Potential analysis of a predictive energy management strategy designed to increase the efficiency of the powertrain in a hybrid vehicle with use of online available route information

Abstract Hybrid vehicles, with their combined use of internal combustion engines and electric motors, present a unique opportunity to leverage intelligent control strategies for optimal performance. The method presented in this paper aims to improve the efficiency of a hybrid powertrain through an increased usage of the advantages of the electric components. While conventional hybrid operation strategies must determine the torque-split on either rule-based decision logics or on strategies that are optimized for certain test scenarios a predictive strategy can optimize the torque-split under consideration of the upcoming load requirements. Therefore, this paper explores the development and implementation of real-time predictive driving and operating strategies for hybrid vehicles to enhance fuel efficiency and reduce environmental impact. The data pertaining to road networks and traffic conditions, currently accessible from numerous map providers, can be effectively utilized to further amplify the benefits offered by hybrid vehicles. This information will be used to improve the accuracy of the predicted driving situations, which in turn improves the effectiveness of the predictive hybrid strategy by increasing the accuracy of the predicted load requirements. Advancements in prediction model accuracy have been shown to enhance the effectiveness of predictive hybrid control strategies, leading to higher energy efficiency and lower emissions. The present study develops and evaluates enhanced prediction models and a real-time predictive energy management strategy in simulation and Hardware-in-the-Loop (HiL) testing. We position the contribution at the system level, integrating forecasting, horizon supervision, receding-horizon P-ECMS with on-horizon EF adaptation and a state-change cost, and ΔSOC-fair evaluation under HiL-level timing. For a representative urban/rural route, the enhanced predictive strategy achieves a ΔSOC-fair, fuel-equivalent reduction of up to 12.18%, approaching the 14.4% obtained under perfect prediction, while reducing engine state transitions from 110 to 30, thereby improving both efficiency and driving comfort. This study is framed as a potential analysis: in this recuperation-rich case the strategy delivers high reduction potential, whereas on a Real Driving Emissions (RDE) trip with limited recuperation opportunity the improvement is ≈ 1% even under perfect prediction, indicating route-dependent potential rather than route-agnostic savings.