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Energy management strategies for vehicular electric power systems

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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.

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Citations
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Journal ArticleDOI

Research on the Optimal Power Management Strategy for a Hybrid Electric Bus

TL;DR: A dynamic programming algorithm based on a powertrain system to find the optimal solution in the specific speed cycle but this method cannot be applied to a real-time control system, so the optimal control rules and thresholds are extracted for the rule-based strategy.
Journal ArticleDOI

Dynamic Programming-Based Energy Management System for Range-Extended Electric Bus

TL;DR: In this paper, an optimal control strategy is designed according to the SOC consumption trend, which is optimized by the DP algorithm, and the energy efficiency of the powertrain system and components are analyzed by the energy flow diagram method.
Journal ArticleDOI

Projected Gradient and Model Predictive Control : Optimal Energy and Pollutants Management for Hybrid Electric Vehicle

TL;DR: A real-time compatible strategy for the energy management of a compression-ignition Hybrid Electric Vehicle (HEV), under pollutants emissions constraints is proposed and an online-oriented approach based on Model Predictive Control (MPC) is described.
Journal ArticleDOI

Optimal energy management for a flywheel-assisted battery electric vehicle

TL;DR: In this paper, the optimal energy management strategy for a mechanically connected flywheel-assisted battery electric vehicle powertrain is presented, which shows significant potential for reduction in the energy consumption in extraurban and highway cycles, while reducing the peak battery loads during all cycles.
Dissertation

Control and design of pem fuel cell-based systems

Diego Feroldi
TL;DR: In this paper, an extenso estudio sobre el control and disno de sistemas de generacion electrica basados en pilas de combustible is presented, with the goal of satisfacer requisitos de conductibilidad and consumiendo la menor cantidad de hidrogeno posible.
References
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Book

Dynamic Programming and Optimal Control

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.
Book

Practical Methods of Optimization

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.
Book

Predictive Control With Constraints

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.
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