Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time

References

• Adornato, B., R. Patil, Z. Filipi, Z. Baraket, and T. Gordon. “Characterizing Naturalistic Driving Patterns for Plug-in Hybrid Electric Vehicle Analysis.” 2009 IEEE Vehicle Power and Propulsion Conference Michigan, USA, 2009, p. 655–660. https://doi.org/10.1109/VPPC.2009.5289786.
   Google Scholar
• Ali Aden W, Zheng J, Ullah I and Safdar M. 2022.“Public Preferences Towards Car Sharing Service: The Case of Djibouti“ Front. Environ. Sci., 10: 889453. doi:10.3389/fenvs.2022.889453
   Web of Science ®Google Scholar
• An, K., K.B. Song, and K. Hur. 2017. “Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration.” Energies 10 (5) : 729. doi:10.3390/en10050729.
   Web of Science ®Google Scholar
• Ashtari, A., E. Bibeau, S. Shahidinejad, and T. Molinski. 2011. “PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data.” IEEE Transactions on Smart Grid 3: 341–350. doi:10.1109/TSG.2011.2162009.
   Web of Science ®Google Scholar
• Azadfar, E., V. Sreeram, and D. Harries. 2015. “The Investigation of the Major Factors Influencing plug-in Electric Vehicle Driving Patterns and Charging Behaviour.” Renewable and Sustainable Energy Reviews 42: 1065–1076. doi:10.1016/j.rser.2014.10.058.
   Web of Science ®Google Scholar
• Bergstra, J., and Y. Bengio. 2012. “Random Search for hyper-parameter Optimization.” Journal of Machine Learning Research 13: 281–305.
   Web of Science ®Google Scholar
• Bergstra, J., D. Yamins, and D. D. Cox. “Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms.” Proceedings of the 12th Python in science conference Austin, Texas, USA, vol. 13, Citeseer; 2013, p. 20.
   Google Scholar
• Bi, J., Y. Wang, S. Sun, and W. Guan. 2018. “Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing.” Energies 11: 1040. doi:10.3390/en11051040.
   Web of Science ®Google Scholar
• Bian, X.-Q., Q. Zhang, L. Zhang, and J. Chen. 2017. “A Grey Wolf optimizer-based Support Vector Machine for the Solubility of Aromatic Compounds in Supercritical Carbon Dioxide.” Chemical Engineering Research and Design 123: 284–294. doi:10.1016/j.cherd.2017.05.008.
   Web of Science ®Google Scholar
• Bunce, L., M. Harris, and M. Burgess. 2014. “Charge up Then Charge Out? Drivers’ Perceptions and Experiences of Electric Vehicles in the UK.” Transportation Research Part A: Policy and Practice 59: 278–287. doi:10.1016/j.tra.2013.12.001.
   Web of Science ®Google Scholar
• Chen, R., X. Qian, L. Miao, and S. V. Ukkusuri. 2020. “Optimal Charging Facility Location and Capacity for Electric Vehicles considering Route Choice and Charging Time Equilibrium.” Computers & Operations Research 113: 104776. doi:10.1016/j.cor.2019.104776.
   Web of Science ®Google Scholar
• Clement-Nyns, K., E. Haesen, and J. Driesen. 2010. “The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid.” IEEE Transactions on Power Systems 25: 371–380. doi:10.1109/TPWRS.2009.2036481.
   Web of Science ®Google Scholar
• Cui, X., H. K. Kim, C. Liu, S.-C. Kao, and B. L. Bhaduri. 2012. “Simulating the Household plug-in Hybrid Electric Vehicle Distribution and Its Electric Distribution Network Impacts.” Transportation Research Part D: Transport and Environment 17: 548–554. doi:10.1016/j.trd.2012.05.011.
   Web of Science ®Google Scholar
• Duell, M., L. M. Gardner, and S. T. Waller. 2018. “Policy Implications of Incorporating Distance Constrained Electric Vehicles into the Traffic Network Design Problem.” Transportation Letters 10: 144–158. doi:10.1080/19427867.2016.1239306.
   Web of Science ®Google Scholar
• Franke, T., and J. F. Krems. 2013. “Understanding Charging Behaviour of Electric Vehicle Users.” Transportation Research Part F: Traffic Psychology and Behaviour 21: 75–89. doi:10.1016/j.trf.2013.09.002.
   Web of Science ®Google Scholar
• Giménez-Gaydou, D. A., A. S. Ribeiro, J. Gutiérrez, and A. P. Antunes. 2016. “Optimal Location of Battery Electric Vehicle Charging Stations in Urban Areas: A New Approach.” International Journal of Sustainable Transportation 10: 393–405. doi:10.1080/15568318.2014.961620.
   Web of Science ®Google Scholar
• Gnecco, G., Y. Hadas, and M. Sanguineti. 2021. “Public Transport Transfers Assessment via Transferable Utility Games and Shapley Value Approximation.” Transportmetrica A: Transport Science 17: 540–565. doi:10.1080/23249935.2020.1799112.
   Web of Science ®Google Scholar
• Habib, S., M. Kamran, and U. Rashid. 2015. “Impact Analysis of vehicle-to-grid Technology and Charging Strategies of Electric Vehicles on Distribution Networks – A Review.” Journal of Power Sources 277: 205–214. doi:10.1016/j.jpowsour.2014.12.020.
   Web of Science ®Google Scholar
• Halbey, J., R. Philipsen, T. Schmidt, and M. Ziefle. “Range Makes All the Difference? Weighing up Range, Charging Time and fast-charging Network Density as Key Drivers for the Acceptance of Battery Electric Vehicles.” International Conference on Applied Human Factors and Ergonomics, Los Angeles, California , USA, Springer; 2017, p. 939–950.
   Google Scholar
• He H-W, Xiong R, Chang Y-H. Dynamic Modeling and. 2010. “Simulation on a Hybrid Power System for Electric Vehicle Applications.” Energies 3: 1821–1830. doi:10.3390/en3111821.
   Web of Science ®Google Scholar
• He, Z., Y. Zhou, X. Chen, J. Wang, W. Shen, M. Wang, et al. 2021. “Examining the Spatial Mode in the Early Market for Electric Vehicles Adoption: Evidence from 41 Cities in China.” Transportation Letters 14 (6) : 1–11. doi:10.1080/19427867.2021.1917217.
   Web of Science ®Google Scholar
• Hmidi, M. E., I. Ben Salem, and L. El Amraoui. 2020. “An Efficient Method for Energy Management Optimization Control: Minimizing Fuel Consumption for Hybrid Vehicle Applications.” Transactions of the Institute of Measurement and Control 42: 69–80. doi:10.1177/0142331219874152.
   Web of Science ®Google Scholar
• Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew. 2006. “Extreme Learning Machine: Theory and Applications.” Neurocomputing 70: 489–501. doi:10.1016/j.neucom.2005.12.126.
   Web of Science ®Google Scholar
• Jamal, A., H. M. Al-Ahmadi, F. M. Butt, M. Iqbal, M. Almoshaogeh, and S. Ali. 2021a. “Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects.“ In : Harkut, D. G., editor. Search Algorithm - Essence of Optimization (London: IntechOpen) 99395. https://www.intechopen.com/online-first/78022 doi: 10.5772/intechopen.99395 .
   Google Scholar
• Jamal, A., and W. Umer. 2020. “Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network.” International Journal of Environmental Research and Public Health 17: 7466. doi:10.3390/ijerph17207466.
   PubMed Web of Science ®Google Scholar
• Jamal, A., M. Zahid, M. Tauhidur Rahman, H. M. Al-Ahmadi, M. Almoshaogeh, D. Farooq, et al. 2021b. “Injury Severity Prediction of Traffic Crashes with Ensemble Machine Learning Techniques: A Comparative Study.”International Journal of Injury Control and Safety Promotion, 28 (4) : 408–427. 10.1080/17457300.2021.1928233.
   PubMed Web of Science ®Google Scholar
• Lee, C. 2021. “An Exact Algorithm for the electric-vehicle Routing Problem with Nonlinear Charging Time.” Journal of the Operational Research Society, 72 (7) : 1461–1485 doi:10.1080/01605682.2020.1730250.
   Web of Science ®Google Scholar
• Li, Q., H. Chen, H. Huang, X. Zhao, Z. Cai, C. Tong, Liu, W., Tian, X. 2017. “An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.” Computational and Mathematical Methods in Medicine, 2017:9512741. doi:10.1155/2017/9512741.
   PubMed Web of Science ®Google Scholar
• Liao, F., E. Molin, and B. van Wee. 2017. “Consumer Preferences for Electric Vehicles: A Literature Review.” Transport Reviews 37: 252–275. doi:10.1080/01441647.2016.1230794.
   Web of Science ®Google Scholar
• Linardatos, P., V. Papastefanopoulos, and S. Kotsiantis. 2021. “Explainable AI: A Review of Machine Learning Interpretability Methods.” Entropy 23: 18. doi:10.3390/e23010018.
   Web of Science ®Google Scholar
• Liu K, Gao H, Liang Z, Zhao M and Li C. 2021.“Optimal charging strategy for large-scale electric buses considering resource constraints“ Transportation Research Part D: Transport and Environment, 99 :103009. doi:10.1016/j.trd.2021.103009
   Web of Science ®Google Scholar
• Liu K, Gao H, Wang Y, Feng T and Li C. 2022.“Robust charging strategies for electric bus fleets under energy consumption uncertainty“ Transportation Research Part D: Transport and Environment, 104 :103215. doi:10.1016/j.trd.2022.103215
   Web of Science ®Google Scholar
• Liu K, Wang J, Yamamoto T and Morikawa T. 2018.“Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption.“Applied Energy, 227: 324–331. doi:10.1016/j.apenergy.2017.08.074
   Web of Science ®Google Scholar
• Lundberg, S.M., and S.I. Lee. “A Unified Approach to Interpreting Model Predictions.” Proceedings of the 31st international conference on neural information processing systems, Long Beach, California, USA, 2017, p. 4768–4777. https://dl.acm.org/doi/10.5555/3295222.3295230.
   Google Scholar
• Mirjalili, S., S. M. Mirjalili, and A. Lewis. 2014. “Grey Wolf Optimizer.” Advances in Engineering Software 69: 46–61. doi:10.1016/j.advengsoft.2013.12.007.
   Web of Science ®Google Scholar
• Moriasi, D. N., M. W. Gitau, N. Pai, and P. Daggupati. 2015. “Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria.” Transactions of the ASABE 58 (6) : 1763–1785 doi:10.13031/trans.58.10715.
   Web of Science ®Google Scholar
• Moriasi, D. N., B. N. Wilson, K. R. Douglas-Mankin, J. G. Arnold, and P. H. Gowda. 2012. “Hydrologic and Water Quality Models: Use, Calibration, and Validation.” Transactions of the ASABE 55: 1241–1247. doi:10.13031/2013.42265.
   Web of Science ®Google Scholar
• Muratori, M. 2018. “Impact of Uncoordinated plug-in Electric Vehicle Charging on Residential Power Demand.” Nature Energy 3: 193–201. doi:10.1038/s41560-017-0074-z.
   Web of Science ®Google Scholar
• Murdoch, W. J., C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu. “Definitions, Methods, and Applications in Interpretable Machine Learning.” Proceedings of the National Academy of Sciences 2019;116 (44) :22071–22080. doi:10.1073/pnas.1900654116.
   PubMed Web of Science ®Google Scholar
• Nimma, K. S., M. D. Al-Falahi, H. D. Nguyen, S. D. G. Jayasinghe, T. S. Mahmoud, and M. Negnevitsky. 2018. “Grey Wolf optimization-based Optimum energy-management and battery-sizing Method for grid-connected Microgrids.” Energies 11: 847. doi:10.3390/en11040847.
   Web of Science ®Google Scholar
• Ou, S., X. Hao, Z. Lin, H. Wang, J. Bouchard, X. He, Przesmitzki, S., Wu, Z., Zheng, J., Lv, R., Qi, L., LaClair, T. J. 2019. “Light-duty plug-in Electric Vehicles in China: An Overview on the Market and Its Comparisons to the United States”. Renewable and Sustainable Energy Reviews 112: 747–761. 10.1016/j.rser.2019.06.021.
   Web of Science ®Google Scholar
• Pan, L., E. Yao, and M. D. Modeling. 2019. “EV Charging Choice considering Risk Attitudes and Attribute non-attendance.” Transportation Research Part C: Emerging Technologies 102: 60–72. doi:10.1016/j.trc.2019.03.007.
   Web of Science ®Google Scholar
• Panwar, L. K., S. Reddy, A. Verma, B. K. Panigrahi, and R. Kumar. 2018. “Binary Grey Wolf Optimizer for Large Scale Unit Commitment Problem.” Swarm and Evolutionary Computation 38: 251–266. doi:10.1016/j.swevo.2017.08.002.
   Web of Science ®Google Scholar
• Pearre, N. S., W. Kempton, R. L. Guensler, and V. V. Elango. 2011. “Electric Vehicles: How Much Range Is Required for a Day’s Driving?” Transportation Research Part C: Emerging Technologies 19: 1171–1184. doi:10.1016/j.trc.2010.12.010.
   Web of Science ®Google Scholar
• Raji, I. D., H. Bello-Salau, I. J. Umoh, A. J. Onumanyi, M. A. Adegboye, and A. T. Salawudeen. 2022. “Simple Deterministic Selection-Based Genetic Algorithm for Hyperparameter Tuning of Machine Learning Models.” Applied Sciences 12: 1186. doi:10.3390/app12031186.
   Google Scholar
• Rao, C. R. and Mitra, S. 1972. Generalized Inverse of a Matrix and Its Applications, . New York: Wiley.
   Google Scholar
• Ritter, A., and R. Munoz-Carpena. 2013. “Performance Evaluation of Hydrological Models: Statistical Significance for Reducing Subjectivity in goodness-of-fit Assessments.” Journal of Hydrology 480: 33–45. doi:10.1016/j.jhydrol.2012.12.004.
   Web of Science ®Google Scholar
• Sanjay, R., T. Jayabarathi, T. Raghunathan, V. Ramesh, and N. Mithulananthan. 2017. “Optimal Allocation of Distributed Generation Using Hybrid Grey Wolf Optimizer.” IEEE Access 5: 14807–14818. doi:10.1109/ACCESS.2017.2726586.
   Web of Science ®Google Scholar
• Sarkar, S., S. Vinay, R. Raj, J. Maiti, and P. Mitra. 2019. “Application of Optimized Machine Learning Techniques for Prediction of Occupational Accidents.” Computers & Operations Research 106: 210–224. doi:10.1016/j.cor.2018.02.021.
   Web of Science ®Google Scholar
• Sazlı, M. H. “A Brief Review of feed-forward Neural Networks.” 2006.
   Google Scholar
• Senthilkumar, M. 2010. Use of Artificial Neural Networks (Anns) in Colour Measurement. Colour Measurement, 125–146. Woodhead Publishing Series in Textiles; Woodhead Publishing: Sawston, UK,: Elsevier.
   Google Scholar
• Shaarbaf, M. R. and M. Ghayeni. “Identification of the Best Charging Time of Electric Vehicles in Fast Charging Stations Connected to Smart Grid Based on Q-learning.” 2018 Electrical Power Distribution Conference (EPDC) Iran 2018 , IEEE; 2018, p. 78–83 doi:10.1109/EPDC.2018.8536301.
   Google Scholar
• Shabbar, R., A. Kasasbeh, and M. M. Ahmed. 2021. “Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization.” Sustainability 13: 3314. doi:10.3390/su13063314.
   Web of Science ®Google Scholar
• Shafiullah, M., and M. A. Abido. 2018. “S-transform Based FFNN Approach for Distribution Grids Fault Detection and Classification.” IEEE Access 6: 8080–8088. doi:10.1109/ACCESS.2018.2809045.
   Web of Science ®Google Scholar
• Shafiullah, M., M. A. Abido, and T. Abdel-Fattah. 2018. “Distribution Grids Fault Location Employing ST Based Optimized Machine Learning Approach.” Energies 11: 2328. doi:10.3390/en11092328.
   Web of Science ®Google Scholar
• Shafiullah, M., M. A. Abido, and Z. Al-Hamouz. 2017. “Wavelet-based Extreme Learning Machine for Distribution Grid Fault Location.” IET Generation, Transmission & Distribution 11: 4256–4263. doi:10.1049/iet-gtd.2017.0656.
   Web of Science ®Google Scholar
• Shafiullah, M., M. A. Abido, and A. H. Al-Mohammed. 2022a. “Metaheuristic Optimization Techniques.” In Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach, edited by M. Shafiullah, M. A. Abido, and A. H. Al-Mohammed, 27–68. Elsevier. doi:10.1016/B978-0-323-88429-7.00008-4.
   Google Scholar
• Shafiullah, M., M. A. Abido, and A. H. Al-Mohammed. 2022b. “Improved Optimal Phasor Measurement Unit Placement Formulation for Power System Observability.” In Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach, edited by M. Shafiullah, M. A. Abido, and A. H. Al-Mohammed, 121–142. Elsevier. doi:10.1016/B978-0-323-88429-7.00004-7.
   Google Scholar
• Shafiullah, M., M. A. Abido, and A. H. Al-Mohammed. 2022c. “Intelligent Fault Diagnosis Technique for Distribution Grid.” In Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach, edited by M. Shafiullah, M. A. Abido, and A. H. Al-Mohammed, 249–292. Elsevier. doi:10.1016/B978-0-323-88429-7.00005-9.
   Google Scholar
• Shafiullah, M., and A. T. Al-Awami. “Maximizing the Profit of a Load Aggregator by Optimal Scheduling of Day Ahead Load with EVs.” 2015 IEEE International Conference on Industrial Technology (ICIT) Beijing, China, IEEE; 2015, p. 1342–1347.
   Google Scholar
• Shi, J., M. Tian, S. Han, T. Y. Wu and Tang, Y. 2022.“ Electric Vehicle Battery Remaining Charging Time Estimation considering Charging Accuracy and Charging Profile Prediction.“ Journal of Energy Storage 49:104132 doi:10.1016/j.est.2022.104132.
   Web of Science ®Google Scholar
• Simolin, T., K. Rauma, R. Viri, J. Mäkinen, A. Rautiainen, and P. Järventausta. 2021. “Charging Powers of the Electric Vehicle Fleet: Evolution and Implications at Commercial Charging Sites.” Applied Energy 303: 117651. doi:10.1016/j.apenergy.2021.117651.
   Web of Science ®Google Scholar
• Song, H., A. K. Qin, and F. D. Salim. “Multivariate Electricity Consumption Prediction with Extreme Learning Machine.” 2016 International Joint Conference on Neural Networks (IJCNN), Ghent, Belgium: IEEE; 2016, p. 2313–2320.
   Google Scholar
• Sun, C., T. Li, S. H. Low, and V. O. Li. 2020. “Classification of Electric Vehicle Charging Time Series with Selective Clustering.” Electric Power Systems Research 189: 106695. doi:10.1016/j.epsr.2020.106695.
   Web of Science ®Google Scholar
• Sun, X.-H., T. Yamamoto, and T. Morikawa. 2016. “Fast-charging Station Choice Behavior among Battery Electric Vehicle Users.” Transportation Research Part D: Transport and Environment 46: 26–39. doi:10.1016/j.trd.2016.03.008.
   Web of Science ®Google Scholar
• Ullah, I., K. Liu, T. Yamamoto, R. E. Al Mamlook, and A. Jamal. 2021a. “A Comparative Performance of Machine Learning Algorithm to Predict Electric Vehicles Energy Consumption: A Path Towards Sustainability.” Energy & Environment. doi:10.1177/0958305X211044998. Article first published online: October 18, 2021 .
   Web of Science ®Google Scholar
• Ullah, I., K. Liu, T. Yamamoto, M. Zahid, and A. Jamal. 2021b. “Electric Vehicle Energy Consumption Prediction Using Stacked Generalization: An Ensemble Learning Approach.” International Journal of Green Energy 18: 896–909. doi:10.1080/15435075.2021.1881902.
   Web of Science ®Google Scholar
• Ullah, I., K. Liu, T. Yamamoto, M. Zahid, and A. Jamal. 2022. “Prediction of Electric Vehicle Charging Duration Time Using Ensemble Machine Learning Algorithm and Shapley Additive Explanations.” International Journal of Energy Research. doi:10.1002/er.8219. First published: 08 June 2022 .
   PubMed Web of Science ®Google Scholar
• Wang, Y., J. Bi, W. Guan, and X. Zhao. 2018. “Optimising Route Choices for the Travelling and Charging of Battery Electric Vehicles by considering Multiple Objectives.” Transportation Research Part D: Transport and Environment 64: 246–261. doi:10.1016/j.trd.2017.08.022.
   Web of Science ®Google Scholar
• Wang, Y., J. Bi, C. Lu, and C. Ding. 2020. “Route Guidance Strategies for Electric Vehicles by considering Stochastic Charging Demands in a time-varying Road Network.” Energies 13: 2287. doi:10.3390/en13092287.
   Web of Science ®Google Scholar
• Wang, J.-S., S.-X. Li, and J. B. de Andrade. 2019. “An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism.” Scientific Reports 9: 1–21. doi:10.1038/s41598-018-37186-2.
   PubMed Web of Science ®Google Scholar
• Wang, J., J. Xu, and X. Wang. 2018. “Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning.” ArXiv Preprint, ArXiv:180101596. doi:10.48550/arXiv.1801.01596.
   Google Scholar
• Wang, C., J. Yang, N. Liu, and Y. Mao. “Study on Siting and Sizing of battery-switch Station.” 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT) Weihai, China, IEEE; 2011, p. 657–662. doi:10.1109/DRPT.2011.5993975.
   Google Scholar
• Xi, H., L. He, Y. Zhang, and Z. Wang. 2021. “Differentiable Road Pricing for environment-oriented Electric Vehicle and Gasoline Vehicle Users in the bi-objective Transportation Network.” Transportation Letters 14 (6): 660–674. doi:10.1080/19427867.2021.1919468.
   Web of Science ®Google Scholar
• Xie, C., T.-G. Wang, X. Pu, and A. Karoonsoontawong. 2017. “Path-constrained Traffic Assignment: Modeling and Computing Network Impacts of Stochastic Range Anxiety.” Transportation Research Part B: Methodological 103: 136–157. doi:10.1016/j.trb.2017.04.018.
   Web of Science ®Google Scholar
• Yang, Y., Z. Tan, and Y. Ren. 2020. “Research on Factors that Influence the Fast Charging Behavior of Private Battery Electric Vehicles.” Sustainability 12: 3439. doi:10.3390/su12083439.
   Web of Science ®Google Scholar
• Yang, Y., E. Yao, Z. Yang, and R. Zhang. 2016. “Modeling the Charging and Route Choice Behavior of BEV Drivers.” Transportation Research Part C: Emerging Technologies 65: 190–204. doi:10.1016/j.trc.2015.09.008.
   Web of Science ®Google Scholar
• Zahid, M., Y. Chen, S. Khan, A. Jamal, M. Ijaz, and T. Ahmed. 2020. “Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do spatio-temporal Attributes Matter?” International Journal of Environmental Research and Public Health 17: 3937. doi:10.3390/ijerph17113937.
   PubMed Web of Science ®Google Scholar
• Zhang, W., D. Zhang, B. Mu, L. Y. Wang, Y. Bao, J. Jiang, H. Morais, et al. 2017. “Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids”. Energies 10: 147. 10.3390/en10020147.
   Web of Science ®Google Scholar