Review
Open Access
Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review
Muaaz Bin Kaleem1Wei He2Heng Li1,∗
1 School of Computer Science and Engineering, Central South University, Changsha, China
2 School of Traffic and Transportation Engineering, Central South University, Changsha, China
  • Volume
    Volume 1 Issue 1, 2024
  • Citation
    Kaleem MB, He W, Li H. Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review. Artif. Intell. Auton. Syst. 2024(1):0003, https://doi.org/10.55092/aias20230003. 
  • DOI
    10.55092/aias20230003
  • Copyright
    Copyright2023 by the authors. Published by ELSP.
Abstract

Electric Vehicles (EVs) have transformed the automotive industry and are becoming a more reliable and consistent mode of public transportation. The development of a pollutionfree environment and improved ecological surroundings is being significantly assisted by battery-powered vehicles. Lithium-ion (Li-ion) batteries are the most widely used type of batteries in EVs because of their superior performance as compared to their counterparts. The core of EVs is their battery management systems (BMS), which can unarguably improve a battery’s performance, operation, safety, and lifespan. Li-ion battery state estimation is one of the most important parts of the implementation of BMS, as it serves an important role in safe and reliable battery operation. Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. The objective of this study is to review, characterize, and compare various ML-based approaches for the state estimation of different Li-ion battery states. Firstly, this study describes and characterizes several Li-ion battery state estimation approaches proposed in recent years. Secondly, the battery state estimation of electric vehicles is discussed. In addition, the challenges and prospects of Li-ion battery state estimation are put forward.

Keywords

battery management system; cloud computing; digital twin; electric vehicles; li-ion battery; machine learning; state estimation

Preview
References
  • [1]Adeyanju A, Manohar K. Effects of vehicular emission on environmental pollution in Lagos. Sci-Afric J Sci Issues Res Essays 2017, 5(4):34–51.
  • [2]Wu P, Shao G, Guo C, Lu Y, Dong X, et al. Long cycle life, low self-discharge carbon anode for Li-ion batteries with pores and dual-doping. J. Alloy. Compd. 2019, 802:620– 627.
  • [3]Takyi-Aninakwa P, Wang S, Zhang H, Li H, Xu W, et al. An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries. Energy 2022, 260:125093.
  • [4]Li W, Fan Y, Ringbeck F, Jöst D, Han X, et al. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter. J. Power Sources 2020, 476:228534.
  • [5]Berecibar M, Gandiaga I, Villarreal I, Omar N, Van Mierlo J, et al. Critical review of state of health estimation methods of Li-ion batteries for real applications. Renew. Sustain. Energy Rev. 2016, 56:572–587.
  • [6]Aaslid P, Geth F, Korpås M, Belsnes MM, Fosso OB. Non-linear charge-based battery storage optimization model with bi-variate cubic spline constraints. J. Energy Storage 2020, 32:101979.
  • [7]Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron. Markets 2021, 31(3):685–695.
  • [8] Qiu X, Wu W, Wang S. Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method. J. Power Sources 2020, 450:227700.
  • [9]Arora S. Selection of thermal management system for modular battery packs of electric vehicles: A review of existing and emerging technologies. J. Power Sources 2018, 400:621–640.
  • [10] Gao Z, Chin CS, Chiew JHK, Jia J, Zhang C. Design and implementation of a smart lithium-ion battery system with real-time fault diagnosis capability for electric vehicles. Energies 2017, 10(10):1503.
  • [11]Liao Z, Zhang S, Li K, Zhang G, Habetler TG. A survey of methods for monitoring and detecting thermal runaway of lithium-ion batteries. J. Power Sources 2019, 436:226879.
  • [12]Yang F, Li W, Li C, Miao Q. State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy 2019, 175:66–75.
  • [13]Hu X, Feng F, Liu K, Zhang L, Xie J, et al. State estimation for advanced battery management: Key challenges and future trends. Renew. Sustain. Energy Rev. 2019, 114:109334.
  • [14]Ghalkhani M, Habibi S. Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application. Energies 2022, 16(1):185.
  • [15]Eswar KNDVS, Doss MAN, Vishnuram P, Selim A, Bajaj M, et al. Comprehensive Study on Reduced DC Source Count: Multilevel Inverters and Its Design Topologies. Energies 2022, 16(1):18.
  • [16]Sase AA, Bhateshvar YK, Vora KC. Electric Vehicle Control System by using Controller Area Network Communication.
  • [17]Zahid T, Xu K, Li W, Li C, Li H. State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles. Energy 2018, 162:871–882.
  • [18]Venugopal P, Vigneswaran T. State-of-charge estimation methods for Li-ion batteries in electric vehicles. Int. J. Innov. Technol. Explor. Eng 2019, 8(7):37–46.
  • [19]Zhang R, Xia B, Li B, Cao L, Lai Y, et al. State of the art of lithium-ion battery SOC estimation for electrical vehicles. Energies 2018, 11(7):1820.
  • [20]Song Y, Liu D, Liao H, Peng Y. A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries. Appl. Energy 2020, 261:114408.
  • [21]Liu X, Li K, Wu J. Power battery SOC estimation based on EKF-SVM algorithm. Autom. Eng 2020, 42:1522–1528.
  • [22]Li J, Ye M, Meng W, Xu X, Jiao S. A novel state of charge approach of lithium ion battery using least squares support vector machine. IEEE Access 2020, 8:195398–195410.
  • [23]Ilott AJ, Mohammadi M, Schauerman CM, Ganter MJ, Jerschow A. Rechargeable lithium-ion cell state of charge and defect detection by in-situ inside-out magnetic resonance imaging. Nat. Commun. 2018, 9(1):1776.
  • [24]Tong S, Lacap JH, Park JW. Battery state of charge estimation using a load-classifying neural network. J. Energy Storage 2016, 7:236–243.
  • [25]Xu G, Du X, Li Z, Zhang X, Zheng M, et al. Reliability design of battery management system for power battery. Microelectron. Reliab. 2018, 88:1286–1292.
  • [26]Cui X, Xu B. State of charge estimation of lithium-ion battery using robust kernel fuzzy model and multi-innovation ukf algorithm under noise. IEEE Trans. Ind. Electron. 2021, 69(11):11121–11131.
  • [27]Khumprom P, Yodo N. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies 2019, 12(4):660.
  • [28]Chemali E, Kollmeyer PJ, Preindl M, Ahmed R, Emadi A. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans. Ind. Electron. 2017, 65(8):6730–6739.
  • [29]Yang F, Song X, Xu F, Tsui KL. State-of-charge estimation of lithium-ion batteries via long short-term memory network. IEEE Access 2019, 7:53792–53799.
  • [30]Chemali E, Kollmeyer PJ, Preindl M, Emadi A. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach. J. Power Sources 2018, 400:242–255.
  • [31]Xiao B, Liu Y, Xiao B. Accurate state-of-charge estimation approach for lithium-ion batteries by gated recurrent unit with ensemble optimizer. IEEE Access 2019, 7:54192– 54202.
  • [32]Liu D, Li L, Song Y, Wu L, Peng Y. Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions. Int. J. Electr. Power Energy Syst. 2019, 110:48–61.
  • [33] Nguyen HT, Walker CL, Walker EA. In A First Course in Fuzzy Logic, 4th, ed., Boca Raton: CRC Press, 2018.
  • [34]Li Y, Wang C, Gong J. A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty. Energy 2016, 109:933– 946.
  • [35]Sheng H, Xiao J. Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine. J. Power Sources 2015, 281:131–137.
  • [36]Saji D, Babu PS, Ilango K. SoC estimation of lithium ion battery using combined coulomb counting and fuzzy logic method. In 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, 17-18 May 2019, pp. 948–952.
  • [37]Huang SC, Tseng KH, Liang JW, Chang CL, Pecht MG. An online SOC and SOH estimation model for lithium-ion batteries. Energies 2017, 10(4):512.
  • [38]Yang D, Wang Y, Pan R, Chen R, Chen Z. A neural network based state-of-health estimation of lithium-ion battery in electric vehicles. Energy Procedia 2017, 105:2059– 2064.
  • [39]Pan H, Lü Z, Wang H, Wei H, Chen L. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine. Energy 2018, 160:466–477.
  • [40]Zhang W, Li X, Li X. Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation. Measurement 2020, 164:108052.
  • [41]Ali MU, Zafar A, Nengroo SH, Hussain S, Park GS, et al. Online remaining useful life prediction for lithium-ion batteries using partial discharge data features. Energies 2019, 12(22):4366.
  • [42]Gao D, Huang M. Prediction of remaining useful life of lithium-ion battery based on multi-kernel support vector machine with particle swarm optimization. J. Power Electron. 2017, 17(5):1288–1297.
  • [43]Chen Z, Xia X, Sun M, Shen J, Xiao R. State of health estimation of lithium-ion batteries based on fixed size LS-SVM. In 2018 IEEE Vehicle Power and Propulsion Conference (VPPC), Chicago, USA, 27-30 Aug. 2018, pp. 1–6.
  • [44]Kim J, Nikitenkov D. Fuzzy logic-controlled online state-of-health (SOH) prediction in large format LiMn 2 O 4 cell for energy storage system (ESS) applications. In 2014 IEEE International Conference on Industrial Technology (ICIT), New York: IEEE, 2014, pp. 474–479.
  • [45]Landi M, Gross G. Measurement techniques for online battery state of health estimation in vehicle-to-grid applications. IEEE Trans. Instrum. Meas. 2014, 63(5):1224–1234.
  • [46]Wu J, Zhang C, Chen Z. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Appl. Energy 2016, 173:134–140.
  • [47]Downey A, Lui YH, Hu C, Laflamme S, Hu S. Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds. Reliab. Eng. Syst. Saf. 2019, 182:1–12.
  • [48]Wu Y, Li W, Wang Y, Zhang K. Remaining useful life prediction of lithium-ion batteries using neural network and bat-based particle filter. IEEE Access 2019, 7:54843–54854.
  • [49] Zhou D, Li Z, Zhu J, Zhang H, Hou L. State of health monitoring and remaining useful life prediction of lithium-ion batteries based on temporal convolutional network. IEEE Access 2020, 8:53307–53320.
  • [50]Zhang S, Zhai B, Guo X, Wang K, Peng N, et al. Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks. J. Energy Storage 2019, 26:100951.
  • [51]Qu J, Liu F, Ma Y, Fan J. A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery. IEEE Access 2019, 7:87178–87191.
  • [52]Zhu J, Tan T, Wu L, Yuan H. RUL prediction of lithium-ion battery based on improved DGWO-ELM method in a random discharge rates environment. IEEE Access 2019, 7:125176–125187.
  • [53]Zhang Y, Xiong R, He H, Pecht MG. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans. Veh. Technol. 2018, 67(7):5695–5705.
  • [54]Patil MA, Tagade P, Hariharan KS, Kolake SM, Song T, et al. A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation. Appl. Energy 2015, 159:285–297.
  • [55]Du J, Zhang W, Zhang C, Zhou X. Battery remaining useful life prediction under coupling stress based on support vector regression. Energy Procedia 2018, 152:538–543.
  • [56]Wang Y, Ni Y, Lu S, Wang J, Zhang X. Remaining useful life prediction of lithium-ion batteries using support vector regression optimized by artificial bee colony. IEEE Trans. Veh. Technol. 2019, 68(10):9543–9553.
  • [57]Xiong R, Li L, Tian J. Towards a smarter battery management system: A critical review on battery state of health monitoring methods. J. Power Sources 2018, 405:18–29.
  • [58]Tran MK, Panchal S, Khang TD, Panchal K, Fraser R, et al. Concept review of a cloudbased smart battery management system for lithium-ion batteries: Feasibility, logistics, and functionality. Batteries 2022, 8(2):19.
  • [59]Yang S, Zhang Z, Cao R, Wang M, Cheng H, et al. Implementation for a cloud battery management system based on the CHAIN framework. Energy AI 2021, 5:100088.
  • [60]Haldar S, Mondal S, Mondal A, Banerjee R. Battery management system using state of charge estimation: An IOT based approach. In 2020 National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (NCETSTEA), Durgapur, 7-8 Feb., 2020, pp. 1–5.Haldar S, Mondal S, Mondal A, Banerjee R. Battery management system using state of charge estimation: An IOT based approach. In 2020 National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (NCETSTEA), Durgapur, 7-8 Feb., 2020, pp. 1–5.
  • [61]Sivaraman P, Sharmeela C. IoT-Based Battery Management System for Hybrid Electric Vehicle. Artif. Intell. Tech. Electr. Hybrid Electr. Veh. 2020, pp. 1–16.
  • [62]Kim T, Makwana D, Adhikaree A, Vagdoda JS, Lee Y. Cloud-based battery condition monitoring and fault diagnosis platform for large-scale lithium-ion battery energy storage systems. Energies 2018, 11(1):125.
  • [63]Al-Ali AR, Gupta R, Zaman Batool T, Landolsi T, Aloul F, et al. Digital twin conceptual model within the context of internet of things. Future Internet 2020, 12(10):163.
  • [64]Aheleroff S, Xu X, Zhong RY, Lu Y. Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model. Adv. Eng. Informatics 2021, 47:101225.
  • [65]Li W, Rentemeister M, Badeda J, Jöst D, Schulte D, et al. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-ofhealth estimation. J. Energy Storage 2020, 30:101557.
  • [66]Wang Y, Xu R, Zhou C, Kang X, Chen Z. Digital twin and cloud-side-end collaboration for intelligent battery management system. J. Manuf. Syst. 2022, 62:124–134.
  • [67]Li H, Kaleem MB, Chiu IJ, Gao D, Peng J. A Digital Twin Model for the Battery Management Systems of Electric Vehicles. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Haikou, China,20-22 Dec. 2021, pp. 1100–1107.
  • [68] Sancarlos A, Cameron M, Abel A, Cueto E, Duval JL, et al. From ROM of electrochemistry to AI-based battery digital and hybrid twin. Arch. Comput. Methods Eng. 2021, 28:979–1015.
  • [69]Li H, Kaleem MB, Chiu IJ, Gao D, Peng J, et al. An intelligent digital twin model for the battery management systems of electric vehicles. Int. J. Green Energy 2023, pp. 1–15.
  • [70]Kortmann F, Brieske D, Piekarek P, Eckstein J, Warnecke A, et al. Concept of a Cloud State Modeling System for Lead-Acid Batteries: Theory and Prototyping. In 2021 International Conference on Electronics, Information, and Communication (ICEIC), New York: IEEE, Jan. 31st-Feb. 3rd, 2021, pp. 1–4.
  • [71]Aheleroff S, Huang H, Xu X, Zhong RY. Toward sustainability and resilience with Industry 4.0 and Industry 5.0. Front Manufact Tech 2023.
  • [72]Lim KYH, Zheng P, Chen CH. A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. J. Intell. Manuf. 2020, 31:1313–1337.
Office Locations

Room 704, Cheuk Nang Centre, 9 Hillwood Road, Tsim Sha Tsui, Kowloon, Hong Kong

No.169th Liangui Road, Dongjing Street, Yuhua District, Changsha 410116, Hunan Province, China

Useful Links
About

ELSPublishing (ELSP) is an international publishing house dedicated to publishing high-quality journals, books, proceedings, and providing free conference system. ELSP is committed to promote scholarly communication and sharing, to build a globally integrated scholarly ecosystem, and to advance the cause for a wide range of scholars.

Website Copyright © 2024 ELSP