16,190 research outputs found

    Energy Management Strategies for Vehicular Electric Power Systems

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    Time-optimal Control Strategies for Electric Race Cars with Different Transmission Technologies

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    This paper presents models and optimization methods to rapidly compute the achievable lap time of a race car equipped with a battery electric powertrain. Specifically, we first derive a quasi-convex model of the electric powertrain, including the battery, the electric machine, and two transmission technologies: a single-speed fixed gear and a continuously variable transmission (CVT). Second, assuming an expert driver, we formulate the time-optimal control problem for a given driving path and solve it using an iterative convex optimization algorithm. Finally, we showcase our framework by comparing the performance achievable with a single-speed transmission and a CVT on the Le Mans track. Our results show that a CVT can balance its lower efficiency and higher weight with a higher-efficiency and more aggressive motor operation, and significantly outperform a fixed single-gear transmission.Comment: 5 pages, 4 figures, submitted to the 2020 IEEE Vehicle Power and Propulsion Conferenc

    A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

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    © 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

    Exclusive Operation Strategy for the Supervisory Control of Series Hybrid Electric Vehicles

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    Supervisory control systems (SCSs) are used to manage the powertrain of hybrid electric vehicles (HEV). This paper presents a novel SCS called Exclusive operation strategy (XOS) that applies simple rules based on the idea that batteries are efficient at lower loads while engines and generators are efficient at higher loads. The XOS is developed based on insights gained from three conventional SCSs for series HEVs: Thermostat control strategy (TCS), Power follower control strategy (PFCS) and Global equivalent consumption minimization strategy (GECMS). Also, recent technological developments have been considered to make the XOS more suited to modern HEVs than conventional SCSs. The resulting control decisions are shown to emulate the operation of approximate global optimal solutions and thus achieve significant improvement in fuel economy as compared to TCS and PFCS. In addition, the generally linear relationship between required power and engine power for the XOS provides auditory cues to the driver that are comparable to conventional vehicles, thus reducing barriers to adopting HEVs. The simplicity and effectiveness of the XOS makes it a practical SCS

    Least costly energy management for series hybrid electric vehicles

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    Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user - the combined costs of fuel, grid energy and battery degradation - is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign
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