PESTEL Analysis: Adaptive EF-Predictor for PHEV Powertrains

A software module for Plug-in Hybrid Electric Vehicles that uses machine learning to predict the optimal equivalence factor in real time from driving-profile features and battery state, minimizing fuel consumption without a pre-defined route. New evidence confirms the core building blocks are feasible: CAN-bus feature pipelines, compact neural regressors, and real-time adaptation of the equivalence factor. Because route and traffic preview demonstrably improves on route-free strategies, the module is positioned as a route-free-first baseline that can optionally fuse navigation data when it is available.