The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
Battery Management Systems in Electric and Hybrid Vehicles
Open accessAbstract
The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery’s performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.
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“Other authors, such as Xing et al. [88], present a review of BMS for Battery Electric (BEVs) and Hybrid Vehicles (HVs) when considering the state of charge, the state of health, and the state of life, as the key features to be considered by BMSs.”
From A Review on Electric Vehicles: Technologies and Challenges (2021) · cites this paper as [88]
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Citations
Cites
Lithium Batteries: Status, Prospects and Future
Scrosati, B., & Garche, J. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2009.11.048 · Cited by 5,153 · cited 1×
“From the perspective of chemical characteristics, Scrosati and Garche [13] presented voltage-to-capacity profiles of several Li/Li+ materials.”
BackgroundExtended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009) · Applied Energy · doi:10.1016/j.apenergy.2008.11.021 · Cited by 1,636 · cited 2×
“The ratio of the currently available capacity to the maximum capacity can be expressed as SOC [25], which is calculated by Equation (1):”
BackgroundA Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles
Tremblay, O., Dessaint, L., & Dekkiche, A. (2007) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc.2007.4544139 · Cited by 1,309 · cited 1×
“Cheng [17] and Tremblay [18] adopted the generic battery model that was integrated in MATLAB [19].”
MethodBattery thermal models for hybrid vehicle simulations
Pesaran, A. A. (2002) · Journal of Power Sources · doi:10.1016/s0378-7753(02)00200-8 · Cited by 1,116 · cited 1×
“Thus, Pesaran [39] pointed out that it is important to reduce the temperature difference among cells, which must be monitored and operated under proper temperature conditions.”
BackgroundHe, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105 · cited 1×
“He [37] developed a degradation model for lithium-ion batteries according to a new exponential model.”
BackgroundSelf-citationTipping, M. E. (1999) · Cited by 869
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831 · cited 1×
“Saha [36] also used an RVM regression model, which was built using internal battery parameters from EIS data.”
MethodA review on prognostics and health monitoring of Li-ion battery
Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792 · cited 3×
“Determination of SOC and SOH will be integrated into a capability assessment, which also presents the life status of the battery and sets the operating limits according to state-of-the-art algorithms, such as fuzzy logic, neural networks, state-space-based models, and so on [17,27,29].”
BackgroundMethodBattery-Management System (BMS) and SOC Development for Electrical Vehicles
Cheng, K. W. E., Divakar, B. P., Wu, H., Ding, K., & Ho, H. F. J. (2010) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2010.2089647 · Cited by 696 · cited 5×
“Cheng [17] and Tremblay [18] adopted the generic battery model that was integrated in MATLAB [19].”
BackgroundMethodInfluential
Research works printed but not linked: 1
- Available online: http://www.mdpi.com/1996-1073/4/7/1102/pdf.
Cited by
Sorted by citations
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005
A Review on Electric Vehicles: Technologies and Challenges
Sanguesa, J. A., Torres-Sanz, V., Garrido, P., Martinez, F. J., & Marquez-Barja, J. M. (2021) · Smart Cities · doi:10.3390/smartcities4010022 · Cited by 1,367
“[88], present a review of BMS for Battery Electric (BEVs) and Hybrid Vehicles (HVs) when considering the state of charge, the state of health, and the state of life, as the key features to be considered by BMSs.”
A critical review of battery thermal performance and liquid based battery thermal management
Wu, W., Wang, S., Wu, W. T., Chen, K., Hong, S., & Lai, Y. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.12.051 · Cited by 1,243
Review of energy storage systems for electric vehicle applications: Issues and challenges
Hannan, M. A., Hoque, M. M., Mohamed, A., & Ayob, A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.11.171 · Cited by 1,067
Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
Xiong, R., Cao, J., Yu, Q., He, H., & Sun, F. (2017) · IEEE Access · doi:10.1109/access.2017.2780258 · Cited by 1,046
Metadata
- Authors
- Xing, Y., Ma, E. W. M., Tsui, K. L., & Pecht, M.
- Affiliations
- City University of Hong Kong (HK)
- Corresponding author
- Pecht, Michael
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2011-10-31
- Volume
- 4
- Issue
- 11
- Pages
- 1840-1857
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2095027161
- Cited by
- 540
- References
- 33
- Citations by year
- 2026: 35 · 2025: 48 · 2024: 50 · 2023: 53 · 2022: 43 · 2021: 63 · 2020: 37 · 2019: 43 · 2018: 31 · 2017: 37 · 2016: 27 · 2015: 26 · 2014: 24 · 2013: 18 · 2012: 5
- FWCI
- 30.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CityU Scholars (submittedVersion, cc-by)
- Funders
- City University of Hong Kong
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- 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, hybrid electric vehicles, state of charge estimation, state of health estimation, battery state monitoring, charge control, battery balancing
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