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Nicu Bizon - One of the best experts on this subject based on the ideXlab platform.

  • Optimization Algorithms and Energy Management Strategies
    Optimization of the Fuel Cell Renewable Hybrid Power Systems, 2020
    Co-Authors: Nicu Bizon
    Abstract:

    A brief comparison of the energy management strategies and the optimization algorithms used is performed in this chapter. The load-following (LFW) strategy for a proton exchange membrane fuel cell (PEMFC) is proposed to sustain the power flow balance on the DC bus of a hybrid power system (HPS) with the battery operating in Charge-Sustaining Mode. This battery operating Mode has many advantages (like small size), especially for FC vehicles where space is vital. In addition, the state of charge (SoC) for battery is almost constant, so there is no need for SoC monitoring as in rule-based strategies. The power-following (PFW) strategy for stand-alone FC/renewable HPSs is the variant of the LFW control using as input the difference between the load and available renewable energy instead of the load. The excess of energy during light load stage can supply an electrolyzer or can be sold if FC HPS is connected to the network. Optimization of the FC HPS operation may be performed using well-known optimization algorithms such as the global maximum power point tracking (GMPPT) algorithms, global maximum efficiency point tracking (GMEPT) algorithms, or fuel economy algorithms. An advanced fuel economy strategy using fueling regulators switching is presented and analyzed in this chapter as fuel economy under constant and variable load. A pulse mitigation strategy using an anti-pulse control for the hybrid storage system (HSS) based on battery and ultracapacitors is also proposed and analyzed in this chapter. The design and performance evaluation are presented for both strategies.

  • real time optimization strategies of fuel cell hybrid power systems based on load following control a new strategy and a comparative study of topologies and fuel economy obtained
    Applied Energy, 2019
    Co-Authors: Nicu Bizon
    Abstract:

    An evaluation of currently optimization energy management strategies is done in this study. The load-following – based strategy for Proton Exchange Membrane Fuel Cell (FC) Hybrid Power Systems ensure the DC power flow balance using the FC system as main energy source. Thus, the battery will operate in Charge-Sustaining Mode. So, the battery state-of-charge will vary in imposed window without need of monitoring the battery. Furthermore, the size and maintenance of the battery stack will decrease compared with rule-based strategies. So for the first time, the classification and evaluation of seven FC Hybrid Power Systems and their possible energy management strategies (including a new one) are performed using the performance indicators related to fuel economy and FC electrical efficiency of the FC system. Consequently, the optimization of the FC Hybrid Power Systems under variable load will mix these performance indicators through the weighting coefficients knet and kfuel into a new optimization function. A new switching strategy for the load-following control and real-time optimization loops is proposed to further increase the fuel economy based on the results obtained in this study. The objective of this study is to highlight how optimization function and switching strategy through choosing the weighting coefficients and the control references could improve the fuel economy. Thus, the design of the switching strategy based on available information about the current load demand and the distance to fuel stations is presented in this paper as well. This switching strategy will push hydrogen to become an energy carrier feasible for FC vehicles.

  • Effective mitigation of the load pulses by controlling the battery/SMES hybrid energy storage system
    Applied Energy, 2018
    Co-Authors: Nicu Bizon
    Abstract:

    Abstract In this paper it is analyzed the behavior of a battery/Superconducting Magnetic Energy Storage (SMES) hybrid Energy Storage Systems that can be used in a Fuel Cell/Renewable Energy Sources (RESs)/Hybrid Power System under an unknown load profile and variable RES power, which uses the Fuel Cell System as Auxiliary Energy Source. In general, the load demand profile includes large and sharp pulses, especially requested for space and military equipment, communication, and high-tech applications. The sizing and control of the battery/SMES Hybrid Power System under pulsed load are validated by simulations. The variability of the load demand and RES power is mitigated by using the Load-Following control for Auxiliary Energy Source of the RES Hybrid Power System. Thus, if the load power is higher than the RES power, then the battery will operate in Charge-Sustaining Mode due to using the Load-Following control for Auxiliary Energy Source. Otherwise, the battery will operate in charge-increasing Mode if the Hybrid Power System does not use an electrolyzer to be supplied with this excess of power. So, a reduced capacity is needed for battery operating in Charge-Sustaining Mode due to use of the Load-Following control. However, the load pulses with large and sharp profile must be mitigated by the appropriate control of the SMES in order to protect the Fuel Cell system. So, the capacity of the SMES to generate (or to absorb) such pulses is analyzed in this paper. The simulation results illustrate the capacity of the SMES to generate different shapes of pulses. Thus, an effective mitigation of the load pulses is proposed here by controlling the SMES converter. Also, the design of the battery/SMES Hybrid Power System under dynamic load is presented.

  • Real-time optimization strategy for fuel cell hybrid power sources with load-following control of the fuel or air flow
    Energy Conversion and Management, 2018
    Co-Authors: Nicu Bizon
    Abstract:

    Abstract This paper analyses two Real-Time Optimization (RTO) strategies for Proton Exchange Membrane Fuel Cell (PEMFC) system which is used as main energy source for Fuel Cell Hybrid Power Source (FCHPS) of the FC vehicle (FCV). In this study the optimization function was defined as mix of the FC net power and the Fuel Consumption Efficiency by using two weighting coefficients. The Global Extremum Seeking (GES) algorithm is proposed here as RTO method for multimodal optimization surfaces having many peaks on the plateau around the optimal point that is the Global Maximum Point (GMP). One of the fueling rates is Load-Following (LF) controlled in order to adapt the FC net power to load demand and assure the Charge-Sustaining Mode for the battery. The GES algorithm will establish the optimal duty cycle for the Boost converter, so the proposed strategies will be called the Boost-GES-RTO strategies with Air-LF and Fuel-LF, respectively. The Static Feed-Forward (sFF) control strategy will be used as reference for constant and variable load profile. The gaps in performance indicators were estimated for both Boost-GES-RTO strategies. For example, the gaps in FC system efficiency and fuel economy could be up to 1.61% and 142 lpm, and 2.65 and 114 lpm for the Boost-GES-RTO strategies with Air-LF and Fuel-LF. The performance of Boost-GES-RTO strategies was also shown by estimating the fuel economy for 6 kW FCHPS under variable load profile.

  • Energy control strategies for the Fuel Cell Hybrid Power Source under unknown load profile
    Energy, 2015
    Co-Authors: Nicu Bizon, Marin Radut, Mihai Oproescu
    Abstract:

    Four new energy control strategies are proposed here for the Fuel Cell Hybrid Power Source (FCHPS) used in stationary and mobile FC application (such as the FC backup source for a smart-house and FC vehicle, respectively) based on the Load Following (LF) control and Maximum Efficiency Point Tracking (MEPT) control of the fueling rates. The LF control approach is used to design simple strategies of the Energy Management Unit (EMU) that will assure a Charge-Sustaining Mode for the batteries stack of the Energy Storage System (ESS). If a fueling rate is controlled based on the LF strategy, then the other is controlled based on MEPT strategy in order to maximize the FC net power available. The advantages of the proposed EMU strategies during an unknown load cycle are comparatively shown.

Bin Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Catch Energy Saving Opportunity in Charge-Depletion Mode, a Real-Time Controller for Plug-In Hybrid Electric Vehicles
    IEEE Transactions on Vehicular Technology, 2018
    Co-Authors: Amir Rezaei, Jeffrey B. Burl, Mohammad Rezaei, Bin Zhou
    Abstract:

    The energy management of plug-in hybrid electric vehicles (HEVs) is commonly divided into two Modes: charge-depletion Mode and Charge-Sustaining Mode. This paper presents the optimal adaption law for any type of adaptive energy consumption minimization strategy (ECMS) in charge-depletion Mode for plug-in HEVs. To present the optimal law, a particular adaptive ECMS is selected, known as catch energy saving opportunity (CESO). CESO has previously been introduced for series and parallel HEVs in Charge-Sustaining Mode. Here, by introducing the optimal adaption law, CESO strategy is expanded to charge-depletion Mode for plug-in HEVs.

  • Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles
    Applied Energy, 2017
    Co-Authors: Amir Rezaei, Jeffrey B. Burl, Mohammad Rezaei, Bin Zhou, Ali Solouk, Shahbakhti
    Abstract:

    Abstract This paper introduces a new energy management (EM) strategy for series hybrid electric vehicles (HEVs). Series HEVs operate in charge-depletion Mode and then switch to the Charge-Sustaining Mode in which the battery state of charge (SOC) is maintained within a certain range. The proposed EM strategy in this paper is a form of adaptive equivalent consumption minimization strategy (ECMS) that is designed for the Charge-Sustaining Mode. The EM strategy defines soft bounds on the battery SOC and is penalized for exceeding these bounds. But, to catch energy-saving opportunities (CESOs), the EM strategy allows SOC to exceed the soft bounds. Thus, the introduced EM strategy is named ECMS-CESO. In addition, a range for the ECMS optimal equivalent factor is proposed for series HEVs. The proposed range is used in deriving the formula for calculating the adaptive equivalent factor. The main advantage of the proposed EM strategy is that ECMS-CESO can achieve close to optimal fuel economy without the need for predicting future driver demand. Since there is no need for prediction, the intensive calculations for finding the optimal control over the prediction horizon can be eliminated. Therefore, implementation of ECMS-CESO is easily feasible for real-time applications. Experimental powertrain data is collected to develop a powertrain Model for a series HEV in this study. Simulation results on several drivecycles show that, on average, the fuel economy achieved by ECMS-CESO is within 6% of the maximum fuel economy. In addition, comparing ECMS-CESO with two existing adaptive ECMSs shows up to 5% improvement in fuel economy, on average.

Amir Rezaei - One of the best experts on this subject based on the ideXlab platform.

  • Catch Energy Saving Opportunity in Charge-Depletion Mode, a Real-Time Controller for Plug-In Hybrid Electric Vehicles
    IEEE Transactions on Vehicular Technology, 2018
    Co-Authors: Amir Rezaei, Jeffrey B. Burl, Mohammad Rezaei, Bin Zhou
    Abstract:

    The energy management of plug-in hybrid electric vehicles (HEVs) is commonly divided into two Modes: charge-depletion Mode and Charge-Sustaining Mode. This paper presents the optimal adaption law for any type of adaptive energy consumption minimization strategy (ECMS) in charge-depletion Mode for plug-in HEVs. To present the optimal law, a particular adaptive ECMS is selected, known as catch energy saving opportunity (CESO). CESO has previously been introduced for series and parallel HEVs in Charge-Sustaining Mode. Here, by introducing the optimal adaption law, CESO strategy is expanded to charge-depletion Mode for plug-in HEVs.

  • Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles
    Applied Energy, 2017
    Co-Authors: Amir Rezaei, Jeffrey B. Burl, Mohammad Rezaei, Bin Zhou, Ali Solouk, Shahbakhti
    Abstract:

    Abstract This paper introduces a new energy management (EM) strategy for series hybrid electric vehicles (HEVs). Series HEVs operate in charge-depletion Mode and then switch to the Charge-Sustaining Mode in which the battery state of charge (SOC) is maintained within a certain range. The proposed EM strategy in this paper is a form of adaptive equivalent consumption minimization strategy (ECMS) that is designed for the Charge-Sustaining Mode. The EM strategy defines soft bounds on the battery SOC and is penalized for exceeding these bounds. But, to catch energy-saving opportunities (CESOs), the EM strategy allows SOC to exceed the soft bounds. Thus, the introduced EM strategy is named ECMS-CESO. In addition, a range for the ECMS optimal equivalent factor is proposed for series HEVs. The proposed range is used in deriving the formula for calculating the adaptive equivalent factor. The main advantage of the proposed EM strategy is that ECMS-CESO can achieve close to optimal fuel economy without the need for predicting future driver demand. Since there is no need for prediction, the intensive calculations for finding the optimal control over the prediction horizon can be eliminated. Therefore, implementation of ECMS-CESO is easily feasible for real-time applications. Experimental powertrain data is collected to develop a powertrain Model for a series HEV in this study. Simulation results on several drivecycles show that, on average, the fuel economy achieved by ECMS-CESO is within 6% of the maximum fuel economy. In addition, comparing ECMS-CESO with two existing adaptive ECMSs shows up to 5% improvement in fuel economy, on average.

Frédéric Gustin - One of the best experts on this subject based on the ideXlab platform.

  • Real-Time Control Based on a CAN-Bus of Hybrid Electrical Systems
    Energies, 2020
    Co-Authors: Kréhi Serge Agbli, Mickael Hilairet, Frédéric Gustin
    Abstract:

    Power management of a one-converter parallel structure with battery and supercapacitor is addressed in this paper. The controller is implemented on a DSP from a Microchip and uses a Controller Area Network (CAN) bus communication for data exchange. However, the low data transmission rate of the CAN bus data impacts the performances of regular power management strategies. This paper details an initial strategy with a charge sustaining Mode for an application coupling a battery with supercapacitors, in which low performances have been witnessed due to the high sampling time of the CAN bus data. Therefore, a new strategy is proposed to tackle the sample time issue based on a depleting Mode. Simulation and experimental results with a dsPIC33EP512MU810 DSP based on a 10 kW hybrid system proves the feasibility of the proposed approach.

Jiangfeng Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Optimal State-of-Charge Value for Charge-Sustaining Mode of Plug-In Hybrid Electric Vehicles
    IEEE Access, 2020
    Co-Authors: Shenrun Zhang, Jiangfeng Zhang
    Abstract:

    For plug-in hybrid electric vehicles or extended range electric vehicles, battery stored energy often cannot fully meet the travel needs, and the battery needs to stay in a Charge-Sustaining Mode to allow for backup sources such as gasoline or diesel to power the vehicle. It is crucial to identify the optimal state-of-charge (SOC) value for the battery to maintain this Charge-Sustaining Mode since this SOC has significant impact to battery degradation. In existing studies, Just-in-Time control proposes that this SOC should be maintained at 55% but without theoretical justification. With the help of a battery degradation Model based on solid electrolyte interphase growth, this article develops a method to decide the optimal SOC value for Charge-Sustaining Mode. Following the principle of superposition, degradation during the battery discharging process is divided as a fixed degradation caused by the drop of SOC from maximum to minimum values during charge-depleting Mode, and a dynamic degradation caused by the oscillation at the Charge-Sustaining SOC value. Then this oscillation-caused degradation is further Modeled and minimized through the investigation of the side reaction current density. The optimal SOC value obtained will be the SOC at which the side reaction current density has the slowest changing rate. This SOC value indeed relies on battery parameters, charging/discharging current and ambient temperature, and a case study shows that the best range of SOC value is 36%~38% for a 1.8Ah SONY 18650 cell. An average SOC of 37% is therefore recommended for Charge-Sustaining Mode considering the possible errors in SOC estimation.