In the field of logistics and transportation, balancing high-quality demand with economic benefits is a crucial issue. The introduction of electric vehicles offers new solutions to this challenge. However, the limited range of electric vehicles poses a significant challenge for logistics operations. To effectively address this problem, this paper proposes an electric vehicle path planning method based on convolutional neural networks and long-short-term memory (CNN-LSTM-EVPP). Specifically, the CNN-LSTM model is employed to predict the driving energy consumption for each route by combining traffic data and road data. Finally, the NSGA-II algorithm is used to optimize the predicted energy consumption values and find the optimal path. Experiments show that the method is effective. The algorithm has the potential to enhance distribution efficiency and reduce costs. The optimized path length is reduced by 8.87% and the driving energy consumption is reduced by 8.31%.
Research on Electric Vehicle Route Planning and Energy Consumption Prediction Based on CNN-LSTM Model
2024-08-23
1409438 byte
Conference paper
Electronic Resource
English
Battery electric vehicle energy consumption prediction for a trip based on route information
SAGE Publications | 2018
|Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning
DOAJ | 2024
|Research on Vehicle Trajectory Prediction Based on Improved LSTM Model
Springer Verlag | 2024
|