A method for the prediction of future driving conditions and for the energy management optimisation of a hybrid electric vehicle

被引：11

作者：

Donateo, Teresa ^{[1
]}

Pacella, Damiano ^{[1
]}

Laforgia, Domenico ^{[1
]}

机构：

[1] Univ Salento, Dipartimento Ingn Innovaz, I-73100 Lecce, Italy

来源：

INTERNATIONAL JOURNAL OF VEHICLE DESIGN | 2012年 / 58卷 / 2-4期

关键词：

HEVs; hybrid electric vehicles; ecological vehicles; plug-in vehicles; energy management strategy; vehicular communications; traffic simulation; simulation-based prediction; genetic algorithm; clustering algorithm; K-Means algorithm;

D O I：

10.1504/IJVD.2012.047385

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Vehicular communications are expected to enable the development of Intelligent Cooperative Systems for solving crucial problems related to mobility: road safety, traffic management etc. Information and Communication Technologies could also play an important role in order to optimise the energy management of conventional, hybrid and electrical vehicles and, thus, to reduce their environment impact. In particular, vehicular communications could be used to predict driving conditions with the objective to determine future load power demand. An adaptive energy management strategy for series Hybrid Electric Vehicles (HEVs) based on genetic algorithm optimised maps and the Simulation of Urban Mobility (SUMO) predictor is presented here.

引用

页码：111 / 133

页数：23

共 50 条

[1] Energy Management in a Parallel Hybrid Electric Vehicle for Different Driving Conditions
Schulze, Mirko
Mustafa, Rashad
Tilch, Benjamin
Eilts, Peter
Kucukay, Ferit
[J]. SAE INTERNATIONAL JOURNAL OF ALTERNATIVE POWERTRAINS, 2014, 3 (02) : 193 - 212
[2] Energy consumption characteristics based driving conditions construction and prediction for hybrid electric buses energy management
Wang, Yue
Li, Keqiang
Zeng, Xiaohua
Gao, Bolin
Hong, Jichao
[J]. ENERGY, 2022, 245
[3] Adaptive Energy Management Strategy Based on Intelligent Prediction of Driving Cycle for Plug-In Hybrid Electric Vehicle
Shi, Dapai
Li, Shipeng
Liu, Kangjie
Wang, Yun
Liu, Ruijun
Guo, Junjie
[J]. PROCESSES, 2022, 10 (09)
[4] Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles
Liu, Yonggang
Li, Jie
Gao, Jun
Lei, Zhenzhen
Zhang, Yuanjian
Chen, Zheng
[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2021, 158
[5] Series Hybrid Electric Vehicle Simultaneous Energy Management and Driving Speed Optimization
Chen, Boli
Evangelou, Simos A.
Lot, Roberto
[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2019, 24 (06) : 2756 - 2767
[6] Driving Assistance for Energy Management in Electric Vehicle
Karmakar, Mousumi
Nandi, Arup Kr
[J]. 2016 IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, DRIVES AND ENERGY SYSTEMS (PEDES), 2016,
[7] Energy Management of a Hybrid Electric Vehicle
Khadhraoui, Ahmed
Selmi, Tarek
Cherif, Adnen
[J]. ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2022, 12 (04) : 8916 - 8921
[8] Energy Management of Hybrid Electric Vehicle Using Vehicle Lateral Dynamic in Velocity Prediction
Li, Lin
Coskun, Serdar
Zhang, Fengqi
Langari, Reza
Xi, Junqiang
[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (04) : 3279 - 3293
[9] Energy Management of a Hybrid Electric Vehicle
Batteux, Michel
Donain, Matthieu
Fiani, Philippe
Garnit, Sadok
Noirtat, Eric
Roy, Francis
[J]. 25TH WORLD BATTERY, HYBRID AND FUEL CELL ELECTRIC VEHICLE SYMPOSIUM AND EXHIBITION PROCEEDINGS, VOLS 1 & 2, 2010, : 94 - 98
[10] Driving Pattern Prediction for an Energy Management System of Hybrid Electric Vehicles in a Specific Driving Course
Yokoi, Yasuhiro
Ichikawa, Shinji
Doki, Shinji
Okuma, Shigeru
Naitou, Takashi
Shiimado, Toshihiro
Miki, Nobuaki
[J]. IECON 2004: 30TH ANNUAL CONFERENCE OF IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOL 2, 2004, : 1727 - 1732

← 1 2 3 4 5 →