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<p>Electric Vehicle (EV) fleet management maintains a group of EVs, which are used for commercial and organizational purposes. Due to the rapid expansion of EV fleets, intelligent management systems are necessary to optimize routes. Existing approaches lack interaction between multiple fleet vehicles and fail to adapt to dynamic grid conditions. Hence, this paper proposes a Wide Slice Dense Network with Adaptive Artificial Lemming Algorithm (WiSDN_Ada-ALA) for EV fleet route optimization and path finding. At first, the EV system model is simulated, where behaviour of EVs is optimized under certain conditions. Then, the input data are normalized using Stopp Normalization. After normalization, intricate features are selected using the Adaptive Artificial Lemming Algorithm (Ada-ALA). Ada-ALA combines an adaptive strategy and Artificial Lemming Algorithm (ALA). Then, data augmentation is performed by Random Value-Based Oversampling (RVOS). Load prediction is done using a Bi-directional Dilated Long Short-Term Memory network (Bi-DLSTM), and then route optimization and path finding are performed by WiSDN, which combines Wide-Slice Residual Networks (WISeR) and Dense Network (DenseNet). Then, the hyperparameters of WiSDN are trained by Ada-ALA. With K-Fold 9, WiSDN_Ada-ALA obtained a charging cost of 9.407 Dollars per kilowatt-hour ($/kWh), a distance of 5.191&#xa0;km (Km), available power of 57.602 Kilowatt (kW), and Normalized Root Mean Square Error (RMSE) of 0.241.</p>

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Electric Vehicle Fleet Load Prediction and Route Optimization Using Bidirectional Dilated Long Short-term Memory and Wide Slice Dense Network

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • S. Shobana,
  • Deepthi Kothapeta,
  • Gandham Raja Vikram,
  • Rameshbabu Akarapu

摘要

Electric Vehicle (EV) fleet management maintains a group of EVs, which are used for commercial and organizational purposes. Due to the rapid expansion of EV fleets, intelligent management systems are necessary to optimize routes. Existing approaches lack interaction between multiple fleet vehicles and fail to adapt to dynamic grid conditions. Hence, this paper proposes a Wide Slice Dense Network with Adaptive Artificial Lemming Algorithm (WiSDN_Ada-ALA) for EV fleet route optimization and path finding. At first, the EV system model is simulated, where behaviour of EVs is optimized under certain conditions. Then, the input data are normalized using Stopp Normalization. After normalization, intricate features are selected using the Adaptive Artificial Lemming Algorithm (Ada-ALA). Ada-ALA combines an adaptive strategy and Artificial Lemming Algorithm (ALA). Then, data augmentation is performed by Random Value-Based Oversampling (RVOS). Load prediction is done using a Bi-directional Dilated Long Short-Term Memory network (Bi-DLSTM), and then route optimization and path finding are performed by WiSDN, which combines Wide-Slice Residual Networks (WISeR) and Dense Network (DenseNet). Then, the hyperparameters of WiSDN are trained by Ada-ALA. With K-Fold 9, WiSDN_Ada-ALA obtained a charging cost of 9.407 Dollars per kilowatt-hour ($/kWh), a distance of 5.191 km (Km), available power of 57.602 Kilowatt (kW), and Normalized Root Mean Square Error (RMSE) of 0.241.