Autonomous driving paper index
Reliability-Informed Life Prediction for New Energy Vehicle Components
One-line summary
For new energy vehicle components, remaining service life requires crucial maintenance, but operating conditions and incomplete fault records still limit the sustainable development of models.
Engineering notes
Key topics: autonomous driving, deployment, prediction, planning. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
For new energy vehicle components, remaining service life requires crucial maintenance, but operating conditions and incomplete fault records still limit the sustainable development of models. This study constructs a data-driven framework for batteries and traction motors by integrating fault analysis, reliability parameter estimation, and machine learning-based prediction. This framework can support maintenance planning and spare parts planning to some extent. However, these results should be interpreted with caution because component type, brand coverage, and data quality may introduce unobserved biases. More extensive cross-brand datasets, prediction ranges that account for uncertainty, and real-time validation are needed before reliable large-scale deployment.
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