Improved fuzzy logic control-based energy management strategy for hybrid power system of FC/PV/battery/SC on tourist ship

An improved fuzzy-based energy management strategy (EMS) is proposed for a tourist ship used hybrid power system with multiple power sources consisting of fuel cell(FC)/photovoltaic cell(PV)/battery(BAT)/super-capacitor(SC). The power demand from propeller and user terminal is afforded by the power sources connecting to power converters. To obtain more superior performance of the power system, the maximum power point tracking (MPPT) algorithm is employed to optimize the PV. Meanwhile, the improved fuzzy logic control based on dynamic programming (DP) associated with wavelet analysis and PI control are employed to achieve the output power optimal distribution and online control. In particular, the MPPT algorithm can improve the utilization of solar energy, and the SC can well absorb the high frequency power and reduce the fluctuation of the battery and FC that exhibits the potential of their lifetime extension. The FC outputs the high and stable power satisfying the ship's power demand even under the extreme work conditions. The developed model is able to illustrate well in the operation process of the hybrid power system governed by the proposed EMS. In addition, compared with the rule-based strategy, the improved fuzzy-based EMS can reduce 14.39% hydrogen consumption and keep the consistency of battery SOC.     

A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.     

Implementation of self-adaptive Harris Hawks Optimization-based energy management scheme of fuel cell-based electric power system

This paper presents a hybrid Fuel Cell-based Power System (FCPS) consisting of fuel cell and hybrid Energy Storage Systems (ESSs), including a battery with high energy density and supercapacitor with high power density to overcome the sudden load demand change and improving the reliability of the delivered power. Any hybrid power system needs Energy Management Strategies (EMS) to balance the power between the different energy sources. In this paper, a comparative analysis of three energy management strategies, including the state machine control method, the classical PI control method and equivalent consumption minimization strategy (ECMS) is performed. The paper's main objective is enhancing the DC-bus voltage profile of a hybrid fuel cell/battery/supercapacitor power system equipped with the developed under-mentioned EMS by using a hybrid modified optimization technique that combines Harris Hawks optimization (HHO) and Sine Cosine Algorithm (SCA). The new hybrid HHO-SCA is employed to determine the optimal control parameters of the DC-bus voltage controller, which significantly assists in enhancing the DC-bus voltage profile as well as the performance of the applicable ESS in terms of improving efficiency and SoC. The effectiveness of the suggested control schemes is simulated using MATLAB/SIMULINK software. The simulation results confirmed that the proposed HHO-SCA is superior and efficient in improving the DC-bus voltage.     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy

A hybrid power system consists of a fuel cell and an energy storage device like a battery and/or a supercapacitor possessing high energy and power density that beneficially drives electric vehicle motor. The structures of the fuel cell-based power system are complicated and costly, and in energy management strategies (EMSs), the fuel cell's characteristics are usually neglected. In this study, a variable structure battery (VSB) scheme is proposed to enhance the hybrid power system, and an incremental fuzzy logic method is developed by considering the efficiency and power change rate of fuel cell to balance the power system load. The principle of VSB is firstly introduced and validated by discharge and charge experiments. Subsequently, parameters matching of the fuel cell hybrid power system according to the proposed VSB are designed and modeled. To protect the fuel cell as well as ensure the efficiency, a fuzzy logic EMS is formulated via setting the fuel cell operating in a high efficiency and generating an incremental power output within the affordable power slope. The comparison between a traditional deterministic rules-based EMS and the designed fuzzy logic was implemented by numerical simulation in three different operation conditions: NEDC, UDDS, and user-defined driving cycle. The results indicated that the incremental fuzzy logic EMS smoothed the fuel cell power and kept the high efficiency. The proposed VSB and incremental fuzzy logic EMS may have a potential application in fuel cell vehicles.     

Experimental investigation on the online fuzzy energy management of hybrid fuel cell/battery power system for UAVs

The hybrid powerplant combining a fuel cell and a battery has become one of the most promising alternative power systems for electric unmanned aerial vehicles (UAVs). To enhance the fuel efficiency and battery service life, highly effective and robust online energy management strategies are needed in real applications.In this work, an energy management system is designed to control the hybrid fuel cell and battery power system for electric UAVs. To reduce the weight, only one programmable direct-current to direct-current (dcdc) converter is used as the critical power split component to implement the power management strategy. The output voltage and current of the dcdc is controlled by an independent energy management controller. An executable process of online fuzzy energy management strategy is proposed and established. According to the demand power and battery state of charge, the online fuzzy energy management strategy produces the current command for the dcdc to directly control the output current of the fuel cell and to indirectly control the charge/discharge current of the battery based on the power balance principle.Another two online strategies, the passive control strategy and the state machine strategy, are also employed to compare with the proposed online fuzzy strategy in terms of the battery management and fuel efficiency. To evaluate and compare the feasibility of the online energy management strategies in application, experiments with three types of missions are carried out using the hybrid power system test-bench, which consists of a commercial fuel cell EOS600, a Lipo battery, a programmable dcdc converter, an energy management controller, and an electric load. The experimental investigation shows that the proposed online fuzzy strategy prefers to use the most power from the battery and consumes the least amount of hydrogen fuel compared with the other two online energy management strategies.     

Optimized power management based on adaptive-PMP algorithm for a stationary PEM fuel cell/battery hybrid system

This research develops an efficient and robust polymer electrolyte membrane (PEM) fuel cell/battery hybrid operating system. The entire system possesses its own rapid dynamic response benefited from hybrid connection and power split characteristics due to DC/DC buck-boost converter. An indispensable energy management system (EMS) plays a significant role in achieving optimal fuel economy and in a promising running stability. EMS as an indispensable part plays a significant role in achieving optimal fuel economy and promising operation stability. This study aims to develop an adaptive supervisory EMS that comprises computer-aided engineering tools to monitor, control, and optimize the performance of the hybrid power system. A stationary fuel cell/battery hybrid operating system is optimized using adaptive-Pontryagin's minimum principle (A-PMP). The proposed algorithm depends on the adaptation of the control parameter (i.e., fuel cell output power) from the state of charge (SOC) and load power feedback. The integrated model simulated in a Matlab/Simulink environment includes the fuel cell, battery, DC/DC converter, and power requirements models by analyzing the three different load profiles. Real-time experiments are performed to verify the effectiveness of EMS after analyzing the simulated operating principle and control scheme.     

A coupled power-voltage equilibrium strategy based on droop control for fuel cell/battery/supercapacitor hybrid tramway

In order to improve the robustness of the energy management system (EMS) and avoid the influence of demand power on the design of EMS, a coupled power-voltage equilibrium strategy based on droop control (CPVE-DC) is proposed in this paper. Making use of the principal that the DC bus can directly reflect the changes of load power, the proposed strategy couples DC bus voltage with output powers through droop control to achieve self-equilibrium. The proposed EMS is applied into a hybrid tramway model configured with multiple proton exchange membrane fuel cell (PEMFC) systems, batteries and super capacitors (SCs). FC systems and SC systems are responsible for satisfying most of the demand power, therefore the CPVE-DC strategy generates FCs and SCs reference power through power-voltage droop control on the primary control. Then batteries supplement the rest part of load power and generate DC bus voltage reference value of the next sampling time. With the gambling between output power and DC bus voltage, the hybrid system achieves self-equilibrium and steps into steady operation by selecting appropriate droop coefficients. Then the secondary control of the proposed strategy allocates power between every single unit. In addition, a penalty coefficient is introduced to balance SOC of SCs. The proposed strategy is tested under a real drive cycle LF-LRV on RT-LAB platform. The results demonstrate that the proposed strategy can achieve self-equilibrium and is effective to allocate demand power among these power sources,achieve active control for the range of DC bus voltage and SOC consensus of SCs as well. In addition, some faults are simulated to verify the robustness of the proposed strategy and it turns out that the CPVE-DC strategy possesses higher robustness. Finally, the CPVE-DC strategy is compared with equivalent consumption minimization strategy (ECMS) and the results shows that the proposed strategy is able to get higher average efficiency and lower equivalent fuel consumption.     

Optimal energy management system for stand-alone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic

This paper presents a novel hourly energy management system (EMS) for a stand-alone hybrid renewable energy system (HRES). The HRES is composed of a wind turbine (WT) and photovoltaic (PV) solar panels as primary energy sources, and two energy storage systems (ESS), which are a hydrogen subsystem and a battery. The WT and PV panels are made to work at maximum power point, whereas the battery and the hydrogen subsystem, which is composed of fuel cell (FC), electrolyzer and hydrogen storage tank, act as support and storage system. The EMS uses a fuzzy logic control to satisfy the energy demanded by the load and maintain the state-of-charge (SOC) of the battery and the hydrogen tank level between certain target margins, while trying to optimize the utilization cost and lifetime of the ESS. Commercial available components and an expected life of the HRES of 25 years were considered in this study. Simulation results show that the proposed control meets the objectives established for the EMS of the HRES, and achieves a total cost saving of 13% over other simpler EMS based on control states presented in this paper.     

Adaptive robust control-based energy management of hybrid PV-Battery systems with improved transient performance

Energy management of hybrid photovoltaic (PV)-battery systems still serve as a challenging task owing to their complex and nonlinear characteristics, multicomponent structures, and the extensive range of environmental factors disturbing their nominal performance. The hybrid energy system developed in this study encompasses PV arrays, a battery component, one boost converter, and one bidirectional boost converter. In this paper, we propose a novel adaptive robust control framework for the optimal energy management of the PV-battery systems under many operating conditions and subject to unmodelled dynamics. An improved exponential-like adaptive integral sliding mode (EISM) control coupled to neural network approximator is introduced using a multi-rate convergence tweaking mechanism for the sliding surface to improve the transient performance of the closed-loop system. Furthermore, the entire dynamics of the hybrid energy system is considered unknown, unlike the previous studies that only assumed the parametric uncertainties. The global asymptotic stability of the system is guaranteed, and the effectiveness of this novel framework is compared to benchmark studies.     

Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan.     

Adaptive back-stepping control for a permanent magnet synchronous generator wind energy conversion system

The maximize energy captured from the wind of a grid-connected variable speed Wind Energy Conversion System (WECS) based on a Permanent Magnet Synchronous Generator (PMSG) is investigated in this paper. An adaptive back-stepping control scheme is applied to achieve maximum power point tracking in the coefficient of maximum power. The features of the proposed control scheme are that it deals with the random nature of wind speed, the uncertainties and external perturbations the acting on WECS effectively, where the bounds of the perturbations are not known in advance. At the same time, a proof of the convergence of the closed-loop system under the proposed controller is derived using the Lyapunov stability theory. Finally, simulations are conducted to illustrate the effectiveness of the proposed approach.     

Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

The polymer electrolyte membrane fuel cell (PEMFC) coupled with the battery is a promising hybrid power system for future energy supply application. Fuel cell durability, battery charge sustenance, and fuel consumption strongly rely on the energy management strategy (EMS). This paper puts forward an optimized rule-based EMS using genetic algorithm (GA) to optimally allocate the power between the fuel cell and the battery system. Control variables in real-time rule-based EMS are optimally adjusted with single objective of battery charge sustenance considering the fuel cell durability and efficiency. The proposed optimized rule-based EMS is simulated and experimentally verified via MATLAB/Simulink and LabVIEW-based experimental rig, respectively. The conventional rule-based EMS, fuzzy logic EMS, and dynamic programming (DP) EMS are also examined for comparison. The comparison results elucidate that the optimized rule-based EMS realizes a large performance improvement over the conventional rule-based and fuzzy logic EMSs. Near optimal performance is verified compared with DP EMS in terms of fuel economy, battery charge sustenance, fuel cell efficiency, and system durability. The combination of rule-based EMS and GA optimization algorithm has the advantage of having expert experience and global optimization properties, realizing optimal power allocation in real-time application with lower computation burden, which could be applied easily to other EMS system without loss of validity.     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.     

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.     

Adaptive energy management for fuel cell hybrid power system with power slope constraint and variable horizon speed prediction

An adaptive energy management strategy (EMS) is proposed to improve the economy and reliability of the fuel cell vehicle. Firstly, a variable horizon speed prediction method based on the principal component analysis and the K-means clustering is constructed. Then, an adaptive equivalent consumption minimization strategy (AECMS) with power slope constraints was designed to minimize the hydrogen consumption while ensuring reliability. Finally, a proportional-integral controller is used to track the air flow and pressure of the fuel cell engine (FCE) under energy distribution. Simulation results under West Virginia University Suburban (WVUSUB) show that the proposed strategy can improve the speed prediction accuracy by 2.80% and 25.57%, and reduce the hydrogen consumption by 2.79% and 2.66%, respectively, compared with the fixed 12 s and 15 s horizon. Moreover, the control error of oxygen excess ratio and the cathode pressure under energy distribution are 0.0102 (0.51%) and 189.4 Pa (0.0935%), respectively, indicating better reliability than the strategy without constraint.     

Autonomous renewable energy conversion system

This paper briefly reviews the need for renewable power generation and describes a medium-power Autonomous Renewable Energy Conversion System (ARECS), integrating conversion of wind and solar energy sources. The objectives of the paper are to extract maximum power from the proposed wind energy conversion scheme and to transfer this power and the power derived by the photovoltaic system in a high efficiency way to a local isolated load. The wind energy conversion operates at variable shaft speed yielding an improved annual energy production over constant speed systems. An induction generator (IG) has been used because of its reduced cost, robustness, absence of separate DC source for excitation, easier dismounting and maintenance. The maximum energy transfer of the wind energy is assured by a simple and reliable control strategy adjusting the stator frequency of the IG so that the power drawn is equal to the peak power production of the wind turbine at any wind speed. The presented control strategy also provides an optimal efficiency operation of the IG by applying a quadratic dependence between the IG terminal voltage and frequency V∼f². For improving the total system efficiency, high efficiency converters have been designed and implemented. The modular principle of the proposed DC/DC conversion provides the possibility for modifying the system structure depending on different conditions. The configuration of the presented ARECS and the implementation of the proposed control algorithm for optimal power transfer are fully discussed. The stability and dynamic performance as well as the different operation modes of the proposed control and the operation of the converters are illustrated and verified on an experimental prototype.     

Energy management of an FC/UC hybrid vehicular power system using a combined neural network-wavelet transform based strategy

An energy management strategy (EMS) is one of the most important issues for the efficiency and performance of a hybrid vehicular system. This paper deals with a neural network and wavelet transform based EMS proposed for a fuel cell/ultra-capacitor hybrid vehicular system. The proposed method combines the capability of wavelet transform to treat transient signals with the ability of auto-associative neural network supervisory mode control. The main originality of the paper is related with the application of neural network instead of another intelligent control method, fuzzy logic, which is presented in the recent publication of the authors, and the combination of neural network-wavelet transform approaches. Then, the effectiveness comparison of both methods considering one of the most important points in a vehicular system, fuel consumption (or hydrogen consumption), is realized. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB^®, Simulink^® and SimPowerSystems^® environments.     

A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system

Due to increasing concerns on environmental pollution and depleting fossil fuels, fuel cell (FC) vehicle technology has received considerable attention as an alternative to the conventional vehicular systems. However, a FC system combined with an energy storage system (ESS) can display a preferable performance for vehicle propulsion. As the additional ESS can fulfill the transient power demand fluctuations, the fuel cell can be downsized to fit the average power demand without facing peak loads. Besides, braking energy can be recovered by the ESS. This study focuses on a vehicular system powered by a fuel cell and equipped with two secondary energy storage devices: battery and ultra-capacitor (UC). However, an advanced energy management strategy is quite necessary to split the power demand of a vehicle in a suitable way for the on-board power sources in order to maximize the performance while promoting the fuel economy and endurance of hybrid system components. In this study, a wavelet and fuzzy logic based energy management strategy is proposed for the developed hybrid vehicular system. Wavelet transform has great capability for analyzing signals consisting of instantaneous changes like a hybrid electric vehicle (HEV) power demand. Besides, fuzzy logic has a quite suitable structure for the control of hybrid systems. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB^®, Simulink^® and SimPowerSystems^® environments.     

Application of ANFASO for optimal power flow management of MG-connected system with energy storage

The power flow management scheme for a microgrid (MG)-connected system utilizing a hybrid technique is suggested in this dissertation. An MG-connected system includes photovoltaic, wind turbine, micro turbine and battery storage. Due to the use of this resource, power production is intermittent and unpredictable, as well as unstable, which causes fluctuation of power in hybrid renewable energy system. To ensure the fluctuation of power, an optimal hybrid technique is suggested. The suggested hybrid technique is joint execution on ANFIS and ASOA. ANFIS stands for adaptive neuro fuzzy interference system, and ASOA stands for advanced salp swarm optimization algorithm, thus it is commonly known as the ANFASO method. In the established method, ANFIS is applied to continuously track the MG-connected system's required load. ASOA optimizes the perfect combination of MG in terms of predicted required load. The suggested methodology is used for optimal cost and to increase renewable energy sources (RESs). Constraints are RES accessibility, power demand and the storage elements. Using the MATLAB/Simulink work site, the ANFASO approach is executed and implemented compared with existing methods. The suggested method is compared with genetic algorithm (GA), BFA and the artificial bee colony algorithm (ABC), and the observed elapsed time of ABC is 37.11 seconds, BFA is 36.96 seconds and GA is 38.08 seconds. The elapsed time of the proposed technique was found to be lower (36.47 seconds) compared to existing techniques. Significant improvements regarding utilization of RES and total generation cost accuracy are attainable by utilizing the proposed approach.