Intelligent energy management system for conventional autonomous vehicles

Duong Phan, Alireza Bab-Hadiashar, Chow Yin Lai, Bryn Crawford, Reza Hoseinnezhad, Reza N. Jazar and Hamid Khayyam

Energy, 2020, vol. 191, issue C

Abstract: Autonomous vehicles have been envisioned to increase vehicle safety, primarily via the reduction of accidents. However, their design could also affect the vehicle travel demand and energy consumption. Although battery-powered electric and hybrid-electric autonomous vehicles assume more widespread use than conventional autonomous vehicles, energy management is harder and more significant for conventional autonomous vehicles. As such, it is necessary to investigate how to manage energy consumption in conventional autonomous vehicles. In this paper, an energy management system is constructed and analyzed by using a road-power-demand model and an intelligent system to reduce fuel consumption for a conventional autonomous vehicle. The road-power-demand model utilizes three impact factors (i) environment-conditions (ii) driver-behavior, and (iii) vehicle-specifications. The proposed intelligent energy management system includes a fuzzy-logic-system with the aim of generating the desired engine torque, based on the vehicle road power demand and a PID controller to control the air/fuel ratio, by changing the throttle angle. Results show that the intelligent energy management system reduces the vehicle energy consumption from 7.2 to 6.71 L/100 km. Next, the parameters of the fuzzy-logic-system are intelligently optimized by the particle-swarm-optimization method and new results indicate that the vehicle energy consumption is reduced by around 9.58%.

Keywords: Autonomous vehicle; Intelligent energy management; Control strategies; Conventional autonomous vehicle; Fuzzy logic system; Particle swarm optimization; Artificial Intelligence (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321711

DOI: 10.1016/j.energy.2019.116476

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