Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility
Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services. Shifting operation from reactive repair to preventive maintenance depends on accurate, forward-looking fault-risk prediction, a task complicated by the heterogeneous time scales of physical, behavioral, contextual, and historical signals and by forecasting over a multi-week horizon. We develop FGDSE,
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- PossiblePossibly related (embedding) · 50%A geospatial machine-learning framework for regional electrification estimation mapping and trend analysis using demographic and infrastructure features - Nature →
- PossiblePossibly related (embedding) · 46%Adaptive primal–dual Q-learning for electric vehicle route optimization on real-world charging networks - Nature →
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- LinkedLinked via arxiv author · 85%Cande Lian →
“Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble fram”
- LinkedLinked via arxiv author · 85%Wentao Zeng →
“Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble fram”
- LinkedLinked via arxiv author · 85%Jiabin Wu →
“Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble fram”
- LinkedLinked via arxiv author · 85%Yiming Bie →
“Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble fram”
- LinkedLinked via arxiv author · 85%Yiwei Zhou →
“Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble fram”