0-443-16160-5.00007-X

Introduction

Bowen Li, Carlos Fernandez, Chunmei Yu, Donglei Liu, Daniel Ioan Stroe, Huan Li, Kailong Liu, Lili Xia, Peng Yu, Shunli Wang, Weihao Shi, Weikang Ji, Wenhua Xu, Xianyong Xiao, Xiaoyong Yang

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Bidrag til bog/antologi › Forskning › peer review

Abstract

As the main source of automotive energy supply and storage, automotive lithium-ion battery packs are indispensable in the overall energy supply system of automobiles. Therefore, Battery Management System (BMS), as the main aspect in energy management and safety monitoring of new energy vehicles, has become an indispensable core of new energy vehicles. BMS not only accurately measures the battery voltage, current, temperature, and other key reference information, simultaneously carries out system safety diagnosis and hazard alarm, but also predicts energy state estimation, peak power estimation, state of charge, and state of health to enhance efficiency of energy utilization and improve working condition. Therefore, the estimation of four core state parameters of lithium-ion batteries is of great significance for reliable, safe, and long-lasting use of the batteries.

Originalsprog	Engelsk
Titel	State Estimation Strategies in Lithium-ion Battery Management Systems
Redaktører	Shunli Wang, Kailong Liu, Yujie Wang, Daniel-Ioan Stroe, Carlos Fernandez, Josep M. Guerrero
Antal sider	25
Forlag	Elsevier
Publikationsdato	1 jan. 2023
Sider	1-25
Kapitel	1
ISBN (Trykt)	9780443161612
ISBN (Elektronisk)	9780443161605
DOI	https://doi.org/10.1016/B978-0-443-16160-5.00007-X
Status	Udgivet - 1 jan. 2023

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© 2023 Elsevier Inc. All rights reserved.

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10.1016/B978-0-443-16160-5.00007-X

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Li, B., Fernandez, C., Yu, C., Liu, D., Stroe, D. I., Li, H., Liu, K., Xia, L., Yu, P., Wang, S., Shi, W., Ji, W., Xu, W., Xiao, X., & Yang, X. (2023). Introduction. I S. Wang, K. Liu, Y. Wang, D.-I. Stroe, C. Fernandez, & J. M. Guerrero (red.), State Estimation Strategies in Lithium-ion Battery Management Systems (s. 1-25). Elsevier. https://doi.org/10.1016/B978-0-443-16160-5.00007-X

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Introduction. / Li, Bowen; Fernandez, Carlos; Yu, Chunmei et al.
State Estimation Strategies in Lithium-ion Battery Management Systems. red. / Shunli Wang; Kailong Liu; Yujie Wang; Daniel-Ioan Stroe; Carlos Fernandez; Josep M. Guerrero. Elsevier, 2023. s. 1-25.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Bidrag til bog/antologi › Forskning › peer review

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AU - Xia, Lili

AU - Yu, Peng

AU - Wang, Shunli

AU - Shi, Weihao

AU - Ji, Weikang

AU - Xu, Wenhua

AU - Xiao, Xianyong

AU - Yang, Xiaoyong

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N2 - As the main source of automotive energy supply and storage, automotive lithium-ion battery packs are indispensable in the overall energy supply system of automobiles. Therefore, Battery Management System (BMS), as the main aspect in energy management and safety monitoring of new energy vehicles, has become an indispensable core of new energy vehicles. BMS not only accurately measures the battery voltage, current, temperature, and other key reference information, simultaneously carries out system safety diagnosis and hazard alarm, but also predicts energy state estimation, peak power estimation, state of charge, and state of health to enhance efficiency of energy utilization and improve working condition. Therefore, the estimation of four core state parameters of lithium-ion batteries is of great significance for reliable, safe, and long-lasting use of the batteries.

AB - As the main source of automotive energy supply and storage, automotive lithium-ion battery packs are indispensable in the overall energy supply system of automobiles. Therefore, Battery Management System (BMS), as the main aspect in energy management and safety monitoring of new energy vehicles, has become an indispensable core of new energy vehicles. BMS not only accurately measures the battery voltage, current, temperature, and other key reference information, simultaneously carries out system safety diagnosis and hazard alarm, but also predicts energy state estimation, peak power estimation, state of charge, and state of health to enhance efficiency of energy utilization and improve working condition. Therefore, the estimation of four core state parameters of lithium-ion batteries is of great significance for reliable, safe, and long-lasting use of the batteries.

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KW - energy state estimation

KW - peak power estimation

KW - state of charge

KW - state of health

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Introduction

Abstract

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