Mwt Michiel Koot

Bio: Mwt Michiel Koot is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Energy management & Electric power. The author has an hindex of 5, co-authored 11 publications receiving 552 citations.

Energy management strategies for vehicular electric power systems

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.

Energy Management for the Electric Powernet in Vehicles With a Conventional Drivetrain

Abstract: The electric power demand in road vehicles increases rapidly. Energy management (EM) turns out to be a viable solution for supplying all electric loads efficiently. The EM strategies developed in this paper focus on vehicles with a conventional drivetrain. By exploiting the storage capacity of the battery, the production, and distribution of electric power is rescheduled to more economic moments. In addition, this paper explores the advantages of electric loads with a flexible power demand. Based on optimization techniques, an optimal offline strategy as well as a causal online strategy are presented. Simulations illustrate the benefits of the EM strategies in terms of fuel economy. The online strategy has also been implemented in a series-production vehicle. Real-world experiments on a roller dynamometer test-bench validate the strategy, but also reveal additional fuel benefits due to unexpected side-effects from the engine control unit and the driver. Measured profits in fuel economy are as large as 2.6%, with only minimal changes to the vehicle hardware

Fuel reduction potential of energy management for vehicular electric power systems

Abstract: In the near future a significant increase in electric power consumption in vehicles is to be expected. To limit the associated increase in fuel consumption and exhaust emissions, smart strategies for the generation, storage/retrieval, distribution and consumption of the electric power can be used. This paper considers a vehicle configuration with a conventional drive train. Two energy management strategies that control the alternator power are analysed: a regenerative braking strategy and a more advanced strategy based on optimisation techniques. The potential behind these strategies is analysed by studying the typical characteristics of components that are directly related to the energy flow in the vehicle. It is shown that operating the internal combustion engine at the highest efficiency will not inherently lead to the lowest fuel consumption. Subsequently, engineering rules are presented to evaluate the performance that can be expected for each strategy. The component characteristics are included as input parameters to make the method generally applicable. To show the value of the engineering rules, the potential fuel reduction is computed for a specific vehicle configuration and driving cycle and compared with simulations results.

Energy management strategies for vehicle power nets

Abstract: In the near future a significant increase in electric power consumption in vehicles is to be expected. To limit the associated increase in fuel consumption and exhaust emissions, smart strategies for the generation, storage/retrieval, distribution, and consumption of the electric power can be used. This paper presents a case study on controlling the vehicle power net using knowledge of the driving pattern to minimize fuel use, by generating and storing extra energy only at the most suitable moments. For this purpose, both off-line and online optimization methods are developed and tested in a simulation environment. Results show a reduction in fuel use, even without an accurate prediction of the drive cycle.

Progress in electrical energy storage system: A critical review

Abstract: Electrical energy storage technologies for stationary applications are reviewed. Particular attention is paid to pumped hydroelectric storage, compressed air energy storage, battery, flow battery, fuel cell, solar fuel, superconducting magnetic energy storage, flywheel, capacitor/supercapacitor, and thermal energy storage. Comparison is made among these technologies in terms of technical characteristics, applications and deployment status.

Control of hybrid electric vehicles

Abstract: Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort

Optimal Power Flow Management for Grid Connected PV Systems With Batteries

Abstract: This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

Abstract: Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

Abstract: This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode power-split PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.