Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle. (15th May 2022)

• Record Type: Journal Article
• Authors: Wei, Changyin; Chen, Yong; Li, Xiaoyu; Lin, Xiaozhe
• Is Part Of: Energy. Volume 247(2022)
• Publication Date: 2022-05-15
• Subjects: Energy management strategy -- Driving pattern recognition -- Fuel economy -- Extender range electric logistics vehicle; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123478
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 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. Highlights: An improved extreme learning machine for driving pattern recognition model is proposed. Adaptive ECMS is searched by driving pattern recognition and SOC reference. Three typical driving patterns have been classified using the K-means clustering. Adaptive ECMS for an ERELV has been proposed and verified.