Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Ji Li, Quan Zhou, Yinglong He, Bin Shuai, Ziyang Li, Huw Williams and Hongming Xu

Applied Energy, 2019, vol. 253, issue C, -

Abstract: This paper investigates an online predictive control strategy for series-parallel plug-in hybrid electric vehicles (PHEVs), resulting in a novel online optimization methodology named the dual-loop online intelligent programming (DOIP) that is proposed for velocity prediction and energy-flow control. By reconsidering the change of driving behaviours at each look-ahead step, this methodology guarantees the effectiveness of optimal control sequence in the energy-saving efficiency of online predictive energy management. The design procedure starts with the simulation of a series-parallel PHEV using a systematic control-oriented model and the definition of a cost function. Inspired by fuzzy granulation technology, a deep fuzzy predictor is created to achieve driver-oriented velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities between vehicle speed and acceleration. To determine the optimal control behaviours and power distribution between two energy sources, chaos-enhanced accelerated swarm optimization is developed for the DOIP algorithm. The prediction capability of the deep fuzzy predictor is evaluated by comparing with two existing predictors over the WLTP-based driving cycle. The proposed control strategy is contrasted with short-sighted and dynamic programming based counterparts, and validated by a driver-in-the-loop test. The results demonstrate that the deep fuzzy predictor can effectively recognize driving behaviour and reduce at least 19% errors compared to involved Markov chain based predictors. Online predictive control strategy using the DOIP algorithm is able to significantly reduce 9.37% fuel consumption from the baseline and shorten computational time.

Keywords: Driver-in-the-loop test; Fuzzy mathematics; Online predictive control; Particle swarm optimization; Plug-in hybrid electric vehicle (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.apenergy.2019.113617

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