scholarly journals Multi-Objective Energy Management Strategy for PV/FC Hybrid Power Systems

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1721
Author(s):  
Nicu Bizon ◽  
Phatiphat Thounthong
Keyword(s):  
Power Systems ◽  
Fuel Economy ◽  
Fuel Efficiency ◽  
Power Converter ◽  
Energy Management Strategy ◽  
Load Demand ◽  
Optimization Function ◽  
Dc Bus ◽  
Will Power ◽  
Excess Power

In this paper, a new control of the DC–DC power converter that interfaces the fuel cell (FC) system with the DC bus of the photovoltaic (PV) power system is proposed to increase the battery lifespan by its operating in charge-sustained mode. Thus, the variability of the PV power and the load demand is compensated by the FC power generated considering the power flows balance on the DC bus. During peak PV power, if the PV power exceeds the load demand, then the excess power on the DC bus will power an electrolyzer. The FC system operation as a backup energy source is optimized using a new fuel economy strategy proposed for fueling regulators. The fuel optimization function considers the fuel efficiency and electrical efficiency of the FC system to maximize fuel economy. The fuel economy obtained in the scenarios considered in this study is compared with reference strategies reported in the literature. For example, under scenarios considered in this paper, the fuel economy is between 4.82–20.71% and 1.64–3.34% compared to a commercial strategy based on static feed-forward (sFF) control and an advanced strategy recently proposed in the literature, respectively.

scholarly journals Improving the Fuel Economy and Battery Lifespan in Fuel Cell/Renewable Hybrid Power Systems Using the Power-Following Control of the Fueling Regulators

2020 ◽  
Vol 10 (22) ◽  
pp. 8310
Author(s):  
Nicu Bizon ◽  
Mihai Oproescu ◽  
Phatiphat Thounthong ◽  
Mihai Varlam ◽  
Elena Carcadea ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Power Systems ◽  
Fuel Economy ◽  
Storage System ◽  
Energy Storage System ◽  
Power Converter ◽  
Hybrid Power Systems ◽  
Safe Operation ◽  
Hybrid Power ◽  
Load Demand

In this study, the performance and safe operation of the fuel cell (FC) system and battery-based energy storage system (ESS) included in an FC/ESS/renewable hybrid power system (HPS) is fully analyzed under dynamic load and variable power from renewable sources. Power-following control (PFC) is used for either the air regulator or the fuel regulator of the FC system, or it is switched to the inputs of the air and hydrogen regulators based on a threshold of load demand; these strategies are referred to as air-PFC, fuel-PFC, and air/fuel-PFC, respectively. The performance and safe operation of the FC system and battery-based ESS under these strategies is compared to the static feed-forward (sFF) control used by most commercial strategies implemented in FC systems, FC/renewable HPSs, and FC vehicles. This study highlights the benefits of using a PFC-based strategy to establish FC-system fueling flows, in addition to an optimal control of the boost power converter to maximize fuel economy. For example, the fuel economy for a 6 kW FC system using the air/fuel-PFC strategy compared to the strategies air-PFC, fuel-PFC, and the sFF benchmark is 6.60%, 7.53%, and 12.60% of the total hydrogen consumed by these strategies under a load profile of up and down the stairs using 1 kW/2 s per step. For an FC/ESS/renewable system, the fuel economy of an air/fuel-PFC strategy compared to same strategies is 7.28%, 8.23%, and 13.43%, which is better by about 0.7% because an FC system operates at lower power due to the renewable energy available in this case study.

scholarly journals Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1889 ◽  
Author(s):  
Nicu Bizon ◽  
Valentin Alexandru Stan ◽  
Angel Ciprian Cormos
Keyword(s):  
Energy Source ◽  
Fuel Cell ◽  
Power System ◽  
Control Mode ◽  
Variable Load ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Load Power ◽  
Load Demand ◽  
Dc Bus

In this paper, a systematic analysis of seven control topologies is performed, based on three possible control variables of the power generated by the Fuel Cell (FC) system: the reference input of the controller for the FC boost converter, and the two reference inputs used by the air regulator and the fuel regulator. The FC system will generate power based on the Required-Power-Following (RPF) control mode in order to ensure the load demand, operating as the main energy source in an FC hybrid power system. The FC system will operate as a backup energy source in an FC renewable Hybrid Power System (by ensuring the lack of power on the DC bus, which is given by the load power minus the renewable power). Thus, power requested from the batteries’ stack will be almost zero during operation of the FC hybrid power system based on RPF-control mode. If the FC hybrid power system operates with a variable load demand, then the lack or excess of power on the DC bus will be dynamically ensured by the hybrid battery/ultracapacitor energy storage system for a safe transition of the FC system under the RPF-control mode. The RPF-control mode will ensure a fair comparison of the seven control topologies based on the same optimization function to improve the fuel savings. The main objective of this paper is to compare the fuel economy obtained by using each strategy under different load cycles in order to identify which is the best strategy operating across entire loading or the best switching strategy using two strategies: one strategy for high load and the other on the rest of the load range. Based on the preliminary results, the fuel consumption using these best strategies can be reduced by more than 15%, compared to commercial strategies.

scholarly journals A Novel Extension Decision-Making Method for Selecting Solar Power Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Meng-Hui Wang
Keyword(s):  
Decision Making ◽  
Solar System ◽  
Power Systems ◽  
Power System ◽  
Solar Power ◽  
Important Task ◽  
Extension Theory ◽  
Solar Systems ◽  
Load Demand ◽  
Generating Capacity

Due to the complex parameters of a solar power system, the designer not only must think about the load demand but also needs to consider the price, weight, and annual power generating capacity (APGC) and maximum power of the solar system. It is an important task to find the optimal solar power system with many parameters. Therefore, this paper presents a novel decision-making method based on the extension theory; we call it extension decision-making method (EDMM). Using the EDMM can make it quick to select the optimal solar power system. The paper proposed this method not only to provide a useful estimated tool for the solar system engineers but also to supply the important reference with the installation of solar systems to the consumer.

scholarly journals Adaptive Solar Power Forecasting based on Machine Learning Methods

2018 ◽  
Vol 8 (11) ◽  
pp. 2224 ◽  
Author(s):  
Yu Wang ◽  
Hualei Zou ◽  
Xin Chen ◽  
Fanghua Zhang ◽  
Jie Chen
Keyword(s):  
Power Systems ◽  
Solar Power ◽  
Machine Learning Methods ◽  
Load Demand ◽  
Forecasting Method ◽  
Combining Data ◽  
Data Updating ◽  
Solar Power Forecasting ◽  
Forecasting Errors ◽  
Power Forecasting

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

scholarly journals Reduction of Aerodynamic Drag of Heavy Vehicles using CFD

2021 ◽  
Vol 9 (8) ◽  
pp. 2670-2678
Author(s):  
Priyank Kothari
Keyword(s):  
Fuel Economy ◽  
Aerodynamic Drag ◽  
Fuel Efficiency ◽  
Heavy Vehicles ◽  
Heavy Trucks ◽  
Air Resistance

Abstract: Aerodynamic drag is the force that opposes an object’s motion. When a vehicle no matter the size, is designed to allow air to move fluidly over its body, aerodynamic drag will have less of an impact on its performance and fuel economy. Heavy trucks burn a significant amount of fuel as to overcome the air resistance. More than 50% of an 18-wheeler’s fuel is spent reducing aerodynamic drag on the highways. Keywords: Aerodynamics, Heavy vehicles, ANSYS, Aerodynamic Drag, Fuel efficiency.

Optimal adaptation equivalent factor of energy management strategy for plug-in CVT HEV

2018 ◽  
Vol 233 (4) ◽  
pp. 877-889 ◽  
Author(s):  
Xinyou Lin ◽  
Qigao Feng ◽  
Liping Mo ◽  
Hailin Li
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Adaptation Strategy ◽  
Management Control ◽  
Energy Management Strategy ◽  
Driving Mode ◽  
Driving Cycles ◽  
Equivalent Factor

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

Parametric Modeling and Optimal Control of a Combined Heating and Power System With Energy Storage

2020 ◽  
Author(s):  
Claudia Lucia De Pascalis ◽  
Stephanie Stockar
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
Power System ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Cost Effective ◽  
Parametric Modeling ◽  
Medium Size ◽  
Energy Management Strategy ◽  
Generation Cost

Abstract Cogeneration is a well-known and cost effective solution for generating power and heat within the same plant, leading to improved overall efficiency and reduced generation cost. Combined heating and power systems can facilitate the penetration of renewable energy sources in medium size applications through the integration of electric and thermal energy storage units. Due to the complexity of the plant as well as significantly variability in power demand and generation, the design and operation of such systems requires a systematic co-optimization of plant and controller for guaranteeing near optimal performance. In this scenario, this paper presents a physics-based parametric modeling approach for the characterization of the main components of a 1MW combined heating and power system that includes renewable sources, electric and thermal storage devices. To demonstrate the model flexibility and potential benefits achieved by an optimal sizing, the system energy management is optimized using Dynamic Programming. The operational costs for different configurations are compared showing that an optimization of the energy management strategy in conjunction with an improved system sizing lead to more than 6% of reduction in the operational cost.

Optimal energy management strategy for fuel cell/battery/supercapacitor vehicles using wavelet transform and equivalent consumption minimization strategy

2021 ◽  
pp. 095440702110697
Author(s):  
Pengfei Zou ◽  
Fazhan Tao ◽  
Zhumu Fu ◽  
Pengju Si ◽  
Chao Ma
Keyword(s):  
Wavelet Transform ◽  
Fuel Cell ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Low Frequency ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Optimal Energy ◽  
Optimal Energy Management

In this paper, the hybrid electric vehicle is equipped with fuel cell/battery/supercapacitor as the research object, the optimal energy management strategy (EMS) is proposed by combining wavelet transform (WT) method and equivalent consumption minimization strategy (ECMS) for reducing hydrogen consumption and prolonging the lifespan of power sources. Firstly, the WT method is employed to separate power demand of vehicles into high-frequency part supplied by supercapacitor and low-frequency part allocated to fuel cell and battery, which can effectively reduce the fluctuation of fuel cell and battery to prolong their lifespan. Then, considering the low-frequency power, the optimal SOC of battery is used to design the equivalent factor of the ECMS method to improve the fuel economy. The proposed hierarchical EMS can realize a trade-off between the lifespan of power sources and fuel economy of vehicles. Finally, the effectiveness of the proposed EMS is verified by ADVISOR, and comparison results are given compared with the traditional ECMS method and ECMS combining the filter.

scholarly journals Experimental Analysis of Calculation of Fuel Consumption Rate by On-Road Mileage in a 2.0 L Gasoline-Fueled Passenger Vehicle

2018 ◽  
Vol 8 (12) ◽  
pp. 2390 ◽  
Author(s):  
Jaehyuk Lim ◽  
Yumin Lee ◽  
Kiho Kim ◽  
Jinwook Lee
Keyword(s):  
Fuel Consumption ◽  
Fuel Economy ◽  
Fuel Efficiency ◽  
Co2 Concentration ◽  
The United States ◽  
Traffic Conditions ◽  
Fuel Consumption Rate ◽  
Driving Cycles ◽  
The Difference ◽  
Consumption Value

The five-driving test mode is vehicle driving cycles made by the Environment Protection Association (EPA) in the United States of America (U.S.A.) to fully reflect actual driving environments. Recently, fuel consumption value calculated from the adjusted fuel consumption formula has been more effective in reducing the difference from that experienced in real-world driving conditions, than the official fuel efficiency equation used in the past that only considered the driving environment included in FTP and HWFET cycles. There are many factors that bring about divergence between official fuel consumption and that experienced by drivers, such as driving pattern behavior, accumulated mileage, driving environment, and traffic conditions. In this study, we focused on the factor of causing change of fuel efficiency value, calculated according to how many environmental conditions that appear on the real-road are considered, in producing the fuel consumption formula, and that of the vehicle’s accumulated mileage in a 2.0 L gasoline-fueled vehicle. So, the goals of this research are divided into four major areas to investigate divergence in fuel efficiency obtained from different equations, and what factors and how much CO2 and CO emissions that are closely correlated to fuel efficiency change, depending on the cumulative mileage of the vehicle. First, the fuel consumption value calculated from the non-adjusted formula, was compared with that calculated from the corrected fuel consumption formula. Also, how much CO2 concentration levels change as measured during each of the three driving cycles was analyzed as the vehicle ages. In addition, since the US06 driving cycle is divided into city mode and highway mode, how much CO2 and CO production levels change as the engine ages during acceleration periods in each mode was investigated. Finally, the empirical formula was constructed using fuel economy values obtained when the test vehicle reached 6500 km, 15,000 km, and 30,000 km cumulative mileage, to predict how much fuel consumption of city and highway would worsen, when mileage of the vehicle is increased further. When cumulative mileage values set in this study were reached, experiments were performed by placing the vehicle on a chassis dynamometer, in compliance with the carbon balance method. A key result of this study is that fuel economy is affected by various fuel consumption formula, as well as by aging of the engine. In particular, with aging aspects, the effect of an aging engine on fuel efficiency is insignificant, depending on the load and driving situation.

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