Hybrid Deep Learning Mechanism for Charging Control and Management of Electric Vehicles


  •   Ashwin Kavasseri Venkitaraman

  •   Venkata Satya Rahul Kosuru


In perspective of their environmental friendliness and energy efficiency, Electric Vehicles (EVs) are posing a threat to traditional gasoline automobiles. Identifying the future charging needs of EV users may be aided by the forecasting of states linked to EV charging. It might deliver customized charge capacity statistics based on users' real-time locations as well as direct the operation and management of charging infrastructure. Consequently, an emergent problem is the effective model of EV charging state predictions. In this study, a hybrid deep learning approach is suggested to assure safe and dependable charging operations that prevent the battery from being overcharged or discharged. A Recursive Neural Networks (RNNs) for feature extraction process is suggested to acquire adequate feature information on the battery. The bidirectional gated recurrent unit framework (GRU) was then established by the study to predict the state of the EV. The GRU receives its input from the RNNs' output, which substantially enhances the effectiveness of the model. Because of its much simpler structure, the RNN-GRU has a lower computational performance. The experimental findings demonstrate the GRU method's ability to accurately track mileage of the electric vehicle. A hybrid deep learning-based prediction approach could give quick convergence speed less error rate in comparison to the appropriate method for obtaining state of charge estimate over conventional models, as demonstrated by the extensive real-world tests.

Keywords: Charge Control, Electric Vehicle, Gated Recurrent Units (GRU), Hybrid Deep Learning (HDL), Recursive Neural Network (RNN).


Wang K, Wang W, Wang L, Li L. An Improved SOC Control Strategy for Electric Vehicle Hybrid Energy Storage Systems. Energies, 2020;13(20):5297. https://doi.org/10.3390/en13205297.

Yuan, Deling, Sun M, Zhao M, Tang S, Qi J, Zhang X, Wang K, Li B. Persulfate promoted ZnIn2S4 visible light photocatalytic dye decomposition. Int. J. Electrochem. Sci., 2020; 15(2020): 8761-8770.

Zhang Q, Li G. A predictive energy management system for hybrid energy storage systems in electric vehicles. Electrical Engineering, 2019;101(3):759–770. https://doi.org/10.1007/s00202-019-00822-9.

Ren G, Wang J, Chen C, Wang H. A variable-voltage ultra-capacitor/battery hybrid power source for extended range electric vehicle. Energy, 2021; 231: 120837. https://doi.org/10.1016/j.energy.2021.120837.

Kai W, Shengzhe Z, Yanting Z, Jun R, Liwei L, Yong L. Synthesis of porous carbon by activation method and its electrochemical performance. Int J Electrochem Sci, 2018;13(11):10766–10773, 2018.

Wen J, Zhao D, Zhang C. An overview of electricity powered vehicles: Lithium-ion battery energy storage density and energy conversion efficiency. Renew. Energy, 2020;162:1629–1648.

Geng C, Jin X, Zhang X. Simulation research on a novel control strategy for fuel cell extended-range vehicles. Int. J. Hydrog. Energy, 2019;44(1): 408–420.

Chu Y, Wu Y, Chen J, Zheng S, Wang Z. Design of energy and materials for ammonia-based extended-range electric vehicles. Energy Procedia, 2019;158:3064–3069.

Li G, Xia J, Wang K, Deng Y, He X, Wang Y. A single-stage interleaved resonant bridgeless boost rectifier with high-frequency isolation. IEEE J. Emerg. Sel. Top. Power Electron., 2019;8(2):1767–1781.

Kouchachvili L, Yaïci W, Entchev E Hybrid battery/supercapacitor energy storage system for the electric vehicles. J. Power Sources, 2018;374:237–248.

She C, Wang Z, Sun F, Liu P, Zhang L. Battery aging assessment for real-world electric buses based on incremental capacity analysis and radial basis function neural network. IEEE Trans. Ind. Inform., 2019;16(5):3345–3354.

Huang L, Zhang Z, Wang Z, Zhang L, Zhu X, Dorrell DD. Thermal runaway behavior during overcharge for large-format Lithium-ion batteries with different packaging patterns. Journal of Energy Storage, 2019;25:100811. https://doi.org/10.1016/j.est.2019.100811.

Chen N, Zhang P, Dai J, Gui W. Estimating the state-of-charge of lithium-ion battery using an H-infinity observer based on electrochemical impedance model. Ieee Access, 2020;8:26872–26884.

Sun W, Qiu Y, Sun L, Huan Q. Neural network-based learning and estimation of battery state-of-charge: a comparison study between direct and indirect methodology. Int. J. Energy Res., 2020;44(13):10307–10319.

Luo L, Gu W, Wu Z, Zhou S. Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation. Appl. Energy, 2019;242:1274–1284.

Zhang C, Greenblatt JB, MacDougall P, Saxena S, Prabhakar AJ. Quantifying the benefits of electric vehicles on the future electricity grid in the midwestern United States. Appl. Energy, 2020;270:115174.

Bai Y, Li J, He H, Santos RCD, Yang Q. Optimal Design of a Hybrid Energy Storage System in a Plug-In Hybrid Electric Vehicle for Battery Lifetime Improvement. IEEE Access, 2020;8:142148–142158. https://doi.org/10.1109/access.2020.3013596.

Jampeethong P, Khomfoi S. Coordinated Control of Electric Vehicles and Renewable Energy Sources for Frequency Regulation in Microgrids. IEEE Access, 2020;8:141967–141976. https://doi.org/10.1109/access.2020.3010276.

Haupt L, Schöpf M, Wederhake L, Weibelzahl M. The influence of electric vehicle charging strategies on the sizing of electrical energy storage systems in charging hub microgrids. Appl. Energy, 2020;273:115231.


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How to Cite
Venkitaraman, A.K. and Kosuru, V.S.R. 2023. Hybrid Deep Learning Mechanism for Charging Control and Management of Electric Vehicles. European Journal of Electrical Engineering and Computer Science. 7, 1 (Jan. 2023), 38–46. DOI:https://doi.org/10.24018/ejece.2023.7.1.485.