Designing for Control Strategy by Particle Swarm Optimization in Parallel Hybrid Electric Vehicles for Economical Fuel Consumption

Teja, Mavuri S. M. Ravi; Varma, P. Srinivasa; Irfan, Mujahid; Sudarshan, E. C. G.

doi:10.1088/1757-899x/981/4/042034

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…In response to these challenges, this paper introduces a comprehensive fleet management system specifically designed for HEVs. The proposed system emphasizes optimal route planning using MATLAB, aiming to enhance operational efficiency and reduce power costs [14].…”

Section: Introductionmentioning

confidence: 99%

A centralized fleet management system for electrified transportation

Lashin,

Ali,

Aiad

et al. 2024

J. Phys.: Conf. Ser.

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Hybrid Electric Vehicles are a promising alternative to Conventional and Electric Vehicles, as they offer better fuel economy, lower emissions, and long drive range without requiring extensive charging infrastructure. Limited driving range and battery degradation in electric vehicles are the main challenges in electrified fleets working in large metropolitan. For these challenges, the hybrid electric vehicle fleet management system is an effective solution to optimize power consumption and mitigate electric vehicle and conventional vehicle challenges. This paper proposes a hybrid electric vehicle fleet management system for public transportation, to analyze and investigate the performance and energy consumption rate of conventional, electric, and hybrid electric vehicles on different routes using representative driving cycles. The hybrid electric vehicle fleet management system enhances operational efficiency for the fleet management system and reduces power consumption in transportation. The proposed work comprises a hybrid electric vehicle model that is based on object-oriented programming. The proposed work offers the ability to add the input of the initial state of charge, power split ratio, and route data for each bus, and the hybrid electric vehicle model computes the state of charge and fuel consumption rate along the road. Finally, The proposed work shows how a hybrid electric vehicle fleet management system has a significant potential to be a solution for public transportation in Egyptian urban areas at this time compared with conventional vehicle and electric vehicle fleet management systems.

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Section: Introductionmentioning

confidence: 99%

A centralized fleet management system for electrified transportation

Lashin,

Ali,

Aiad

et al. 2024

J. Phys.: Conf. Ser.

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“…M.S. Teja et al [16] used the particle swarm optimization algorithm to optimize the fuel consumption, power output, and energy flow of hybrid electric vehicles. A comparison of guidelines proved that the method rapidly completed the design and reduced fuel consumption and vehicle emissions.…”

Section: Introductionmentioning

confidence: 99%

Equivalent Consumption Minimization Strategy of Hybrid Electric Vehicle Integrated with Driving Cycle Prediction Method

Yao

Zheng

et al. 2023

Machines

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Hybrid electric vehicles that can combine the advantages of traditional and new energy vehicles have become the optimal choice at present in the face of increasingly stringent fuel consumption restrictions and emission regulations. Range-extended hybrid electric vehicles have become an important research topic because of their high energy mixing degree and simple transmission system. A compact traditional fuel vehicle is the research object of this study and the range-extended hybrid system is developed. The design and optimization of the condition prediction energy management strategy are investigated. Vehicle joint simulation analysis and bench test platforms were built to verify the proposed control strategy. The vehicle tracking method was selected to collect real vehicle driving data. The number of vehicles in the field of view and the estimation of the distances between the front and following vehicles are calculated by means of the mature algorithm of the monocular camera and by computer vision. Real vehicle cycle conditions with driving environment and slope information were constructed and compared with all driving data, typical working conditions under NEDC, and typical working conditions under UDDS. The BP neural network and fuzzy logic control were used to identify the road conditions and the driver’s intention. The results showed that the equivalent fuel consumption of the control strategy was lower than that of the fixed-point power following control strategy and vehicle economy improved.

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