TY - JOUR
T1 - A novel energy-efficiency optimization approach based on driving patterns styles and experimental tests for electric vehicles
AU - Valladolid, Juan Diego
AU - Patino, Diego
AU - Gruosso, Giambattista
AU - Correa-Flórez, Carlos Adrián
AU - Vuelvas, José
AU - Espinoza, Fabricio
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/5/2
Y1 - 2021/5/2
N2 - 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 energyefficiency 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%.
AB - 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 energyefficiency 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%.
KW - Electrical vehicle
KW - Energy management
KW - Optimization of driving patterns
KW - Particle swarm optimization algorithm
KW - System efficiency
UR - http://www.scopus.com/inward/record.url?scp=85105877241&partnerID=8YFLogxK
U2 - 10.3390/electronics10101199
DO - 10.3390/electronics10101199
M3 - Article
AN - SCOPUS:85105877241
SN - 2079-9292
VL - 10
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 10
M1 - 1199
ER -