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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Xing et al. present a review of BMS for Battery Electric and Hybrid Vehicles considering state of charge, state of health, and state of life as key features.
“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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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× · see citing works
“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 · see citing works
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× · see citing works
“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.-I. (2007) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc.2007.4544139 · Cited by 1,246 · cited 1× · see citing works
“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× · see citing works
“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× · see citing works
“He [37] developed a degradation model for lithium-ion batteries according to a new exponential model.”
BackgroundSelf-citationTipping, M. E. (1999) · Cited by 869 · see citing works
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× · see citing works
“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× · see citing works
“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× · see citing works
“Cheng [17] and Tremblay [18] adopted the generic battery model that was integrated in MATLAB [19].”
BackgroundMethodInfluentialBattery balancing methods: A comprehensive review
Cao, J. B., Schofield, N., & Emadi, A. (2008) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc.2008.4677669 · Cited by 658 · cited 1× · see citing works
“imbalance [23].”
Simulation of capacity fade in lithium-ion batteries
Spotnitz, R. M. (2002) · Journal of Power Sources · doi:10.1016/s0378-7753(02)00490-1 · Cited by 575 · cited 1× · see citing works
“While taking into account SOH estimation, the battery degradation model based on capacity fade was simulated and built [20–22].”
MethodDetermination of state-of-charge and state-of-health of batteries by fuzzy logic methodology
Salkind, A. J., Fennie, C., Singh, P., Atwater, T., & Reisner, D. E. (1999) · Journal of Power Sources · doi:10.1016/s0378-7753(99)00079-8 · Cited by 457 · cited 2× · see citing works
“[29] utilized Electrochemical Impedance Spectroscopy (EIS) data to estimate SOC with fuzzy logic.”
BackgroundMethodBattery Management Systems for Large Lithium Ion Battery Packs
Andrea, D. (2010) · Cited by 405 · see citing works
Intelligent prognostics for battery health monitoring based on sample entropy
Widodo, A., Shim, M.-C., Caesarendra, W., & Yang, B.-S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.03.063 · Cited by 392 · cited 3× · see citing works
“Coup de Fouet voltage [35], internal resistance and transient current, were input into a modified extension matter-element model to develop intelligent SOH evaluation [32].”
BackgroundMethodSupport vector based battery state of charge estimator
Hansen, T., & Wang, C.-J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.09.020 · Cited by 347 · cited 1× · see citing works
“Hansen and Wang [30] applied a support vector machine (SVM) using both classification and regression to estimate the SOC without establishing a battery circuit model.”
MethodCapacity fade analysis of a lithium ion cell
Zhang, Q., & White, R. E. (2008) · Journal of Power Sources · doi:10.1016/j.jpowsour.2008.01.028 · Cited by 289 · cited 1× · see citing works
“While taking into account SOH estimation, the battery degradation model based on capacity fade was simulated and built [20–22].”
MethodUno, M., & Tanaka, K. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2127500 · Cited by 249 · cited 2× · see citing works
“Uno [24] studied the influence of high frequency on the life performance of lithium-ion cells.”
BackgroundBattery Monitoring and Electrical Energy Management
Meissner, E., & Richter, G. (2003) · Journal of Power Sources · doi:10.1016/s0378-7753(02)00713-9 · Cited by 245 · cited 1× · see citing works
“Meissner and Richter [7] proposed a layer structure for battery monitoring, battery state, and battery management.”
BackgroundMaterials and processing for lithium-ion batteries
Daniel, C. (2008) · JOM · doi:10.1007/s11837-008-0116-x · Cited by 192 · see citing works
A PSPICE macromodel for lithium-ion batteries
Gold, S. (2002) · doi:10.1109/bcaa.1997.574106 · Cited by 157 · cited 1× · see citing works
“Gold [8] categorized the different functions in a BMS.”
BackgroundOptimized battery-management system to improve storage lifetime in renewable energy systems
Kaiser, R. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.12.024 · Cited by 151 · cited 1× · see citing works
“A literature review has revealed that BMSs are still in a premature stage [40].”
BackgroundComprehensive dynamic battery modeling for PHEV applications
Zhang, H., & Chow, M. (2010) · IEEE Power Engineering Society General Meeting · doi:10.1109/pes.2010.5590108 · Cited by 148 · cited 1× · see citing works
“Currently, battery modeling for SOC determination is commonly developed from various equivalent circuit (RC network) models, which are distinct for different material characteristics and accuracy requirements [14–16].”
Kozlowski, J. D. (2004) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2003.1234169 · Cited by 136 · cited 5× · see citing works
“Some researchers have proposed adopting EIS to monitor internal impedance [12,26].”
BackgroundMethodInfluentialAnalysis of capacity fade in a lithium ion battery
Stamps, A. T., Holland, C. E., White, R. E., & Gatzke, E. P. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.02.033 · Cited by 102 · cited 1× · see citing works
“While taking into account SOH estimation, the battery degradation model based on capacity fade was simulated and built [20–22].”
MethodAutomotive battery management systems
Pattipati, B., Pattipati, K. R., Christopherson, J. P., Namburu, S. M., Prokhorov, D. V., & Qiao, L. (2008) · IEEE AUTOTESTCON (IEEE Systems Readiness Technology Conference) · doi:10.1109/autest.2008.4662684 · Cited by 101 · see citing works
A Modular Battery Management System for HEVs
Stuart, T. A., Fang, F., Wang, X., Ashtiani, C., & Pesaran, A. A. (2002) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2002-01-1918 · Cited by 73 · see citing works
Reliable battery operation — a challenge for the battery management system
Jossen, A., Späth, V., Döring, H., & Garche, J. (1999) · Journal of Power Sources · doi:10.1016/s0378-7753(99)00329-8 · Cited by 69 · cited 1× · see citing works
“Communication through a CAN Bus is a major way to transfer data within the BMS [17,38].”
Windarko, N. A., Choi, J., & Chung, G.-B. (2011) · doi:10.1109/icpe.2011.5944483 · Cited by 38 · cited 1× · see citing works
“Windarko [14] also adopted the EKF to estimate SOC based on an electrical model with two series RC networks.”
MethodChao, K., & Chen, J. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.05.084 · Cited by 27 · cited 2× · see citing works
“Cheng [17] and Tremblay [18] adopted the generic battery model that was integrated in MATLAB [19].”
BackgroundStandby Power System VRLA Battery Reserve Life Estimation Scheme
Pascoe, P. E., & Al‐Anbuky, A. (2005) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2005.853749 · Cited by 27 · cited 1× · see citing works
“Coup de Fouet voltage [35], internal resistance and transient current, were input into a modified extension matter-element model to develop intelligent SOH evaluation [32].”
MethodBattery state-of-charge estimation based on H∞ filter for hybrid electric vehicle
Yan, J., Xu, G., Xu, Y., & Xie, B. (2008) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2008.4795563 · Cited by 16 · see citing works
Flagged first, then by citations
1 of the 33 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Pattipati, B.; Pattipati, K.; Christopherson, J.P.; Namburu, S.M.; Prokhorov, D.V.; Qiao, L. Automotive Battery Management System. In Proceedings of IEEE AUTOTESTCON, Salt Lake City, UT, USA, 8-11 September 2008; pp. 581-586.
- Electric Vehicle Battery Test Procedures Manual, Revision 2; United States Advanced Battery Consortium: Southfield, MI, USA, 1996; Available online: http://www.mdpi.com/1996-1073/ 4/7/1102/pdf (accessed on 20 July 2011).
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 · see citing works
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 · see citing works
“[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.-X., Wang, S.-F., Wu, W., Chen, K., Hong, S.-H., & Lai, Y. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.12.051 · Cited by 1,088 · see citing works
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 · see citing works
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 1,022 · see citing works
Hannan, M. A., Hoque, M. M., Hussain, A., Yusof, Y., & Ker, P. J. (2018) · IEEE Access · doi:10.1109/access.2018.2817655 · Cited by 998 · see citing works
Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.07.008 · Cited by 977 · see citing works
How, D. N. T., Hannan, M. A., Lipu, M. S. H., & Ker, P. J. (2019) · IEEE Access · doi:10.1109/access.2019.2942213 · Cited by 823 · see citing works
Lipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.065 · Cited by 755 · see citing works
An ensemble model for predicting the remaining useful performance of lithium-ion batteries
Xing, Y., Eden, W. M., Tsui, K., & PECHT, M. G. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.003 · Cited by 675 · see citing works
Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.09.010 · Cited by 574 · see citing works
Review of Battery Management Systems (BMS) Development and Industrial Standards
Gabbar, H. A., Othman, M. I. A., & Abdussami, M. R. (2021) · Technologies · doi:10.3390/technologies9020028 · Cited by 484 · see citing works
“Battery Management System (BMS) connections and integrations [5].”
BackgroundInfluentialRemaining useful life prediction of lithium-ion battery with unscented particle filter technique
Miao, Q., Xie, L., Cui, H., Liang, W. Z., & PECHT, M. G. (2012) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.004 · Cited by 479 · see citing works
“Through battery health monitoring, state-of-charge (SOC) and state-of-health (SOH) for batteries could be estimated which provide useful information to the battery management systems (BMSs) [7].”
BackgroundKumar, R. R., Bharatiraja, C., Udhayakumar, K., Devakirubakaran, S., Sekar, K. S., & Mihet‐Popa, L. (2023) · IEEE Access · doi:10.1109/access.2023.3318121 · Cited by 497 · see citing works
“Within the battery pack, various sensors are strategically placed to collect data at the monitoring layer [11].”
Lipu, M. S. H., Hannan, M. A., Karim, T. F., Hussain, A., Saad, M. H. M., Ayob, A., Miah, M. S., & Mahlia, T. M. I. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126044 · Cited by 461 · see citing works
Ali, M. U., Zafar, A., Nengroo, S. H., Hussain, S., Alvi, M. J., & Kim, H. (2019) · Energies · doi:10.3390/en12030446 · Cited by 455 · see citing works
Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
Ren, L., Li, Z., Hong, S., Zhao, S., Wang, H. H., & Zhang, L. (2018) · IEEE Access · doi:10.1109/access.2018.2858856 · Cited by 434 · see citing works
“As a key power source for a variety of industrial systems, lithium-ion battery defects often lead to fatal system failures [8].”
BackgroundA review on various temperature-indication methods for Li-ion batteries
Raijmakers, L. H. J., Danilov, D. L., Eichel, R., & Notten, P. H. L. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.02.078 · Cited by 430 · see citing works
A review on prognostics and health management (PHM) methods of lithium-ion batteries
Meng, H., & Li, Y. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109405 · Cited by 393 · see citing works
A failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries
Hendricks, C., Williard, N., Mathew, S., & Pecht, M. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.07.100 · Cited by 381 · see citing works
Tian, Y., Lai, R., Li, X., Xiang, L., & Tian, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114789 · Cited by 357 · see citing works
Wang, D., Miao, Q., & Pecht, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.03.129 · Cited by 351 · see citing works
Adaikkappan, M., & Nageswari, S. (2021) · International Journal of Energy Research · doi:10.1002/er.7339 · Cited by 326 · see citing works
“BMS has some operating modules which are mainly divided into two groups, that is, hardware and software.(3) The overview of the components of BMS is shown in Figure 1.”
Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability
Williard, N., He, W., Hendricks, C., & PECHT, M. G. (2013) · Energies · doi:10.3390/en6094682 · Cited by 322 · see citing works
“Prediction algorithms [34–41] could further supplement these techniques to provide accurate time-to-failure estimations.”
MethodOmariba, Z. B., Zhang, L., & Sun, D. (2019) · IEEE Access · doi:10.1109/access.2019.2940090 · Cited by 333 · see citing works
“According to [95], overdischarge also causes reduced cell capacity due to irreversible chemical reactions and can cause an explosion [96].”
BackgroundReview of battery state estimation methods for electric vehicles - Part I: SOC estimation
Demirci, O., Taşkın, S., Schaltz, E., & Demirci, B. A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111435 · Cited by 318 · see citing works
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310 · see citing works
A novel health indicator for on-line lithium-ion batteries remaining useful life prediction
Zhou, Y., Huang, M., Chen, Y., & Tao, Y. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.119 · Cited by 289 · see citing works
Wang, D., Yang, F., Tsui, K., Zhou, Q., & Bae, S. J. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2534258 · Cited by 285 · see citing works
Liu, K., Wang, J., Yamamoto, T., & Morikawa, T. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.08.074 · Cited by 279 · see citing works
State of health and charge measurements in lithium-ion batteries using mechanical stress
Cannarella, J., & Arnold, C. B. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.003 · Cited by 278 · see citing works
Tang, S., Yu, C., Wang, X., Guo, X., & Si, X. (2014) · Energies · doi:10.3390/en7020520 · Cited by 279 · see citing works
“Failure of lithium-ion batteries could lead to performance degradation, operational impairment, and even catastrophic failure [3–5].”
BackgroundKey challenges for a large-scale development of battery electric vehicles: A comprehensive review
Lebrouhi, B. E., Khattari, Y., Lamrani, B., Maaroufi, M., Zeraouli, Y., & Kousksou, T. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103273 · Cited by 276 · see citing works
Chen, L., Lü, Z., Lin, W., Li, J., & Pan, H. (2017) · Measurement · doi:10.1016/j.measurement.2017.11.016 · Cited by 276 · see citing works
Shen, M., & Gao, Q. (2019) · International Journal of Energy Research · doi:10.1002/er.4433 · Cited by 273 · see citing works
Battery charge equalization controller in electric vehicle applications: A review
Hoque, M. M., Hannan, M. A., Mohamed, A., & Ayob, A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.11.126 · Cited by 269 · see citing works
Lai, X., Gao, W., Zheng, Y., Ouyang, M., Li, J., Han, X., & Zhou, L. (2018) · Electrochimica Acta · doi:10.1016/j.electacta.2018.11.134 · Cited by 268 · see citing works
Tran, M., Panchal, S., Khang, T. D., Panchal, K., Fraser, R. A., & Fowler, M. W. (2022) · Batteries · doi:10.3390/batteries8020019 · Cited by 248 · see citing works
“The original two main functions of the BMS were monitoring and protecting the battery [28].”
BackgroundSamanta, A., Chowdhuri, S., & Williamson, S. S. (2021) · Electronics · doi:10.3390/electronics10111309 · Cited by 237 · see citing works
“However, the safe and reliable operating area of the LIB is very narrow, which necessitates a battery management system (BMS) for effective operational control, protection and energy management [3–7].”
BackgroundXiong, R., Sun, F., Gong, X., & Gao, C. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.09.006 · Cited by 229 · see citing works
A review on passive and active strategies of enhancing the safety of lithium-ion batteries
Qiu, Y., & Jiang, F. (2021) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2021.122288 · Cited by 204 · see citing works
Yang, F., Xing, Y., Wang, D., & Tsui, K. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.11.072 · Cited by 208 · see citing works
Shrivastava, P., Naidu, P. A., Sharma, S., Panigrahi, B. K., & Garg, A. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107159 · Cited by 205 · see citing works
Wu, L., Fu, X., & Guan, Y. (2016) · Applied Sciences · doi:10.3390/app6060166 · Cited by 203 · see citing works
“Therefore, random-variable current RUL prediction is an enormous challenge [59].”
BackgroundXu, X., Tang, S., Yu, C., Xie, J., Han, X., & Ouyang, M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107675 · Cited by 193 · see citing works
Huang, Z., Yang, F., Xu, F., Song, X., & Tsui, K. (2019) · IEEE Access · doi:10.1109/access.2019.2928037 · Cited by 188 · see citing works
“However, this approach is appropriate in laboratory tests but requires some additional measurements that are not practical applications [4], [5].”
BackgroundMcCarthy, K., Gullapalli, H., Ryan, K., & Kennedy, T. (2021) · Journal of the Electrochemical Society · doi:10.1149/1945-7111/ac1a85 · Cited by 166 · see citing works
Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles
Hu, X., Li, S. E., Jia, Z., & Egardt, B. (2014) · Energy · doi:10.1016/j.energy.2013.11.061 · Cited by 187 · see citing works
Liu, D., Wang, H., Peng, Y., Xie, W. H., & Liao, H. (2013) · Energies · doi:10.3390/en6083654 · Cited by 186 · see citing works
Tran, M., Panchal, S., Chauhan, V., Brahmbhatt, N., Mevawalla, A., Fraser, R. A., & Fowler, M. W. (2021) · International Journal of Energy Research · doi:10.1002/er.7202 · Cited by 182 · see citing works
“16,17 The BMS acts as the brain of the battery system.”
BackgroundVrchota, J., Pech, M., Rolínek, L., & Bednář, J. (2020) · Sustainability · doi:10.3390/su12155968 · Cited by 175 · see citing works
“Transportation technology Clean transportation [221], electric vehicles [222], fuel cell cars [223], marine vessel propulsion [224], autonomous vehicles [225].”
BackgroundTürksoy, A., Teke, A., & Alkaya, A. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110274 · Cited by 165 · see citing works
Ragone, M., Yurkiv, V., Ramasubramanian, A., Kashir, B., & Mashayek, F. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.229108 · Cited by 162 · see citing works
Saiteja, P., & Ashok, B. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.112038 · Cited by 154 · see citing works
Development in energy storage system for electric transportation: A comprehensive review
Ahmad, F., Khalid, M., & Panigrahi, B. K. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103153 · Cited by 151 · see citing works
Wang, Q.-K., He, Y.-J., Shen, J.-N., Hu, X.-S., & Ma, Z.-F. (2018) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2017.2780184 · Cited by 146 · see citing works
Kumar, P., Chaudhary, D., Varshney, P., Varshney, U., Yahya, S. M., & Rafat, Y. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.102003 · Cited by 129 · see citing works
Battery management solutions for li-ion batteries based on artificial intelligence
Khawaja, Y., Shankar, N., Qiqieh, I., Alzubi, J. A. A., Alzubi, O. A., Nallakaruppan, M. K., & Padmanaban, S. (2023) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2023.102213 · Cited by 140 · see citing works
Norouzi, A., Heidarifar, H., Borhan, H., Shahbakhti, M., & Koch, C. R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105878 · Cited by 134 · see citing works
Ullah, F., Zhang, X., Khan, M., Mastoi, M. S., Munir, H. M., Flah, A., & Said, Y. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30466 · Cited by 142 · see citing works
“[155] Cost: Lowering the production and maintenance expenses of battery systems Manufacturing Processes : Streamlining production processes to reduce costs.”
BackgroundUzair, M., Abbas, G., & Hosain, S. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12030120 · Cited by 131 · see citing works
“The most difficult job is to estimate SOC by using a low cost BMS which should have small memory storage, but fast speed [11,18,19,23].”
BackgroundInfluentialUllah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2021) · International Journal of Green Energy · doi:10.1080/15435075.2021.1881902 · Cited by 133 · see citing works
“…factors include travel-related attributes (Liaw and Dubarry 2007), vehicle characteristics (Kambly and Bradley 2014), environment-related variables (Xing et al. 2011), traffic characteristics (K. Liu et al. 2016), roadway features (Travesset-Baro, Rosas-Casals, and Jover 2015), driver-related…”
BackgroundAnuphappharadorn, S., Sukchai, S., Sirisamphanwong, C., & Ketjoy, N. (2014) · Energy Procedia · doi:10.1016/j.egypro.2014.07.167 · Cited by 121 · see citing works
Parallel Architecture for Battery Charge Equalization
Dong, B., Li, Y., & Han, Y. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2014.2364838 · Cited by 121 · see citing works
McCarthy, K., Gullapalli, H., Ryan, K., & Kennedy, T. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104608 · Cited by 111 · see citing works
Internal resistance and polarization dynamics of lithium-ion batteries upon internal shorting
Noelle, D. J., Wang, M., Le, A. V., Shi, Y., & Qiao, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.12.086 · Cited by 117 · see citing works
Liu, X., Li, W., & Zhou, A. (2018) · IEEE Access · doi:10.1109/access.2018.2812421 · Cited by 117 · see citing works
Xia, Q., Wang, Z., Ren, Y., Sun, B., Yang, D., & Feng, Q. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.036 · Cited by 115 · see citing works
Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles
Omariba, Z. B., Zhang, L., & Sun, D. (2018) · Electronics · doi:10.3390/electronics7050072 · Cited by 114 · see citing works
“In addition to these functions, the BMS also monitors the system temperature to provide a better power consumption scheme, and communicates with individual components and operators [15].”
BackgroundDevelopment of Battery Monitoring System in Smart Microgrid Based on Internet of Things (IoT)
Friansa, K., Haq, I. N., Santi, B. M., Kurniadi, D., Leksono, E., & Yuliarto, B. (2017) · Procedia Engineering · doi:10.1016/j.proeng.2017.03.077 · Cited by 113 · see citing works
State of charge estimation for lithium-ion pouch batteries based on stress measurement
Dai, H., Yu, C., Wei, X., & Sun, Z. (2017) · Energy · doi:10.1016/j.energy.2017.04.099 · Cited by 112 · see citing works
He, Z., Gao, M., Wang, C., Wang, L. Y., & Liu, Y. (2013) · Energies · doi:10.3390/en6084134 · Cited by 112 · see citing works
“[25], in which battery state evaluation and battery modeling were listed as the major concerns.”
BackgroundA review of stochastic battery models and health management
Tao, L., Ma, J., Cheng, Y., Noktehdan, A., Chong, J., & Lü, C. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.127 · Cited by 111 · see citing works
A review of pivotal energy management strategies for extended range electric vehicles
Xiao, B., Ruan, J., Yang, W., Walker, P. D., & Zhang, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111194 · Cited by 110 · see citing works
Empirical Modeling of Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy Tests
Samadani, E., Farhad, S., Scott, W., Mastali, M., Gimenez, L. E., Fowler, M. W., & Fraser, R. A. (2015) · Electrochimica Acta · doi:10.1016/j.electacta.2015.02.021 · Cited by 104 · see citing works
Krishna, G., Singh, R., Gehlot, A., Akram, S. V., Priyadarshi, N., & Twala, B. (2022) · Electronics · doi:10.3390/electronics11172695 · Cited by 102 · see citing works
“Connections of a battery-management system and its integration [27].”
BackgroundShah, A., Shah, K., Shah, C., & Shah, M. (2022) · Renewable energy focus · doi:10.1016/j.ref.2022.06.001 · Cited by 99 · see citing works
Wang, D., Zhao, Y., Yang, F., & Tsui, K. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.02.027 · Cited by 97 · see citing works
Zhang, H., Mo, Z., Wang, J., & Miao, Q. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2896230 · Cited by 97 · see citing works
“Lithium-ion battery capacity will gradually decline due to usage, aging, and improper maintenance, which has a negative impact on the performance of power equipment and systems, and even leads to mission failures and casualties [4].”
BackgroundCloud-Based Artificial Intelligence Framework for Battery Management System
Shi, D., Zhao, J., Eze, C., Wang, Z., Wang, J., Lian, Y., & Burke, A. F. (2023) · Energies · doi:10.3390/en16114403 · Cited by 96 · see citing works
“For large-scale EV or grid-scale energy storage applications, BMS is a technology that monitors the performance of a battery system, which is typically composed of multiple battery cells arranged in a matrix configuration [22,23].”
BackgroundTseng, K.-H., Liang, J.-W., Chang, W., & Huang, S. (2015) · Energies · doi:10.3390/en8042889 · Cited by 92 · see citing works
“In Table 3, the second column is directly digested from [47] and the third column is the result of replicating their approach onto the present data of battery D.”
BackgroundLipu, M. S. H., Hannan, M. A., Hussain, A., & Saad, M. H. M. (2017) · Journal of Renewable and Sustainable Energy · doi:10.1063/1.5008491 · Cited by 93 · see citing works
Jin, N., Danilov, D. L., den Hof, P. M. J. V., & Donkers, M. C. F. (2018) · International Journal of Energy Research · doi:10.1002/er.4022 · Cited by 91 · see citing works
Suganya, R., Joseph, L. M. I. L., & Kollem, S. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103047 · Cited by 90 · see citing works
Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction
Jin, S., Sui, X., Huang, X., Wang, S., Teodorescu, R., & Stroe, D. (2021) · Electronics · doi:10.3390/electronics10243126 · Cited by 91 · see citing works
“A high-performance BMS can make the energy storage system operate reliably and extend the battery lifetime [4].”
BackgroundLaadjal, K., & Cardoso, A. J. M. (2021) · Electronics · doi:10.3390/electronics10131588 · Cited by 87 · see citing works
Huang, Q., Shen, Y., Huang, Y., Zhang, L., & Zhang, J. (2016) · Electrochimica Acta · doi:10.1016/j.electacta.2016.09.154 · Cited by 84 · see citing works
Pourrahmani, H., Yavarinasab, A., Zahedi, R., Gharehghani, A., Mohammadi, M. H., Bastani, P., & herle, J. V. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100579 · Cited by 82 · see citing works
Zeng, Z., Tian, J., Li, D., & Tian, Y. (2018) · Energies · doi:10.3390/en11010059 · Cited by 82 · see citing works
“Therefore, in order to prolong the battery’s lifetime as well as to ensure the battery operates reliability and safety, battery management systems (BMS) have to be developed to monitor and control the whole operating process of lithium-ion batteries [8].”
BackgroundHighly Effective Cell Equalization in a Lithium-Ion Battery Management System
Koseoglou, M., Tsioumas, E., Jabbour, N., & Mademlis, C. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2920728 · Cited by 80 · see citing works
“parts; however, they are more expensive and complicated in the implementation and, thus, more vulnerable to faults [25], [26].”
BackgroundWang, S., Zhao, L., Su, X., & Ma, P. (2014) · Energies · doi:10.3390/en7106492 · Cited by 80 · see citing works
A method to prolong lithium-ion battery life during the full life cycle
Zhu, J., Xu, W., Knapp, M., Darma, M. S. D., Mereacre, L., Su, P., Hua, W., Liu‐Théato, X., Dai, H., Wei, X., et al. (2023) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2023.101464 · Cited by 79 · see citing works
“14,15 Various stress factors, including temperature, 16–18 current rates, 19–21 lower/upper cutoff voltage, 22,23 state of charge (SoC), 24 and cycling depth of discharge (DoD), 25 are considered in battery lifespan management to evaluate the battery durability and develop suitable usage strategies.”
BackgroundZhang, Y., Chen, L., Li, Y., Zheng, X., Chen, J., & Jin, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103245 · Cited by 76 · see citing works
Hazardous scenarios identification for Li-ion secondary batteries
Bubbico, R., Greco, V., & Menale, C. (2018) · Safety Science · doi:10.1016/j.ssci.2018.04.024 · Cited by 74 · see citing works
Battery Management Systems for Electric Vehicles using Lithium Ion Batteries
Vaideeswaran, V., Bhuvanesh, S., & Devasena, M. (2019) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact44901.2019.8959965 · Cited by 74 · see citing works
“To improve the efficiency of the battery and also for its safe operation EV system need to be safely maintained [24].”
BackgroundGao, D., & Huang, M. (2017) · Journal of Power Electronics · doi:10.6113/jpe.2017.17.5.1288 · Cited by 74 · see citing works
Ofoegbu, E. O. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115833 · Cited by 74 · see citing works
Feng, F., Lu, R., Wei, G., & Zhu, C. (2015) · Energies · doi:10.3390/en8042950 · Cited by 74 · see citing works
“The most important function of the BMS is to estimate state of charge (SOC), which is provided to drivers to show remaining mileage, improve power distribution efficiency, and extend the expected life span of batteries [2,3].”
BackgroundHealth monitoring of lithium-ion batteries
Sood, B., Osterman, M. D., & PECHT, M. G. (2013) · IEEE Symposium on Product Compliance Engineering (ISPCE) · doi:10.1109/ispce.2013.6664165 · Cited by 72 · see citing works
Wang, C., Xu, M., Zhang, Q., Feng, J., Jiang, R., Wei, Y., & Liu, Y. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103244 · Cited by 69 · see citing works
- 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 › 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, hybrid electric vehicles, state of charge estimation, state of health estimation, battery state monitoring, charge control, battery balancing
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