Model-based design validation and optimization of drive systems in electric, hybrid, plug-in hybrid and fuel cell vehicles

Currently we are at the beginning of the fourth industrial revolution, which involves, among others, technology to prevent climate change, transformation of the transport sector, digitization, and artificial intelligence. This paper contributes to technological development accelerating the design of ecological vehicles and their introduction in smart cities. This paper describes an adaptive, flexible, expandable, simple, and high-accuracy methodology capable of maximizing vehicle range with the finest computational effort, thanks to a genetic algorithm. Further, it produces predictive information to minimize cost, volume, and weight of the drivetrain in the vehicle structure while meeting the desires of the designer. Range is calculated using a standard or customised drive cycle. Calculation of the CO2 produced in the electricity production process is also provided. The reliability of the system has been verified with commercially available vehicles, taking into account their technical specifications such as electric motor type (e.g. induction, permanent magnet, or hybrid electric motors), the technology of the energy storage system (e.g. nickel-metal hydride or lithium-ion batteries or fuel cell), configuration (e.g. pure electric vehicle, series/parallel/series-parallel (plug-in) hybrid electric vehicle, or fuel cell vehicle) and their category (light quadricycles [L6e], heavy quadricycles [L7e], passenger cars [M1], vans [N1], or low-speed vehicles). The results obtained demonstrate that the model is capable of extraordinary precision. (c) 2022 Published by Elsevier Ltd.

Automated summary

This paper presents an adaptive and high-accuracy methodology that utilizes genetic algorithms to accelerate the design and implementation of ecological vehicles in smart cities. The methodology maximizes vehicle range with minimal computational effort and provides predictive information on cost, volume, and weight. The reliability and precision of the model have been verified using commercially available vehicles.