A Novel Adaptive Equivalence Fuel Consumption Minimisation Strategy for a Hybrid Electric Two-Wheeler

One of the major challenges in implementing the equivalent fuel consumption minimisation strategy in hybrid electric vehicles is the adaptation of the equivalence factor to real-world driving. In this paper, a novel adaptive equivalent fuel consumption minimisation strategy (A-ECMS) has been developed for a hybrid two-wheeler to further improve fuel savings by predicting the drive cycles and thereby estimating and adapting the equivalence factor online for the ECMS energy management control. A learning vector quantitative neural network (LVQNN)-based classifier was first proposed to recognise the real-world driving cycle based on a fixed time window of past driving information. Along with standardised drive cycles, real-world driving data were used in the learning process to increase the robustness of the learning. The A-ECMS is then capable of regulating its equivalence factors online based on the LVQNN controller output. Numerical simulation results indicated that there was considerable improvement in fuel economy of the vehicle with the proposed methodology, up to 10.7%, compared to the use of traditional ECMS which was manually optimised for a single drive cycle. The average improvement in fuel economy over the ten drive cycles considered for testing is 3.93%.

Automated summary

This paper proposes a novel adaptive equivalent fuel consumption minimisation strategy for hybrid vehicles, which predicts drive cycles and adjusts equivalence factors to improve fuel economy. The results showed significant improvement in fuel savings compared to traditional methods.