Intelligent Maintenance of Electric Vehicle Battery Charging Systems and Networks Challenges and Opportunities



Published Feb 13, 2023
Yuan-Ming Hsu Dai-Yan Ji Marcella Miller Xiaodong Jia Jay Lee


Electric Vehicles (EVs) have become a trending topic in recent years due to the industry’s race for competitive pricing as well as environmental awareness. These concerns have led to increased research into the development of both affordable and environmentally friendly EV technology. This paper aims to review EV-related issues beginning with the component level, through the system level, based on intelligent maintenance aspects. The paper will also clarify the existing gaps in practical applications and highlight the potential opportunities related to the current issues in EVs for the EV industry moving forward. More specifically, we will briefly start with an overview of the fast-growing EV market, showing the urgent demand for Prognostics and Health Management (PHM) applications in the EV industry. At the component level, the issues of the major components such as the motor, battery, and charging system in EVs are elaborated, and the relevant PHM research of these components is surveyed to show the development in the era of EV expansion. Moreover, the impact of an increasing number of EVs at the system level such as power distribution systems and power grid are explored to uncover possible research in the future.

The combination of existing PHM techniques and robust measurement or feature extraction methods can provide better solutions to address the motor, battery, or transformer issues at the component level. A comprehensive optimization and cybersecurity strategy will help to address the issues of the whole network at a system level. Four aspects of vision in the overall charging network – battery innovation, charging optimization, infrastructure evolution, and sustainability – that cover the demands of research in new battery materials, innovative charging techniques, new architectures of the charging network, and reliable waste treatment mechanisms are outlined. A conclusion is reached in this paper by summarizing the opportunities for future EV research and development.

Abstract 509 | PDF Downloads 259



Electric Vehicles, Prognostics and Health Management

Acharya, S., Dvorkin, Y., Pandzic, H., & Karri, R. (2020). Cybersecurity of Smart Electric Vehicle Charging: A Power Grid Perspective. IEEE Access, 8, 214434–214453.
Adelhelm, P., Hartmann, P., Bender, C. L., Busche, M., Eufinger, C., & Janek, J. (2015). From lithium to sodium: Cell chemistry of room temperature sodium–air and sodium–sulfur batteries. Beilstein Journal of Nanotechnology, 6, 1016–1055.
Aiyanyo, I. D., Samuel, H., & Lim, H. (2020). A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning. Applied Sciences, 10(17), 5811.
Ajenikoko, G. A., & Sangotola, O. (2016). An Overview of Impedance-Based Fault Location Techniques in Electrical Power Transmission Network. 8.
Akhavan-Rezai, E., Shaaban, M. F., El-Saadany, E. F., & Zidan, A. (2012). Uncoordinated charging impacts of electric vehicles on electric distribution grids: Normal and fast charging comparison. 2012 IEEE Power and Energy Society General Meeting, 1–7.
All-Electric Vehicles. (n.d.). Retrieved April 28, 2022, from
Alternative Fuels Data Center: Emissions from Hybrid and Plug-In Electric Vehicles. (n.d.). Retrieved April 28, 2022, from
Álvarez Antón, J. C., García Nieto, P. J., de Cos Juez, F. J., Sánchez Lasheras, F., González Vega, M., & Roqueñí Gutiérrez, M. N. (2013). Battery state-of-charge estimator using the SVM technique. Applied Mathematical Modelling, 37(9), 6244–6253.
Analysis of Faulted Power Systems | IEEE eBooks | IEEE Xplore. (n.d.). Retrieved November 13, 2021, from
Aslan, Y. (2012). An alternative approach to fault location on power distribution feeders with embedded remote-end power generation using artificial neural networks. Electrical Engineering, 94(3), 125–134.
Awalin, L. J., Mokhlis, H., & Halim, A. H. A. (2012). Improved fault location on distribution network based on multiple measurements of voltage sags pattern. 2012 IEEE International Conference on Power and Energy (PECon), 767–772.
Bai, G., Wang, P., Hu, C., & Pecht, M. (2014). A generic model-free approach for lithium-ion battery health management. Applied Energy, 135, 247–260.
Beaudet, A., Larouche, F., Amouzegar, K., Bouchard, P., & Zaghib, K. (2020). Key Challenges and Opportunities for Recycling Electric Vehicle Battery Materials. Sustainability, 12(14), 5837.
Bi, Z., Kan, T., Mi, C. C., Zhang, Y., Zhao, Z., & Keoleian, G. A. (2016). A review of wireless power transfer for electric vehicles: Prospects to enhance sustainable mobility. Applied Energy, 179, 413–425.
Brahma, S. M. (2011). Fault Location in Power Distribution System With Penetration of Distributed Generation. IEEE Transactions on Power Delivery, 26(3), 1545–1553.
Brenna, M., Foiadelli, F., Leone, C., & Longo, M. (2020). Electric Vehicles Charging Technology Review and Optimal Size Estimation. Journal of Electrical Engineering & Technology, 15(6), 2539–2552.
Chen, M., & Rincon-Mora, G. A. (2006). Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion, 21(2), 504–511.
Cui, D., Wang, Z., Zhang, Z., Liu, P., Wang, S., & Dorrell, D. G. (2022). Driving Event Recognition of Battery Electric Taxi Based on Big Data Analysis. IEEE Transactions on Intelligent Transportation Systems, 23(7), 9200–9209.
de Faria, H., Costa, J. G. S., & Olivas, J. L. M. (2015). A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis. Renewable and Sustainable Energy Reviews, 46, 201–209.
Deb, S., Kalita, K., & Mahanta, P. (2017). Review of impact of electric vehicle charging station on the power grid. 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy), 1–6.
Deng, J., Bae, C., Denlinger, A., & Miller, T. (2020). Electric Vehicles Batteries: Requirements and Challenges. Joule, 4(3), 511–515.
Dodds, M. (2020, January 22). AAA Research: Electric Vehicles Cost About the Same as Gas-Powered Vehicles; AAA Oregon/Idaho.
Downey, A., Lui, Y.-H., Hu, C., Laflamme, S., & Hu, S. (2019). Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds. Reliability Engineering & System Safety, 182, 1–12.
Duduta, M., Ho, B., Wood, V. C., Limthongkul, P., Brunini, V. E., Carter, W. C., & Chiang, Y.-M. (2011). Semi-Solid Lithium Rechargeable Flow Battery. Advanced Energy Materials, 1(4), 511–516.
EV Charging Statistics – EVAdoption. (n.d.). Retrieved October 5, 2021, from
Evans, S. C., Mishra, P., Yan, W., & Bouqata, B. (2013). Security Prognostics: Cyber meets PHM. 2013 IEEE Conference on Prognostics and Health Management (PHM), 1–6.
Falk, J., Nedjalkov, A., Angelmahr, M., & Schade, W. (2020). Applying Lithium-Ion Second Life Batteries for Off-Grid Solar Powered System—A Socio-Economic Case Study for Rural Development. Zeitschrift Für Energiewirtschaft, 44(1), 47–60.
Fries, M., Kerler, M., Rohr, S., Schickram, S., & Sinning, M. (n.d.). An Overview of Costs for Vehicle Components, Fuels, Greenhouse Gas Emissions and Total Cost of Ownership Update 2017. 27.
Gao, D., & Lin, X. (2021). Fault Diagnosis Method of DC Charging Points for EVs Based on Deep Belief Network. World Electric Vehicle Journal, 12(1), 47.
Gelman, D., Shvartsev, B., & Ein-Eli, Y. (2014). Aluminum–air battery based on an ionic liquid electrolyte. Journal of Materials Chemistry A, 2(47), 20237–20242.
Ghavami, M., & Singh, C. (2017). Reliability evaluation of electric vehicle charging systems including the impact of repair. 2017 IEEE Industry Applications Society Annual Meeting, 1–9.
Gilleran, M., Bonnema, E., Woods, J., Mishra, P., Doebber, I., Hunter, C., Mitchell, M., & Mann, M. (2021). Impact of electric vehicle charging on the power demand of retail buildings. Advances in Applied Energy, 4, 100062.
Girishkumar, G., McCloskey, B., Luntz, A. C., Swanson, S., & Wilcke, W. (2010). Lithium−Air Battery: Promise and Challenges. The Journal of Physical Chemistry Letters, 1(14), 2193–2203.
Gu, F., Wang, T., Alwodai, A., Tian, X., Shao, Y., & Ball, A. D. (2015). A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals. Mechanical Systems and Signal Processing, 50–51, 400–413.
Gundewar, S. K., & Kane, P. V. (2022). Condition Monitoring and Fault Diagnosis of Induction Motor in Electric Vehicle. In R. Kumar, V. S. Chauhan, M. Talha, & H. Pathak (Eds.), Machines, Mechanism and Robotics (pp. 531–537). Springer.
Gupta, R. B., & Singh, S. K. (2019). Detection of Crack and Unbalancing in a Rotor System Using Artificial Neural Network. In A. Prasad, S. S. Gupta, & R. K. Tyagi (Eds.), Advances in Engineering Design (pp. 607–618). Springer.
Gururajapathy, S. S., Mokhlis, H., & Illias, H. A. (2017). Fault location and detection techniques in power distribution systems with distributed generation: A review. Renewable and Sustainable Energy Reviews, 74, 949–958.
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017). A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations. Renewable and Sustainable Energy Reviews, 78, 834–854.
Harper, G., Sommerville, R., Kendrick, E., Driscoll, L., Slater, P., Stolkin, R., Walton, A., Christensen, P., Heidrich, O., Lambert, S., Abbott, A., Ryder, K., Gaines, L., & Anderson, P. (2019). Recycling lithium-ion batteries from electric vehicles. Nature, 575(7781), 75–86.
He, H., Xiong, R., & Guo, H. (2012). Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles. Applied Energy, 89(1), 413–420.
Hong, S., Hwang, H., Kim, D., Cui, S., & Joe, I. (2021). Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods. Applied Sciences, 11(23), 11285.
How much charging infrastructure do electric vehicles need? A review of the evidence and international comparison | Elsevier Enhanced Reader. (n.d.).
Huangfu, Y., Xu, J., Zhao, D., Liu, Y., & Gao, F. (2018). A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer. Energies, 11(5), 1211.
Kaur, K., Garg, A., Cui, X., Singh, S., & Panigrahi, B. K. (2021). Deep learning networks for capacity estimation for monitoring SOH of Li-ion batteries for electric vehicles. International Journal of Energy Research, 45(2), 3113–3128.
kexugit. (n.d.). Uncover Security Design Flaws Using The STRIDE Approach. Retrieved September 27, 2022, from
Khumprom, P., & Yodo, N. (2019). A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm. Energies, 12(4), 660.
Kim, H., Park, K.-Y., Hong, J., & Kang, K. (2015). All-graphene-battery: Bridging the gap between supercapacitors and lithium ion batteries. Scientific Reports, 4(1), 5278.
Kim, I.-S. (2010). A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer. IEEE Transactions on Power Electronics, 25(4), 1013–1022.
Labrador Rivas, A. E., & Abrão, T. (2020). Faults in smart grid systems: Monitoring, detection and classification. Electric Power Systems Research, 189, 106602.
Li, Y., Zhang, S., Li, H., Zhai, Y., Zhang, W., & Nie, Y. (2012). A fault location method based on genetic algorithm for high-voltage direct current transmission line. European Transactions on Electrical Power, 22(6), 866–878.
Li, Z., Ming, A., Zhang, W., Liu, T., Chu, F., & Li, Y. (2019). Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform. Applied Sciences, 9(9), 1876.
Lopes, F. V., Silva, K. M., Costa, F. B., Neves, W. L. A., & Fernandes, D. (2015). Real-Time Traveling-Wave-Based Fault Location Using Two-Terminal Unsynchronized Data. IEEE Transactions on Power Delivery, 30(3), 1067–1076.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2016). Communication-Efficient Learning of Deep Networks from Decentralized Data.
Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2018). Exploiting Unintended Feature Leakage in Collaborative Learning.
Mori, R. (2020). Recent Developments for Aluminum–Air Batteries. Electrochemical Energy Reviews, 3(2), 344–369.
Murugan, R., & Ramasamy, R. (2019). Understanding the power transformer component failures for health index-based maintenance planning in electric utilities. Engineering Failure Analysis, 96, 274–288.
Nicholas, M. (n.d.). Estimating electric vehicle charginginfrastructure costs across majorU.S. metropolitan areas. 11.
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013). Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. Journal of Power Sources, 239, 680–688.
Panchal, C., Stegen, S., & Lu, J. (2018). Review of static and dynamic wireless electric vehicle charging system. Engineering Science and Technology, an International Journal, 21(5), 922–937.
Pinheiro, A. A., Brandao, I. M., & Costa, C. D. (2019). Vibration Analysis in Turbomachines Using Machine Learning Techniques. European Journal of Engineering and Technology Research, 4(2), 12–16.
Pires, V. F., Foito, D., Martins, J. F., & Pires, A. J. (2015). Detection of stator winding fault in induction motors using a motor square current signature analysis (MSCSA). 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), 507–512.
Pozzato, G., Allam, A., & Onori, S. (2022). Lithium-ion battery aging dataset based on electric vehicle real-driving profiles. Data in Brief, 41, 107995.
Qian, J., Henderson, W. A., Xu, W., Bhattacharya, P., Engelhard, M., Borodin, O., & Zhang, J.-G. (2015). High rate and stable cycling of lithium metal anode. Nature Communications, 6(1), 6362.
Romare, M., & Dahllöf, L. (n.d.). The Life Cycle Energy Consumption and Greenhouse Gas Emissions from Lithium-Ion Batteries. 58.
Salat, R., & Osowski, S. (2004). Accurate fault location in the power transmission line using support vector machine approach. IEEE Transactions on Power Systems, 19(2), 979–986.
Sanghvi, A., & Markel, T. (2021). Cybersecurity for Electric Vehicle Fast-Charging Infrastructure. 2021 IEEE Transportation Electrification Conference & Expo (ITEC), 573–576.
Sanguesa, J. A., Torres-Sanz, V., Garrido, P., Martinez, F. J., & Marquez-Barja, J. M. (2021). A Review on Electric Vehicles: Technologies and Challenges. Smart Cities, 4(1), 372–404.
Sarmah, S. B., Kalita, P., Garg, A., Niu, X., Zhang, X.-W., Peng, X., & Bhattacharjee, D. (2019). A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles. Journal of Electrochemical Energy Conversion and Storage, 16(4), Article 4.
Shafahi, A., Huang, W. R., Najibi, M., Suciu, O., Studer, C., Dumitras, T., & Goldstein, T. (2018). Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks.
Shuvo, S. S., & Yilmaz, Y. (2020). Predictive Maintenance for Increasing EV Charging Load in Distribution Power System. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 1–6.
Steinstraeter, M. (2020). Battery and Heating Data in Real Driving Cycles [Data set]. IEEE.
Sturm, J., Spingler, F. B., Rieger, B., Rheinfeld, A., & Jossen, A. (2017). Non-Destructive Detection of Local Aging in Lithium-Ion Pouch Cells by Multi-Directional Laser Scanning. Journal of The Electrochemical Society, 164(7), A1342–A1351.
Sun, Y., Hu, X., Liu, X., He, X., & Wang, K. (2017). A Software-Defined Green Framework for Hybrid EV-Charging Networks. IEEE Communications Magazine, 55(11), 62–69.
Swetapadma, A., & Yadav, A. (2015). Fuzzy inference system approach for locating series, shunt, and simultaneous series-shunt faults in double circuit transmission lines. Computational Intelligence and Neuroscience, 2015, 79:79.
Talukdar, B. K., & Deka, B. C. (2021). An Approach to Reliability, Availability and Maintainability Analysis of a Plug-In Electric Vehicle. World Electric Vehicle Journal, 12(1), 34.
Tang, Y., Chen, Y., Madawala, U. K., Thrimawithana, D. J., & Ma, H. (2018). A New Controller for Bidirectional Wireless Power Transfer Systems. IEEE Transactions on Power Electronics, 33(10), 9076–9087.
Update on electric vehicle costs in the United States through 2030 | International Council on Clean Transportation. (2021, November 13).
Using PSpice to Simulate the Discharge Behavior of Common Batteries | PSpice. (n.d.). Retrieved April 28, 2022, from
Vazifeh, M. M., Zhang, H., Santi, P., & Ratti, C. (2019). Optimizing the deployment of electric vehicle charging stations using pervasive mobility data. Transportation Research Part A: Policy and Practice, 121, 75–91.
Wang, W., & Mu, J. (2019). State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error. IEEE Access, 7, 29223–29235.
Wei, S.-Y., Zhu, Q., Li, X.-M., & Meng, X.-H. (2021). Research on Comprehensive Evaluation of Electric Vehicle Charging Failures. 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), 1255–1259.
Zhang, L., Shaffer, B., Brown, T., & Scott Samuelsen, G. (2015). The optimization of DC fast charging deployment in California. Applied Energy, 157, 111–122.
Zhirong, Z.-K., & Maximilian, F. (2017). Magnesium-sulfur battery: Its beginning and recent progress. MRS Communications, 7(4), 770–784.
Technical Papers