Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range
Energies, Vol. 12, No. 7, pp. 1396-14162019A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed. electric vehicles; deep neural networks; energy demand modeling; SoC at destination; fuel consumption; all-electric range; big data
Cited by 31
▾
-
[1]
Hierarchical model predictive control-based electric vehicle fleet charging management🔗
Energy Conversion and Management, 2025
-
[2]
Representative driving cycle-based framework to model indirect CO2 emissions from electric cars🔗
Transportation Research Part D: Transport and Environment, 2025
-
-
[4]
Optimizing electric vehicle driving range prediction using deep learning: A deep neural network (DNN) approach🔗
Results in Engineering, 2024
-
[5]
Mitigating Grid Peaks in E-Mobility Charging A Comparative Evaluation of §14a EnWG and Priority-Driven Load Reduction Approaches🔗
IEEE International Conference on Renewable Energy Research and Applications, 2024
-
-
-
[8]
Investigation of Long Short-Term Memory Networks in Short-Term Electric Vehicle Charging Load Modeling🔗
International Conference on Electrical and Electronics Engineering, 2023
-
[9]
Conceptual Implementation of LSTM-Improved LA Based Smart Electric Vehicle Battery Management System🔗
2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 2023
-
-
-
[12]
Short-term electric vehicle charging demand prediction: A deep learning approach🔗
Applied Energy, 2023
-
[13]
SVM Modeling Simulation to Evaluate the Electric Vehicle Transmitting Points🔗
2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 2023
-
[14]
Energy Management and Optimization of Large-Scale Electric Vehicle Charging on the Grid🔗
World Electric Vehicle Journal, 2023
-
[15]
A Novel Method for Estimating Parameters of Battery Electric Vehicles🔗
Intelligent Systems with Applications, 2022
-
[16]
A Machine Learning Method for EV Range Prediction with Updates on Route Information and Traffic Conditions🔗
AAAI Conference on Artificial Intelligence, 2022
-
[17]
Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles🔗
Applied Sciences, 2022
-
[18]
Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data🔗
Sustainability, 2022
-
[19]
Influence of Auxiliary Loads on the Energy Consumption of Electric Vehicle – A Case Study🔗
2021 IEEE Transportation Electrification Conference (ITEC-India), 2021
-
[20]
Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system🔗
Electrical Engineering, 2021
-
-
[22]
Neural Network Control of Green Energy Vehicles with Blended Braking Systems🔗
The Renewable Energies and Power Quality Journal (RE&PQJ), 2021
-
-
[24]
Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles🔗
Sustainability, 2021
-
-
-
-
[28]
Nonlinear Modeling of Lithium-Ion Battery Cells for Electric Vehicles using a Hammerstein–Wiener Model🔗
Journal of Electrical Engineering and Technology, 2020
-
-
[30]
Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks🔗
Computer Modeling in Engineering & Sciences, 2025
-
[31]
Eco-routing navigation systems in electric vehicles: A comprehensive survey🔗
Autonomous and Connected Heavy Vehicle Technology, 2022
Energies, Vol. 12, No. 7, pp. 1396-1416
2019
Cited by 31
▾
-
[1] Hierarchical model predictive control-based electric vehicle fleet charging management🔗Energy Conversion and Management, 2025
-
[2] Representative driving cycle-based framework to model indirect CO2 emissions from electric cars🔗Transportation Research Part D: Transport and Environment, 2025
-
[4] Optimizing electric vehicle driving range prediction using deep learning: A deep neural network (DNN) approach🔗Results in Engineering, 2024
-
[5] Mitigating Grid Peaks in E-Mobility Charging A Comparative Evaluation of §14a EnWG and Priority-Driven Load Reduction Approaches🔗IEEE International Conference on Renewable Energy Research and Applications, 2024
-
[8] Investigation of Long Short-Term Memory Networks in Short-Term Electric Vehicle Charging Load Modeling🔗International Conference on Electrical and Electronics Engineering, 2023
-
[9] Conceptual Implementation of LSTM-Improved LA Based Smart Electric Vehicle Battery Management System🔗2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 2023
-
[12] Short-term electric vehicle charging demand prediction: A deep learning approach🔗Applied Energy, 2023
-
[13] SVM Modeling Simulation to Evaluate the Electric Vehicle Transmitting Points🔗2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 2023
-
[14] Energy Management and Optimization of Large-Scale Electric Vehicle Charging on the Grid🔗World Electric Vehicle Journal, 2023
-
[15] A Novel Method for Estimating Parameters of Battery Electric Vehicles🔗Intelligent Systems with Applications, 2022
-
[16] A Machine Learning Method for EV Range Prediction with Updates on Route Information and Traffic Conditions🔗AAAI Conference on Artificial Intelligence, 2022
-
[17] Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles🔗Applied Sciences, 2022
-
[18] Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data🔗Sustainability, 2022
-
[19] Influence of Auxiliary Loads on the Energy Consumption of Electric Vehicle – A Case Study🔗2021 IEEE Transportation Electrification Conference (ITEC-India), 2021
-
[20] Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system🔗Electrical Engineering, 2021
-
[22] Neural Network Control of Green Energy Vehicles with Blended Braking Systems🔗The Renewable Energies and Power Quality Journal (RE&PQJ), 2021
-
[24] Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles🔗Sustainability, 2021
-
[28] Nonlinear Modeling of Lithium-Ion Battery Cells for Electric Vehicles using a Hammerstein–Wiener Model🔗Journal of Electrical Engineering and Technology, 2020
-
[30] Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks🔗Computer Modeling in Engineering & Sciences, 2025
-
[31] Eco-routing navigation systems in electric vehicles: A comprehensive survey🔗Autonomous and Connected Heavy Vehicle Technology, 2022