A brief review on key technologies in the battery management system of electric vehicles
Open accessAbstract
Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.
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ML methods can be utilized for both SOH estimation and RUL prediction, but there is a large difference between the two applications in terms of input features and desired output.
“While ML methods can be utilized for both SOH estimation and RUL prediction [122,123], there is a large difference between the two applications in terms of input features and the desired output.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [123]
Li-ion batteries have a longer cycle life than other secondary battery types, which is a major benefit for guaranteeing that electric vehicles have an extended lifespan (usually 6-10 years).
“Table 3 lists the salient features of several popular battery types showing that Li-ion batteries have a longer cycle life than other kinds, which is a major benefit for guaranteeing that electric vehicles (EVs) having an extended lifespan (usually 6 -10 years) [14 -16].”
From Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review (2024) · cites this paper as [14]
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- Authors
- Liu, K., Li, K., Peng, Q., & Zhang, C.
- Affiliations
- Queen's University Belfast (GB)
- Nanjing University of Information Science and Technology (CN)
- University of Warwick (GB)
- Corresponding author
- Li, Kang
- Venue
- Frontiers of Mechanical Engineering
- Published
- 2018-04-02
- Volume
- 14
- Issue
- 1
- Pages
- 47-64
- ISSN
- 2095-0233 (ISSN-L)
- 2095-0241
- OpenAlex
- W2796379873
- Cited by
- 635
- References
- 139
- Citations by year
- 2026: 53 · 2025: 91 · 2024: 97 · 2023: 101 · 2022: 127 · 2021: 78 · 2020: 43 · 2019: 39 · 2018: 5 · 2014: 1
- FWCI
- 21.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Frontiers of Mechanical Engineering (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Research Portal (Queen's University Belfast) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61640316, EP/L 001063/1, 61673256, 61533010)
- Engineering and Physical Sciences Research Council (EP/L001063/1)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- battery management systems, electric vehicles, battery modeling, electro-thermal modeling, thermal modeling, state of charge estimation, state of health estimation, battery temperature prediction, battery charging, charging optimization, hybrid electric vehicles
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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