AI-Enabled Dynamic EV Route Optimization with Real-Time Traffic & Charging Constraints
TL;DR
Empirical validation demonstrates that this architecture satisfies strict enterprise service level agreements (SLAs) under peak infrastructure stress of 25,000 requests per second, achieving a robust Precision-Recall AUC of 0.895 and a Target Recall of 0.918.
Abstract
Rapid growth in electric vehicle (EV) fleets demands resilient, low-latency navigation systems capable of balancing dynamic vehicle kinetics against volatile external constraints. This paper presents an end-to-end operational AI architecture for real-time EV route optimization and state-of-charge (SoC) exhaustion risk classification. The proposed system is engineered as an event-driven, decoupled pipeline divided into four primary layers: an edge-enabled Ingestion Layer, a concurrent Streaming and Storage Layer, a Contextual Feature Graph Assembly Pipeline, and a hardware-accelerated Predictive Inference Engine. The lifecycle of an optimization event transitions continuously from raw high-frequency vehicle telemetry (e.g., instantaneous battery temperatures, spatial-temporal coordinates, and power draw) ingested at the edge into distributed Apache Kafka partitions. A stateful stream processor aggregates these inputs with real-time external indices including microsecond-scale lookups against an in-memory Redis cluster for sector traffic delays, grid spot-pricing metrics, and charging station queue times. These attributes are assembled into a dense numerical vector and passed through high-performance binary gRPC transport to an optimized XGBoost model hosted on a Triton Inference Server using the Forest Inference Library (FIL) backend. Incorporating asymmetric loss functions and native missing feature routing, the model classifies critical battery depletion risk (P(Exhaustion)) within a highly constrained target P95 latency window of under 12 milliseconds. When the classification score breaches a defined threshold (P ≥ 0.72), an automated override path routes the payload to a Deep Reinforcement Learning (DRL) network that dynamically mutates navigation trajectories, reserves charging infrastructure stalls, and updates the in-cabin display in real time. Empirical validation demonstrates that this architecture satisfies strict enterprise service level agreements (SLAs) under peak infrastructure stress of 25,000 requests per second, achieving a robust Precision-Recall AUC of 0.895 and a Target Recall of 0.918.