A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic thresho...

Full description

Bibliographic Details
Main Authors: Pei Li, Jun Yan, Qunzhang Tu, Ming Pan, Jinhong Xue
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/8450213
id doaj-a8f9bd78f4f9407386d48fd1b9bfb08e
record_format Article
spelling doaj-a8f9bd78f4f9407386d48fd1b9bfb08e2020-11-25T00:43:16ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/84502138450213A Novel Energy Management Strategy for Series Hybrid Electric Rescue VehiclePei Li0Jun Yan1Qunzhang Tu2Ming Pan3Jinhong Xue4Field Engineering College, Army Engineering University of PLA, Nanjing 210007, ChinaField Engineering College, Army Engineering University of PLA, Nanjing 210007, ChinaField Engineering College, Army Engineering University of PLA, Nanjing 210007, ChinaField Engineering College, Army Engineering University of PLA, Nanjing 210007, ChinaField Engineering College, Army Engineering University of PLA, Nanjing 210007, ChinaThe performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.http://dx.doi.org/10.1155/2018/8450213
collection DOAJ
language English
format Article
sources DOAJ
author Pei Li
Jun Yan
Qunzhang Tu
Ming Pan
Jinhong Xue
spellingShingle Pei Li
Jun Yan
Qunzhang Tu
Ming Pan
Jinhong Xue
A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle
Mathematical Problems in Engineering
author_facet Pei Li
Jun Yan
Qunzhang Tu
Ming Pan
Jinhong Xue
author_sort Pei Li
title A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle
title_short A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle
title_full A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle
title_fullStr A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle
title_full_unstemmed A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle
title_sort novel energy management strategy for series hybrid electric rescue vehicle
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2018-01-01
description The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.
url http://dx.doi.org/10.1155/2018/8450213
work_keys_str_mv AT peili anovelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT junyan anovelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT qunzhangtu anovelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT mingpan anovelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT jinhongxue anovelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT peili novelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT junyan novelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT qunzhangtu novelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT mingpan novelenergymanagementstrategyforserieshybridelectricrescuevehicle
AT jinhongxue novelenergymanagementstrategyforserieshybridelectricrescuevehicle
_version_ 1725279424865107968

Similar Items