Energy management strategies for vehicular electric power systems
Mwt Michiel Koot,J.T.B.A. Kessels,B. de Jager,Wpmh Maurice Heemels,P.P.J. van den Bosch,M Maarten Steinbuch +5 more
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TLDR
An extensive study on controlling the vehicular electric power system to reduce the fuel use and emissions, by generating and storing electrical energy only at the most suitable moments is presented.Abstract:
In the near future, a significant increase in electric power consumption in vehicles is expected. To limit the associated increase in fuel consumption and exhaust emissions, smart strategies for the generation, storage/retrieval, distribution, and consumption of electric power will be used. Inspired by the research on energy management for hybrid electric vehicles (HEVs), this paper presents an extensive study on controlling the vehicular electric power system to reduce the fuel use and emissions, by generating and storing electrical energy only at the most suitable moments. For this purpose, both off-line optimization methods using knowledge of the driving pattern and on-line implementable ones are developed and tested in a simulation environment. Results show a reduction in fuel use of 2%, even without a prediction of the driving cycle being used. Simultaneously, even larger reductions of the emissions are obtained. The strategies can also be applied to a mild HEV with an integrated starter alternator (ISA), without modifications, or to other types of HEVs with slight changes in the formulation.read more
Citations
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Journal ArticleDOI
From Optimal to Real-Time Control of a Mechanical Hybrid Powertrain
TL;DR: The design of an energy controller for a mechanical hybrid powertrain, which is suitable for implementation in real-time hardware, and is transparent, causal, and robust, where the latter is shown by simulations for various driving cycles and start conditions.
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Intelligent Vehicle Power Control Based on Prediction of Road Type and Traffic Congestions
Jungme Park,Zhihang Chen,Leonidas Kiliaris,Yi Lu Murphey,Ming Kuang,Anthony Mark Phillips,M.A. Masrur +6 more
TL;DR: A machine learning approach to the efficient vehicle power management and an intelligent power controller (IPC) that applies the learnt knowledge about the optimal power control parameters specific to specific road types and traffic congestion levels to online vehicle power control.
Proceedings ArticleDOI
Energy Management for Hybrid Electric Tractors Combining Load Point Shifting, Regeneration and Boost
TL;DR: In this paper, an energy management for hybrid electric vehicles combining load point shifting based on optimization with regeneration and boost based on heuristics is introduced and the real-time implementation on a hybrid electric tractor is discussed.
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Power flow control strategies in parallel hybrid electric vehicles
TL;DR: In this article, two control strategies for power flow control in hybrid electric vehicles (HEVs) with parallel configuration and a planetary gear system as a power coupling device between the internal combustion engine and the electric machine are proposed.
Proceedings ArticleDOI
Optimal Adaptive Solution to Powersplit Problem in Vehicles with Integrated Starter/Generator
TL;DR: In this paper, the authors show that typical vehicle characteristics determine the general behavior of the energy management algorithm, and an optimal strategy exists without predicting the future vehicle speed, which is directly suitable for on-line vehicle implementation.
References
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TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
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TL;DR: The aim of this book is to provide a Discussion of Constrained Optimization and its Applications to Linear Programming and Other Optimization Problems.
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TL;DR: A standard formulation of Predictive Control is presented, with examples of step response and transfer function formulations, and a case study of robust predictive control in the context of MATLAB.