A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems

• Published in: Energies (Basel) Vol. 9; no. 1; p. 25
• Main Authors: Wang, Yanzi, Wang, Weida, Zhao, Yulong, Yang, Lei, Chen, Wenjun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2016
• Subjects: battery; Demand; Electric batteries; Electric power; Electric power distribution; Electric power generation; Electric vehicles; Energy storage; Fuzzy logic; hybrid energy storage system (HESS); Markov processes; Markov random prediction; Mathematical models; Power management; Simulation; Strategy; ultracapacitor (UC); Beijing China; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en9010025

Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs) in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power management strategy based on Markov random prediction.