Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle

Improving the fuel economy and optimizing power allocation of the extender range electric logistics vehicles importantly relies on reasonable energy management strategies and accurate driving pattern recognition. Herein, an adaptive equivalent consumption minimization strategy for extender range electric logistics vehicles is proposed. Firstly, a K-means clustering algorithm has been utilized to classify driving blocks. A novel driving pattern recognition approach is designed by using combination of variational mode decomposition (VMD) and extreme learning machine (ELM). Subsequently, a state-of charge reference planning approach is proposed based on traffic information with dynamic programing for guiding battery energy allocation. Combining the driving pattern recognition results with the reference state-of-charge value, the proposed method acquires the optimal control action through minimizing the objective function. A hardware-in-the-loop test platform by Model Based Design (MBD) approach is implemented to validate the performance of controller. It has been verified that the proposed method can conserve the equivalent fuel consumption by respectively over 6.04% and 3.79%, and suppress the average battery power transients by over 11.61% and 2.85%, compared with the conventional energy management strategies. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes an adaptive equivalent consumption minimization strategy for extender range electric logistics vehicles, which aims to improve fuel economy and optimize power allocation. By combining driving pattern recognition and state-of-charge reference planning, the proposed method can adjust control actions in real time, leading to significant reduction in energy consumption and battery power transients.