A rule-based intelligent energy management system for an internal combustion engine vehicle

Khayyam, Hamid; Kouzani, Abbas Z.; Khoshmanesh, Khashayar; Hu, Eric

doi:10.1109/tencon.2008.4766637

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Energy management systems (EMSs) are control systems aiming for energy efficiency in AVs. Numerous methods have been proposed to improve engine efficiency, such as regenerative braking protocols [28], rule-based reduction of energy consumption [29,30] and intelligent approaches [31][32][33]. Among these approaches, intelligent control algorithms, such as fuzzy systems, show a great potential to deal with the nonlinearities of this problem in vehicles [31].…”

Section: Introductionmentioning

confidence: 99%

“…Among these approaches, intelligent control algorithms, such as fuzzy systems, show a great potential to deal with the nonlinearities of this problem in vehicles [31]. Many of the existing fuzzy logic-based EMS has been designed based on a type 1 fuzzy logic system (T1FLS) [29,[34][35][36]. The set of rules and membership functions of a T1FLS are formulated based on the knowledge of human beings.…”

Section: Introductionmentioning

confidence: 99%

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Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

et al. 2021

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A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the future. Autonomous vehicles are supposed to provide a safe, easy-to-use and environmentally friendly means of transport. To this end, improving AVs’ safety and energy efficiency by using advanced control and optimization algorithms has become an active research topic to deliver on new commitments: carbon reduction and responsible innovation. The focus of this study is to improve the energy consumption of an AV in a vehicle-following process while safe driving is satisfied. We propose a cascade control system in which an autonomous cruise controller (ACC) is integrated with an energy management system (EMS) to reduce energy consumption. An adaptive model predictive control (AMPC) is proposed as the ACC to control the acceleration of the ego vehicle (the following vehicle) in a vehicle-following scenario, such that it can safely follow the lead vehicle in the same lane on a highway. The proposed ACC appropriately switches between speed and distance control systems to follow the lead vehicle safely and precisely. The computed acceleration is then used in the EMS component to find the optimal engine torque that minimizes the fuel consumption of the ego vehicle. EMS is designed based on two methods: type 1 fuzzy logic system (T1FLS) and interval type 2 fuzzy logic system (IT2FLS). Results show that the combination of AMPC and IT2FLS significantly reduces fuel consumption while the ego vehicle follows the lead vehicle safely and with a minimum spacing error. The proposed controller facilitates smarter energy use in AVs and supports safer transportation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

et al. 2021

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“…A number of researches have been conducted to improve the fuel economy to reduce the fuel usage along with the Internal Combustion Engine (ICE) of conventional vehicles [5,6] and Hybrid Electric Vehicles (HEVs) [7][8][9]. In this study, we focus on improving the energy efficiency of a conventional autonomous vehicle (CAV).…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that fuel consumption was decreased without sacrificing vehicle performance by using the control strategy. Khayyam et al [6] proposed a system for a conventional vehicle in terms of reducing fuel consumption by enhancing the efficiency of the engine. The simulation results showed that fuel usage can be improved by 5.6% with the designed system.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

et al. 2020

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This paper investigates the energy management system (EMS) of a conventional autonomous vehicle, with a view to enhance its powertrain efficiency. The designed EMS includes two neuro-fuzzy (NF) systems to produce the optimal torque of the engine. This control system uses the dynamic road power demand of the autonomous vehicle as an input, and a PID controller to regulate the air mass flow rate into the cylinder by changing the throttle angle. Two NF systems were trained by the Grid Partition (GP) and the Subtractive Clustering (SC) methods. The simulation results show that the proposed EMS can reduce the fuel consumption of the vehicle by 6.69 and 6.35 l/100 km using the SC and the GP, respectively. In addition, the EMS based on NF trained by GP and NF trained by SC can reduce the fuel consumption of the vehicle by 11.8% and 7.08% compared with the case without the controller, respectively.

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An intelligent energy management and traffic predictive model for autonomous vehicle systems

et al. 2021

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A rule-based intelligent energy management system for an internal combustion engine vehicle

Cited by 4 publications

References 8 publications

Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

An intelligent energy management and traffic predictive model for autonomous vehicle systems

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