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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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Dissertation

Adaptive Techniques for Estimation and Online Monitoring of Battery Energy Storage Devices

S. Nejad
TL;DR: In this paper, a nonlinear version of the KF technique, namely the Extended Kalman Filter (EKF) is applied throughout this thesis to estimate the battery states including SOC, as well as the battery's impedance parameters.
Dissertation

Multiobjective Optimization of the Power Flow Control of Hybrid Electric Power Train Systems within Simulation and Experimental Emulation Applications

Matthias Marx
TL;DR: In this paper, the power flow control of hybrid electric power train systems is discussed using the focus of multiobjective optimization goals and related algorithms, based on different control optimization methods, are developed and applied within simulation and experimental environments.
Proceedings ArticleDOI

Economic operating characteristics of permanent magnet synchronous motor in electric vehicle

TL;DR: In this article, the PMSM efficiency model was combined with the EV and road load system to study the optimal energy-saving control strategy, which is significant for the economic operation of EV.
Proceedings ArticleDOI

Realization of an energy management strategy for a series-parallel hybrid electric vehicle

TL;DR: In this article, an energy management strategy for a series-parallel hybrid electric vehicle, whose front wheels are driven by engine-ISG hybrid system and rear wheels by two wheel motors respectively, is presented.

Energy management strategy for a hybrid container crane

S. Mulder
TL;DR: The simulation results show that new strategies consistently outperform the current system, significantly improving the fuel savings and therefore increasing the operational profits.
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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