Simulation of Energy Control Strategy for Hybrid Electric Vehicle Based on Modified Dynamic Programming

Wang, Jinbo; Wang, Jian; Yang, Jun

doi:10.1109/cvci47823.2019.8951616

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…If operational and environmental details of a mission are known a priori, the energy management problem can be formulated as an optimal control problem. Dynamic Programming (DP) (Bai et al, 2020;Wang et al, 2019) and Pontryagin's Minimum Principle (PMP) are widespread approaches to derive an optimal solution to hybrid powertrain energy management problems, even though alternative approaches based on the calculus of variations have been considered (East and Cannon, 2019). Dynamic Programming requires discretizing both the state and the time.…”

Section: Introductionmentioning

confidence: 99%

ECMS for energy management of hybrid vessels via quasi-LPV control

Moreno¹,

Delprat²,

G³

et al. 2022

Preprint

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He is currently pursuing his Ph.D. degree in Automatic Control from the Polytechnic University Hauts-de-France, Valenciennes, France. His research interests are Energy Management Algorithms, Optimal control, Linear Matrix Inequalities and SOS techniques. Sébastien Delprat received his PhD in 2002 and became assistant professor at the University of Valenciennes and Hainaut Cambresis. His research activities are dedicated to vehicle control, and especially hybrid vehicle energy management. Graeme Hawksley was born in Liverpool, UK in 1959. He was awarded an MSc in microelectronics (hybrid control methods) from Bolton University in 2005. He formed Hybrid Marine Ltd in 1999 to conduct research and development into marine hybrid systems, and was granted three patents for hybrid control methods. He presents widely at international boat shows and part of the EU ISHY project (Implementation of Ship Hybridisation).

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Section: Introductionmentioning

confidence: 99%

ECMS for energy management of hybrid vessels via quasi-LPV control

Moreno¹,

Delprat²,

G³

et al. 2022

Preprint

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“…EMSs can be divided into RB-EMS (Sabri et al, 2016), global optimization EMS (Wang et al, 2019), instantaneous optimization EMS (Sun et al, 2017) and intelligent EMS (Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Adaptive energy management strategy for plug-in hybrid electric vehicles based on intelligent recognition of driving cycle

Shi

Liu

et al. 2022

Energy Exploration & Exploitation

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In order to enhance the adaptability of energy management strategy (EMS) to complex and changeable driving cycles, this paper proposed an adaptive energy management strategy (A-EMS) based on intelligent recognition of driving cycle (IRDC) by the back propagation neural network (BPNN) and genetic algorithm (GA). Firstly, BPNN is employed to design IRDC. Secondly, the equivalent fuel consumption minimization strategy (ECMS) is derived based on Pontryagin's minimum principle (PMP). Then, GA is used to optimize the MAP of the initial equivalent factor (EF) with the initial state of charge (SOC) and mileage. At the same time, the SOC penalty function and the velocity penalty function are employed to modify the initial EF, and then an adaptive minimum equivalent fuel consumption strategy (A-ECMS) is established. Finally, A-ECMS strategy model based on IRDC is modelled by the Matlab/Simulink software, and its model control effect is verified. Simulation results show that compared with ECMS strategy, A-ECMS strategy can maintain high fuel economy under complex driving cycles, and improve the vehicle's fuel economy up to 3%.

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Dynamic Energy Management Strategy of Hybrid Electric Vehicles based on Velocity Prediction

Lin

Jin

et al. 2021

IFAC-PapersOnLine

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Simulation of Energy Control Strategy for Hybrid Electric Vehicle Based on Modified Dynamic Programming

Cited by 3 publications

References 6 publications

ECMS for energy management of hybrid vessels via quasi-LPV control

ECMS for energy management of hybrid vessels via quasi-LPV control

Adaptive energy management strategy for plug-in hybrid electric vehicles based on intelligent recognition of driving cycle

Dynamic Energy Management Strategy of Hybrid Electric Vehicles based on Velocity Prediction

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