RDR_prediction: Electric Vehicle Remaining Driving Range Prediction Based on Multimodal Models and Transfer Learning

RDR_prediction is an open-source solution addressing range anxiety among electric vehicle users. It uses a multimodal deep learning model to fuse multi-source data such as vehicle status, driving, environment, and driving behavior, and combines transfer learning techniques to solve problems like data scarcity and distribution differences, effectively improving the accuracy and generalization ability of remaining driving range prediction.

Traditional range prediction relies only on single battery parameters (e.g., SOC), ignoring complex factors like driving habits, road conditions, and ambient temperature, leading to large errors. Multimodal data affecting range includes vehicle status (battery voltage/current/SOC), driving data (speed/acceleration), environmental data (temperature/humidity), and driving behavior (frequency of sudden acceleration/braking). Fusing these data is key to improving prediction accuracy.

Multimodal Deep Learning Architecture: Process time-series data via a temporal encoder, extract local features using CNN, adaptively fuse multimodal features with an attention mechanism, and finally output the predicted value through a regression layer. Transfer Learning Application: To address issues like data scarcity and distribution differences, strategies such as feature transfer (fine-tuning pre-trained features from the source domain), model fine-tuning (initializing with source domain model then training on target domain), and domain adaptation (adversarial training to reduce domain distribution differences) are adopted.

Dataset: Includes BMS data (voltage/current/SOC/SOH), CAN bus data (vehicle speed/mileage), GPS data (location/altitude), and environmental data (temperature/weather). Evaluation Metrics: RMSE (deviation), MAE (intuitive error), MAPE (relative error), R² (explained variance). Results: The multimodal model outperforms single-modal baselines; transfer learning performs better than training from scratch when target domain data is limited; the model is highly adaptable to scenarios like urban commuting and highways.

Users: Reduce range anxiety, optimize charging plans, and get personalized driving advice; Manufacturers: Enhance product competitiveness, support rapid launch of new models, and provide personalized services; Industry: Drive technological progress, promote data sharing, and support intelligent traffic scheduling and route planning.

Code Structure: Data preprocessing (cleaning/feature engineering/normalization), model definition (multimodal network), training script, evaluation script, transfer learning module; Dependencies: Deep learning frameworks (PyTorch/TensorFlow), data processing (Pandas/NumPy), visualization (Matplotlib/Seaborn), machine learning tools (Scikit-learn).

Current Limitations: Relies on high-quality data, high computational complexity, generalization to extreme weather/special driving scenarios needs verification; Future Directions: Online learning to adapt to changes in driving habits, edge deployment to optimize in-vehicle resources, uncertainty quantification to provide confidence intervals, federated learning for collaborative training while protecting privacy.