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

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Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles. / Li, Ji; Zhou, Quan; He, Yinglong; Shuai, Bin; Li, Ziyang; Williams, Huw; Xu, Hongming.

In: Applied Energy, Vol. 253, 113617, 01.11.2019.

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@article{efb8b3bd3be54c84bb2aacaa4d489fb3,
title = "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles",
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",
author = "Ji Li and Quan Zhou and Yinglong He and Bin Shuai and Ziyang Li and Huw Williams and Hongming Xu",
year = "2019",
month = "11",
day = "1",
doi = "10.1016/j.apenergy.2019.113617",
language = "English",
volume = "253",
journal = "Applied Energy",
issn = "0306-2619",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

AU - Li, Ji

AU - Zhou, Quan

AU - He, Yinglong

AU - Shuai, Bin

AU - Li, Ziyang

AU - Williams, Huw

AU - Xu, Hongming

PY - 2019/11/1

Y1 - 2019/11/1

N2 - 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.

AB - 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.

KW - Driver-in-the-loop test

KW - Fuzzy mathematics

KW - Online predictive control

KW - Particle swarm optimization

KW - Plug-in hybrid electric vehicle

UR - http://www.scopus.com/inward/record.url?scp=85069809340&partnerID=8YFLogxK

U2 - 10.1016/j.apenergy.2019.113617

DO - 10.1016/j.apenergy.2019.113617

M3 - Article

AN - SCOPUS:85069809340

VL - 253

JO - Applied Energy

JF - Applied Energy

SN - 0306-2619

M1 - 113617

ER -