Wireless Charging for EV/HEV with Prescriptive Analytics, Machine Learning, Cybersecurity and Blockchain Technology: Ongoing and Future Trends
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
Due to the rapid development in the technological aspect of the autonomous vehicle (AV), there is a compelling need for research in the field vehicle efficiency and emission reduction without affecting the performance, safety and reliability of the vehicle. Electric vehicle (EV) with rechargeable battery has been proved to be a practical solution for the above problem. In order to utilize the maximum capacity of the battery, a proper power management and control mechanism need to be developed such that it does not affect the performance, reliability and safety of vehicle. Different optimization techniques along with deterministic dynamic programming (DDP) approach are used for the power distribution and management control. The battery-operated electric vehicle can be recharged either by plug-in a wired connection or by the inductive mean (i.e. wirelessly) with the help of the electromagnetic field energy. These inductive and wireless charging techniques utilize the principle of electromagnetic induction for transferring the power. The design of the wireless charging system, can be divided into three primary stage such as coil design, compensation topology and power converter with the control mechanism for transferring power efficiently. Different coil structures are proposed for maximizing the magnetic flux therefore helping in transferring the energy effectively. Compensation topology is used for the tuning of the high-frequency AC ranging from a few kHz to MHz between the primary coil and secondary coil. Different advance machine learning techniques are evolved for optimization of the parameters such as state of charge (SoC) and state of health (SoH), temperature, current etc. Based on the data obtained by pre-processing through data analysis techniques and then applying ML technique and prescriptive analytics are applied to estimate the value. In order to provide the secure charging environment, blockchain technology framework is proposed along with appropriate cyber security algorithm where ever required.
CitationMishra, V., Kodakkadan, A., Koduri, R., Nandyala, S. et al., "Wireless Charging for EV/HEV with Prescriptive Analytics, Machine Learning, Cybersecurity and Blockchain Technology: Ongoing and Future Trends," SAE Technical Paper 2019-01-0790, 2019, https://doi.org/10.4271/2019-01-0790.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
- Nguyen, T.T. and Jossen, A., “Analysing the Driving Load on Electric Vehicles Using Unsupervised Segmentation Model as Enabler to Determine the Time of Battery Replacement and Assess Driving Mileage,” in International Conference on Ecological Vehicles and Renewable Energies, Vol. 34, No. 10-12, April 2018.
- Zhang, R., Xia, B., and Li, B., “State of the Art of Lithium Ion Battery SOC Estimation for Electrical Vehicles,” Energies 11(7), 2018.
- Wipke, K., Markel, T., and Nelson, D., “Optimizing Energy Management Strategy and Degree of Hybridization for a Hydrogen Fuel Cell SUV,” in Proc. 18th Int. EVS, Berlin, Germany, 2001.
- Zhu, Y., Chen, Y., and Chen, Q., “Analysis and Design of an Optimal Energy Management and Control System for Hybrid Electric Vehicles,” in Proc 9th Elect. Veh. Symp., Busan, Korea, 2002.
- Lin, C., Peng, H., and Grizzle, J., “A Stochastic Control Strategy for Hybrid Electric Vehicles,” in Proc. Amer. Control Conf., Boston, MA, 2004.
- Murphey, Y.L., Park, J., Chen, Z., Kuang, M.L., and Abul Masrur, M., “Intelligent Hybrid Vehicle Power Control-Part I: Machine Learning of Optimal Vehicle Power,” IEEE Trans. on Vehicular Technology 61(8), 2012.
- Musavi, F. and Eberle, W., “Wireless Power Transfer: A Survey of EV Battery Charging Technologies, in IEEE Energy Conversion Congress and Exposition (ECCE), Raleigh, NC, 2012, 1804-1810.
- Kan, T., Lu, F., Nguyen, T.D., Mercier, P.P., and Mi, C., “Integrated Coil Design for EV Wireless Charging Systems Using LCC Compensation Topology,” IEEE Trans. Power Electron., 2018.
- Lu, F., Zhang, H., Kan, T., Hofmann, H., Mei, Y., Cai, L., and Mi, C., “A High Efficiency and Compact Inductive Power Transfer System Compatible with Both 3.3kW and 7.7kW Receivers,” in Proc. Applied Power Electronics Conference (APEC), Mar. 2017.
- Li, W., Zhao, H., Kan, T., and Mi, C., “Inter-Operability Considerations of the Double-Sided LCC Compensated Wireless Charger for Electric Vehicle and Plug-In Hybrid Electric Vehicle Applications, “in Proc. IEEE PELS Workshop Emerging Technology: Wireless Power, 2015, 1-6.
- Hannan, M.A., Lipu, M.S.H., Hussain, A., and Mohamed, A., “A Review of Lithium-Ion Battery State of Charge Estimation and Management System in Electric Vehicle Applications: Challenges and Recommendations,” Renew. Sustain. Energy Rev. 78:834-854, 2017.
- Dai, H., Xu, T., Zhu, L., Wei, X., and Sun, Z., “Adaptive Model Parameter Identification for Large Capacity Li-Ion Batteries on Separated Time Scales,” Appl. Energy 184:119-131, 2016.
- Zheng, Y., Ouyang, M., Han, X., Lu, L., and Li, J., “Investigating the Error Sources of the Online State of Charge Estimation Methods for Lithium-Ion Batteries in Electric Vehicles,” J. Power Sources 377:161-188, 2018.
- Plett, G.L., “Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs: Part 1. Background,” J. Power Sources 134:252-261, 2004.
- Hu, X., Li, S., Peng, H., and Sun, F., “Robustness Analysis of State-of-Charge Estimation Methods for Two Types of Li-Ion Batteries,” J. Power Sources 217:209-219, 2012.
- Zhang, C., Li, K., Pei, L., and Zhu, C., “An Integrated Approach for Real-Time Model-Based State-of-Charge Estimation of Lithium-Ion Batteries,” J. Power Sources 283:24-36, 2015.
- Wei, J., Dong, G., and Chen, Z., “On-Board Adaptive Model for State of Charge Estimation of Lithium-Ion Batteries Based on Kalman Filter with Proportional Integral-Based Error Adjustment,” J. Power Sources 365:308-319, 2017.
- Hu, C., Youn, B.D., and Chung, J., “A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation,” Appl. Energy 92:694-704, 2012.
- Xiong, B., Zhao, J., Wei, Z., and Skyllas-Kazacos, M., “Extended Kalman Filter Method for State of Charge Estimation of Vanadium Redox Flow Battery Using Thermal-Dependent Electrical Model,” J. Power Sources 262:50-61, 2014.
- Yang, F., Xing, Y., Wang, D., and Tsui, K.-L., “A Comparative Study of Three Model-Based Algorithms for Estimating State-of-Charge of Lithium-Ion Batteries under a New Combined Dynamic Loading Profile,” Appl. Energy 164:387-399, 2016.
- Di Domenico, D., Prada, E., and Creff, Y., “An Adaptive Strategy for Li-Ion Battery Internal State Estimation,” Control Eng. Pract. 21:1851-1859, 2013.
- Wei, Z., Tseng, K.J., Wai, N., Lim, T.M., and Skyllas-Kazacos, M., “Adaptive Estimation of State of Charge and Capacity with Online Identified Battery Model for Vanadium Redox Flow Battery,” J. Power Sources 332:389-398, 2016.
- Xiong, R., Gong, X., Mi, C.C., and Sun, F., “A Robust State-of-Charge Estimator for Multiple Types of Lithium-Ion Batteries Using Adaptive Extended Kalman Filter,” J. Power Sources 243:805-816, 2013.
- Xiong, R., He, H., Sun, F., and Zhao, K., “Evaluation on State of Charge Estimation of Batteries with Adaptive Extended Kalman Filter by Experiment Approach,” IEEE Trans. Veh. Technol. 62:108-117, 2013.
- Lim, K., Bastawrous, H.A., Duong, V.-H., See, K.W. et al., “Fading Kalman Filter-Based Real-Time State of Charge Estimation in LiFePO4 Battery-Powered Electric Vehicles,” Appl. Energy 169:40-48, 2016.
- Zou, C., Manzie, C., Nešic, D., and Kallapur, A.G., “Multi-Time-Scale Observer Design for State-of-Charge and State-of-Health of a Lithium-Ion Battery,” J. Power Sources 335:121-130, 2016.
- Zheng, F., Xing, Y., Jiang, J., Sun, B. et al., “Influence of Different Open Circuit Voltage Tests on State of Charge Online Estimation for Lithium-Ion Batteries,” Appl. Energy 183:513-525, 2016.
- Plett, G.L., “Sigma-Point Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs: Part 1: Introduction and State Estimation,” J. Power Sources 161:1356-1368, 2006.
- Plett, G.L., “Sigma-Point Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs: Part 2: Simultaneous State and Parameter Estimation,” J. Power Sources 161:1369-1384, 2006.
- He, Z., Liu, Y., Gao, M., and Wang, C., “A Joint Model and SOC Estimation Method for Lithium Battery Based on the Sigma Point KF,” in Proceedings of the Transportation Electrification Conference and Expo, Dearborn, MI, June 18-20, 2012, 1-5.
- Xia, B., Sun, Z., Zhang, R., Lao, Z. et al., “A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model,” Energies 10:457, 2017.
- Ye, M., Guo, H., Xiong, R., and Yu, Q., “A Double-Scale and Adaptive Particle Filter-Based Online Parameter and State of Charge Estimation Method for Lithium-Ion Batteries,” Energy 144:789-799, 2018.
- Charkhgard, M. and Zarif, M.H., “Design of Adaptive H∞ Filter for Implementing on State-of-Charge Estimation Based on Battery State-of-Charge-Varying Modelling,” Power Electron. IET 8:1825-1833, 2015.
- Xia, B., Cui, D., Sun, Z., Lao, Z. et al., “State of Charge Estimation of Lithium-Ion Batteries Using Optimized Levenberg-Marquardt Wavelet Neural Network,” Energy 153:694-705, 2018.
- Dang, X., Yan, L., Xu, K., Wu, X. et al., “Open-Circuit Voltage-Based State of Charge Estimation of Lithium-Ion Battery Using Dual Neural Network Fusion Battery Model,” Electrochim. Acta 188:356-366, 2016.
- Tong, S., Lacap, J.H., and Park, J.W., “Battery State of Charge Estimation Using a Load-Classifying Neural Network,” J. Energy Storage 7:236-243, 2016.
- Chaoui, H., Ibe-Ekeocha, C.C., and Gualous, H., “Aging Prediction and State of Charge Estimation of a LiFePO4 Battery Using Input Time-Delayed Neural Networks,” Electr. Power Syst. Res. 146:189-197, 2017.
- Hu, J.N., Hu, J.J., Lin, H.B., Li, X.P. et al., “State-of-Charge Estimation for Battery Management System Using Optimized Support Vector Machine for Regression,” J. Power Sources 269:682-693, 2014.
- Sheng, H. and Xiao, J., “Electric Vehicle State of Charge Estimation: Nonlinear Correlation and Fuzzy Support Vector Machine,” J. Power Sources 281:131-137, 2015.
- Blaifi, S., Moulahoum, S., Colak, I., and Merrouche, W., “An Enhanced Dynamic Model of Battery Using Genetic Algorithm Suitable for Photovoltaic Applications,” Appl. Energy 169:888-898, 2016.
- Xu, J., Cao, B., Chen, Z., and Zou, Z., “An Online State of Charge Estimation Method with Reduced Prior Battery Testing Information,” Int. J. Electr. Power Energy Syst. 63:178-184, 2014.
- Chen, Z., Mi, C.C., Fu, Y., Xu, J., and Gong, X., “Online Battery State of Health Estimation Based on Genetic Algorithm for Electric and Hybrid Vehicle Applications,” J. Power Sources 240:184-192, 2013.
- Gao, Z., Cheng, S.C., Woo, W.L., Jia, J., and Wei, D.T., “Genetic Algorithm Based Back-Propagation Neural Network Approach for Fault Diagnosis in Lithium-Ion Battery System,” in Proceedings of the International Conference on Power Electronics Systems and Applications, Hong Kong, China, December 15-17, 2015, 1-6.
- Awadallah, M.A. and Venkatesh, B., “Accuracy Improvement of SOC Estimation in Lithium-Ion Batteries,” J. Energy Storage 6:95-104, 2016.
- Cai, C.H., Du, D., and Liu, Z.Y., “Battery State-of-Charge (SOC) Estimation Using Adaptive Neuro-Fuzzy Inference System (ANFIS),” in Proceedings of the IEEE International Conference on Fuzzy Systems, St. Louis, MO, May 25-28, 2003, Vol. 1062, 1068-1073.
- Affanni, A., Bellini, A., Concari, C., and Franceschini, G., “EV Battery State of Charge: Neural Network Based Estimation,” in Proceedings of the IEEE International Electric Machines and Drives Conference, IEMDC’03, Madison, WI, June 1-4, 2003, Vol. 682, 684-688.
- Zhou, F., Wang, L., Lin, H., and Lv, Z., “High Accuracy State-of-Charge Online Estimation of EV/HEV Lithium Batteries Based on Adaptive Wavelet Neural Network,” in Proceedings of the Ecce Asia Downunder, Melbourne, VIC, Australia, June 3-6, 2013, 513-517.
- Dai, H., Guo, P., Wei, X., Sun, Z., and Wang, J., “ANFIS (Adaptive Neuro-Fuzzy Inference System) Based Online SOC (State of Charge) Correction Considering Cell Divergence for the EV (Electric Vehicle) Traction Batteries,” Energy 80:350-360, 2015.
- Tian, Y., Li, D., Tian, J., and Xia, B., “State of Charge Estimation of Lithium-Ion Batteries Using an Optimal Adaptive Gain Nonlinear Observer,” Electrochim. Acta 225:225-234, 2017.
- Tang, X., Wang, Y., and Chen, Z., “A Method for State-of-Charge Estimation of LiFePO4 Batteries Based on a Dual-Circuit State Observer,” J. Power Sources 296:23-29, 2015.
- Xia, B., Chen, C., Tian, Y., Sun, W. et al., “A Novel Method for State of Charge Estimation of Lithium-Ion Batteries Using a Nonlinear Observer,” J. Power Sources 270:359-366, 2014.
- Ma, Y., Li, B., Li, G., Zhang, J., and Chen, H., “A Nonlinear Observer Approach of SOC Estimation Based on Hysteresis Model for Lithium-Ion Battery,” IEEE/CAA J. Autom. Sin. 4:195-204, 2017.
- Xu, J., Mi, C.C., Cao, B., and Deng, J., “The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer,” IEEE Trans. Veh. Technol. 63:1614-1621, 2014.
- Ning, B., Xu, J., Cao, B., Wang, B., and Xu, G., “A Sliding Mode Observer SOC Estimation Method Based on Parameter Adaptive Battery Model,” Energy Procedia 88:619-626, 2016.
- Zhong, Q., Zhong, F., Cheng, J., Li, H., and Zhong, S., “State of Charge Estimation of Lithium-Ion Batteries Using Fractional Order Sliding Mode Observer,” ISA Trans. 66:448-459, 2017.
- Kim, I.S., “The Novel State of Charge Estimation Method for Lithium Battery Using Sliding Mode Observer,” J. Power Sources 163:584-590, 2006.
- Ma, Y., Li, B., Xie, Y., and Chen, H., “Estimating the State of Charge of Lithium-Ion Battery Based on Sliding Mode Observer,” IFAC Papers On Line 49:54-61, 2016.
- Zhang, F., Liu, G., and Fang, L. “A Battery State of Charge Estimation Method Using Sliding Mode Observer,” in Proceedings of the 2008 World Congress on Intelligent Control and Automation, Chongqing, China, June 25-27, 2008, 989-994.
- Du, J., Liu, Z., Wang, Y., and Wen, C., “An Adaptive Sliding Mode Observer for Lithium-Ion Battery State of Charge and State of Health Estimation in Electric Vehicles,” Control Eng. Pract. 54:81-90, 2016.
- Kim, I.S., “Nonlinear State of Charge Estimator for Hybrid Electric Vehicle Battery,” IEEE Trans. Power Electron. 23:2027-2034, 2008.
- Xia, B., Zheng, W., Zhang, R., Lao, Z. et al., “A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model,” Energies 10:1150, 2017.
- Wang, Q., Wang, J., Zhao, P., Kang, J. et al., “Correlation between the Model Accuracy and Model-Based SOC Estimation,” Electrochim. Acta 228:146-159, 2017.
- Meng, J., Luo, G., and Gao, F., “Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine,” IEEE Trans. Power Electron. 31:2226-2238, 2016.
- Sripad, S., Kulandaivel, S., Pande, V., Sekar, V., and Viswanathan, V., “Vulnerabilities of Electric Vehicles Battery Packs to Cyberattack,” PNAS Conference, arxu=iv:1711.04822v2, July 2018.
- Anthony, B.L., Korosh, V.P., Atul, P.D., and Shuo, Y., “Security Perspective on Battery Systems of the IOT,” Journal of Hardware and Systems Security 1:188-199, 2017.
- Chio, C. and Freeman, D., Machine Learning and Security-Protecting Systems with Data and Algorithm (Willey O’Reilly, 2018).
- Sonalker, A. and Sherman, D., “Temporal Anomaly Detection on Automotive Networks,” U.S. Patent application 14/857,098, filled Sept. 17, 2015.
- Harris, S.A. and Mayhew, K., “Anomaly Detection for Vehicular Network for Intrusion and Malfunction Detection,” U.S. Patent 14/857016, filled Sept. 17, 2015.
- Lim, K.L.A., “Method and System for Anomaly Detection Using Collective Set of the Unsupervised ML Algorithms,” U.S. Patent application 11/449533, filled Jun. 8, 2006.
- Puthal, D., Malik, N., Mohanty, S.P., Kougianos, E., and Yang, C., “The Blockchain as a Decentralized Security Framework [Future Directions],” IEEE Consumer Electronics Magazine 7(2):18-21, 2018.
- Rutkin, A., “Blockchain- +Based Microgrid Gives Power to Consumers in New York,” New Scientist, Daily News, 2016. Available at: https://www.newscientist.com/article/2079334-blockchain-based-microgrid-gives-power-to-consumers-in-new-york.
- Gogerty, N., “What is SolarCoin?,” Singularity Weblog, 2015. Available at: https://www.singularityweblog.com/open-source-software-and-the-solarcoin-foundation/.
- Coleman, L., “How the Energy Blockchain Will Create a Distributed Grid,” Cryptocoinsnews, 2016. [Online] Available at: https://www.cryptocoinsnews.com/energy-blockchain-will-create-distributed-grid/.
- Luo, J., “Investigate Centralized and Decentralized Information Infrastructure for Future Electricity Market,” University of Michigan-Dearborn, 2017.
- Rim, C.T., “The Development and Deployment of On-Line Electric Vehicles (OLEV),” in IEEE Energy Conversion Congress and Exposition (ECCE), 2013.
- Choi, S.Y. and Gu, B.W., “Advances in Wireless Power Transfer Systems for Roadway-Powered Electric Vehicles,” in IEEE VTC Workshop on Emerging Technologies: Wireless Power, 2014.
- Stielau, O.H. and Covic, G.A., n.d., “Design of Loosely Coupled Inductive Power Transfer Systems,” in PowerCon 2000. 2000 International Conference on Power System Technology, 2000.
- Musavi, F., Edington, M., and Eberle, W., “Wireless Power Transfer: A Survey of EV Battery Charging Technologies,” in IEEE Energy Conversion Congress and Exposition (ECCE), 2012.
- Qi, X., Wu, G., Boriboonsomsin, K., and Barth, M.J., “Development and Evaluation of an Evolutionary Algorithm-Based OnLine Energy Management System for Plug-In Hybrid Electric Vehicles,” IEEE Transactions on Intelligent Transportation Systems, 2016.
- Zhang, Z., Chau, K.T., Qiu, C., and Liu, C., “Energy Encryption for Wireless Power Transfer,” IEEE Transactions on Power Electronics, 2014.