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In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development. [ABSTRACT FROM AUTHOR]

The field of hybrid vehicles has undergone intensive research and development, primarily due to the increasing concern of depleting resources and increasing pollution. In order to investigate further options to optimize the performance of hybrid vehicles with regards to different criteria, such as fuel economy, battery aging, etc., a detailed state-of-the-art review is presented in this contribution. Different power management and optimization techniques are discussed focusing on rule-based power management and multi-objective optimization techniques. The extent of rule-based power management and optimization in solving battery aging issues is investigated along with an implementation in real-time driving scenarios where no pre-defined drive cycle is followed. The goal of this paper is to illustrate the significance and applications of rule-based power management optimization based on previous contributions. [ABSTRACT FROM AUTHOR]

In recent years, the rapidly-increasing demand for energy generation from renewable resources has been noticeable. Additional requirements are consequently set on Wind Turbine (WT) systems, primarily reflected in WT size and power rating increases. With the size increase of WT, structural loads/fatigue stress on the wind turbine become larger, simultaneously leading to its accelerated aging and the shortening of its lifetime. The primary goal of this contribution is to establish an approach for structural load reduction while retaining or slightly sacrificing the power production requirements. The approach/control strategy includes knowledge about current fatigue damage and/or damage increments and consists of multi-input multi-output controllers with variable control parameters. By the appropriate selection of the designed Multi-Input Multi-Output (MIMO) controllers, the mitigation of structural loads in accordance with a predefined range of accumulated fatigue damage or damage increments, exactly to the extent required to provide a predefined service lifetime, is obtained. The validation of the aforementioned control strategy is based on the simulation results and the WT model developed by National Renewable Energy Laboratory (NREL). The obtained results prove the efficiency of the proposed control strategy with respect to the reduction of rotor blade bending moments, simultaneously exhibiting no significant impact on the resulting power generation. [ABSTRACT FROM AUTHOR]