4,640 research outputs found
Least costly energy management for series hybrid electric vehicles
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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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
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Chapter 2 - Data-Driven Energy Efficient Driving Control in Connected Vehicle Environment
Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio
Policy instruments to promote electro-mobilityiIn the Eu28: A comprehensive review
Despite its environmental benefits, the amount of Electric Vehicles (EVs) in use within the
European Union 28 is still very limited. Poor penetration might be explained by certain factors that
dissuade potential buyers. To balance these factors and promote electro-mobility, Member States
have established incentives to increase demand. However, the various measures are scattered. This
paper contributes to fill the gap in the literature by offering an overall view of the main measures.
The authors will focus on measures to promote electro-mobility within the EU28 until 2014. After an
in-depth and comprehensive review of the relevant measures, the authors conclude that the most
important policy instruments to promote EVs are tax and infrastructure measures in addition to
financial incentives for purchasing and supporting R&D projects. Regardless of the scarcity of EV
registration data, the available information allows us to conclude that higher EV penetration levels
appear in countries where the registration tax, the ownership tax, or both taxes have developed a
partial green tax by including CO2 emissions in the calculation of the final invoice.Junta de Andalucía proyecto SEJ-132Ministerio de Economía y Competitividad de España, Cátedra de Economía de la Energía y del Medio Ambiente (Cátedra de Energía y Economía Ambiental) ECO2014-56399-RUniversidad Autónoma de Chil
Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses
We discuss a recently introduced ECO-driving concept known as SPONGE in the
context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to
illustrate the benefits of this approach to ECO-driving. Finally, distributed
algorithms to realise SPONGE are discussed, paying attention to the privacy
implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on
Automation Science and Engineerin
Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning
The climate-adaptive energy management system holds promising potential for harnessing the concealed energy-saving capabilities of connected plug-in hybrid electric vehicles. This research focuses on exploring the synergistic effects of artificial intelligence control and traffic preview to enhance the performance of the energy management system (EMS). A high-fidelity model of a multi-mode connected PHEV is calibrated using experimental data as a foundation. Subsequently, a model-free multistate deep reinforcement learning (DRL) algorithm is proposed to develop the integrated thermal and energy management (ITEM) system, incorporating features of engine smart warm-up and engine-assisted heating for cold climate conditions. The optimality and adaptability of the proposed system is evaluated through both offline tests and online hardware-in-the-loop tests, encompassing a homologation driving cycle and a real-world driving cycle in China with real-time traffic data. The results demonstrate that ITEM achieves a close to dynamic programming fuel economy performance with a margin of 93.7%, while reducing fuel consumption ranging from 2.2% to 9.6% as ambient temperature decreases from 15°C to -15°C in comparison to state-of-the-art DRL-based EMS solutions
A novel strategy for power sources management in connected plug-in hybrid electric vehicles based on mobile edge computation framework
This paper proposes a novel control framework and the corresponding strategy for power sources management in connected plug-in hybrid electric vehicles (cPHEVs). A mobile edge computation (MEC) based control framework is developed first, evolving the conventional on-board vehicle control unit (VCU) into the hierarchically asynchronous controller that is partly located in cloud. Elaborately contrastive analysis on the performance of processing capacity, communication frequency and communication delay manifests dramatic potential of the proposed framework in sustaining development of the cooperative control strategy for cPHEVs. On the basis of MEC based control framework, a specific cooperative strategy is constructed. The novel strategy accomplishes energy flow management between different power sources with incorporation of the active energy consumption plan and adaptive energy consumption management. The method to generate the reference battery state-of-charge (SOC) trajectories in energy consumption plan stage is emphatically investigated, fast outputting reference trajectories that are tightly close to results by global optimization methods. The estimation of distribution algorithm (EDA) is employed to output reference control policies under the specific terminal conditions assigned via the machine learning based method. Finally, simulation results highlight that the novel strategy attains superior performance in real-time application that is close to the offline global optimization solutions
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