A Novel Energy-Efficiency Optimization Approach Based on Driving Patterns Styles and Experimental Tests for Electric Vehicles

被引：17

作者：

Valladolid, Juan Diego ^{[1
,2
]}

Patino, Diego ^{[2
]}

Gruosso, Giambattista ^{[3
]}

Adrian Correa-Florez, Carlos ^{[2
]}

Vuelvas, Jose ^{[2
]}

Espinoza, Fabricio ^{[1
]}

机构：

[1] Univ Politecn Salesiana, Dept Automot Engn, Cuenca 101007, Ecuador

[2] Pontificia Univ Javeriana, Dept Elect Engn, Bogota 110321, Colombia

[3] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20135 Milan, Italy

来源：

ELECTRONICS | 2021年 / 10卷 / 10期

关键词：

electrical vehicle; energy management; system efficiency; optimization of driving patterns; particle swarm optimization algorithm; PARTICLE SWARM OPTIMIZATION; BATTERY PERFORMANCE; CONTROL STRATEGY; POWER; STATE; MANAGEMENT; SPEED; SIMULATION; TOPOLOGIES;

D O I：

10.3390/electronics10101199

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article proposes an energy-efficiency strategy based on the optimization of driving patterns for an electric vehicle (EV). The EV studied in this paper is a commercial vehicle only driven by a traction motor. The motor drives the front wheels indirectly through the differential drive. The electrical inverter model and the power-train efficiency are established by lookup tables determined by power tests in a dynamometric bank. The optimization problem is focused on maximizing energy-efficiency between the wheel power and battery pack, not only to maintain but also to improve its value by modifying the state of charge (SOC). The solution is found by means of a Particle Swarm Optimization (PSO) algorithm. The optimizer simulation results validate the increasing efficiency with the speed setpoint variations, and also show that the battery SOC is improved. The best results are obtained when the speed variation is between 5% and 6%.

引用

页数：21

共 48 条

[21] Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach
Lv, Chen
Hu, Xiaosong
Sangiovanni-Vincentelli, Alberto
Li, Yutong
Martinez, Clara Marina
Cao, Dongpu
[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 66 (04) : 2965 - 2975
[22] State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model
Ma, Xiao
Qiu, Danfeng
Tao, Qing
Zhu, Daiyin
[J]. APPLIED SCIENCES-BASEL, 2019, 9 (13):
[23] A Bidirectional Power Charging Control Strategy for Plug-in Hybrid Electric Vehicles
Mohammadi, Fazel
Nazri, Gholam-Abbas
Saif, Mehrdad
[J]. SUSTAINABILITY, 2019, 11 (16)
[24] A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles
Moura, Scott Jason
Fathy, Hosam K.
Callaway, Duncan S.
Stein, Jeffrey L.
[J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2011, 19 (03) : 545 - 555
[25] Neubauer J., 2013, P SAE WORLD C EXH DE, V1
[26] On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles
Rahman, Imran
Vasant, Pandian M.
Singh, Balbir Singh Mahinder
Abdullah-Al-Wadud, M.
[J]. ALEXANDRIA ENGINEERING JOURNAL, 2016, 55 (01) : 419 - 426
[27] Rajamani R, 2012, MECH ENG SER, P87, DOI 10.1007/978-1-4614-1433-9_4
[28] Salazar M., 2019, P 13 EUR C ANT PROP, P1
[29] Sarabi S, 2014, 2014 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT ENERGY AND POWER SYSTEMS (IEPS)
[30] Drive Cycle Generation for Design Optimization of Electric Vehicles
Schwarzer, Volker
Ghorbani, Reza
[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2013, 62 (01) : 89 - 97

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