Work

Driving Strategy Optimization in Experimental Electric Vehicles: A Study on Optimization Algorithms

Year: 2024

Type: conference-paper

Abstract: In this paper, the driving strategy simulations for a single-seat, lightweight, energy-efficient experimental electric vehicle are introduced. The vehicle’s operation is simulated using a developed measurement-based vehicle model in the simulation environment. The optimization was performed for the UniTrack platform at the ZalaZone proving ground using the algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), with different optimization settings corresponding to varying iterations and initial population/swarm sizes. A 2.95% difference was observed between the least effective and the best PSO results, where both the number of iterations and swarm size were doubled. This demonstrates the effectiveness of PSO in solving the presented driving strategy problem, even when using fewer iterations and a smaller swarm size.

DOI: 10.3390/engproc2024079042

Authors: Zoltán Pusztai, Ferenc Szauter, Ferenc Friedler

Institution: Széchenyi István University

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Funder: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap

OpenAlex ID: W4404115779

FWCI:

Cites: 12

Cited by:

Related to: 10