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.     

An online prognostics-based health management strategy for fuel cell hybrid electric vehicles

As the energy transformation in the transportation sector is taking place driven by the development of fuel cell technologies, fuel cell hybrid electric vehicles become promising solutions owing to their long driving duration and zero emissions. However, the unsatisfied lifespan of fuel cells is an inevitable obstacle for their massive commercialization. This paper aims to propose an online adaptive prognostics-based health management strategy for fuel cell hybrid electric vehicles, which can improve the durability of the fuel cell thanks to online health monitoring. Here, particle filtering method is adapted for online fuel cell prognostics and the uncertainty of the predicted results is calculated based on the distribution of particles. A health management strategy is developed based on prognostics and a decision-making process is designed by considering the prognostics uncertainty through a decision fusion method. The obtained results show that the developed strategy has effectively improved the durability of the on-board fuel cell by up to 95.4%. Moreover, a sensitivity analysis of the prognostics occurrence frequency and probability calculation has also been conducted in this paper.     

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.     

An improved energy management strategy for FC/UC hybrid electric vehicles propelled by motor-wheels

The hybridization of the fuel-cell electric-vehicle (FCEV) by a second energy source has the advantage of improving the system's dynamic response and efficiency. Indeed, an ultra-capacitor (UC) system used as an energy storage device fulfills the FC slowest dynamics during fast power transitions and recovers the braking energy. In FC/UC hybrid vehicles, the search for a suitable power management approach is one of the main objectives. In this paper, an improved control strategy managing the active power distribution between the two energy sources is proposed. The UC reference power is calculated through the DC link voltage regulation. For the FC power demand, an algorithm with five operating modes is developed. This algorithm, depending on the UC state of charge (SOC) and the vehicle speed level, minimizes the FC power demand transitions and therefore ameliorates its durability. The traction power is provided using two permanent magnetic synchronous motor-wheels to free more space in the vehicle. The models of the FC/UC vehicle system parts and the control strategy are developed using MATLAB software. Simulation results show the effectiveness of the proposed energy management strategy.     

A new cost-minimizing power-allocating strategy for the hybrid electric bus with fuel cell/battery health-aware control

The two primary challenges preventing the commercialization of fuel cell hybrid electric vehicles (FCHEV) are their high cost and limited lifespan. Improper use usage can could also hasten the breakdown of proton exchange membrane fuel cell (PEMFC). This paper proposes a new cost-minimizing power-allocating technique with fuel cell/battery health-aware control to optimize the economic potential of fuel cell/battery hybrid buses. The proposed framework quantifies the fuel cell (FC) deterioration of the whole working zone in a real hybrid electric bus using a long short-term memory network (LSTM), which reduces the time required to get the key lifetime parameters. A new FC lifespan model is embedded into the control framework, together with a battery aging model, to balance hydrogen consumption and energy source durability. In addition, in the speed prediction step, an enhanced online Markov prediction approach with stochastic disturbances is presented to increase the forecast accuracy for future disturbances. Finally, comparative analysis is used to verify the efficacy of the suggested approach, which shows that when compared to the benchmark method, the proposed strategy may extend the FC lifetime and lower operating costs by 5.04% and 3.76%, respectively.     

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.     

An innovative optimal power allocation strategy for fuel cell, battery and supercapacitor hybrid electric vehicle

An optimal design of a three-component hybrid fuel cell electric vehicle comprised of fuel cells, battery, and supercapacitors is presented. First, the benefits of using this hybrid combination are analyzed, and then the article describes an active power-flow control strategy from each energy source based on optimal control theory to meet the demand of different vehicle loads while optimizing total energy cost, battery life and other possible objectives at the same time. A cost function that minimizes the square error between the desired variable settings and the current sensed values is developed. A gain sequence developed compels the choice of power drawn from all devices to follow an optimal path, which makes trade-offs among different targets and minimizes the total energy spent. A new method is introduced to make the global optimization into a real-time based control. A model is also presented to simulate the individual energy storage systems and compare this invention to existing control strategies, the simulation results show that the total energy spent is well saved over the long driving cycles, also the fuel cell and batteries are kept operating in a healthy way.     

A control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid unmanned aerial vehicles

Fuel cell/battery hybrid energy storage system (HESS) powered unmanned aerial vehicle (UAV) has the outstanding advantage of long endurance time. Trajectory tracking motion is a commonly used task execution mode of UAVs, especially in autonomous UAVs. This study aims at developing a control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid UAVs. Its position tracking control adopts model predictive control (MPC) and an extended state observer to eliminate the modeling errors and effect of interference. The attitude tracking control adopts an auto-disturbance rejection controller having a quick response. The obtained control parameters are given as an input to the energy management block. Energy management strategies (EMSs) based on online dynamic programming and hierarchical MPC have been proposed. The results obtained from a simulation show that the proposed trajectory tracking control architecture can track the target trajectory stably with a small tracking error. The tracking performance is stable under interference. Experimental results show that dynamic programming is solved online with good control performance. Compared to ordinary EMSs, dynamic programming and hierarchical MPC can increase endurance time by 2.69% and 1.27%, respectively. The proposed control architecture verifies the coordination of energy management and trajectory tracking control, and prospected the advantages of the combination of fuel cell and autonomous driving for long endurance UAVs in the future.     

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.     

Optimal vehicle control strategy of a fuel cell/battery hybrid city bus

In this article, an optimal vehicle control strategy based on a time-triggered controller area network (TTCAN) system for a polymer electrolyte membrane (PEM) fuel cell/nickel-metal hydride (Ni-MH) battery powered city bus is presented. Aiming at improving the fuel economy of the city bus, the control strategy comprises an equivalent consumption minimization strategy (ECMS) and a braking energy regeneration strategy (BERS). On the basis of the introduction of a battery equivalent hydrogen consumption model incorporating a charge-sustaining coefficient, an analytical solution to the equivalent consumption minimization problem is given. The proposed strategy has been applied in several city buses for the Beijing Olympic Games of 2008. Results of the “China city bus typical cycle” testing show that, the ECMS and the BERS lowered hydrogen consumption by 2.5% and 15.3% respectively, compared with a rule-based strategy. The BERS contributes much more than the ECMS to the fuel economy, because the fuel cell system does not leave much room for the optimal algorithm in improving the efficiency.     

Towards health-aware energy management strategies in fuel cell hybrid electric vehicles: A review

An energy management strategy (EMS) is responsible for distributing the power between the electrochemical power sources of a fuel cell hybrid electric vehicle (FCHEV) with a view to minimizing the hydrogen consumption and maximizing the lifetime of the system. However, the energetic characteristics of the electrochemical devices (fuel cell, battery, and supercapacitor) are time-varying due to the influence of ageing, and different ambient and operating conditions. Any drift in the characteristics of the power sources can lead to the mismanagement of an EMS. According to the literature, ignorance of health adaptation can increase the hydrogen consumption from almost 6.5%–24% depending on the EMS. Therefore, it is necessary to develop a strategy which is aware of the actual state of the components while conducting the power split. Health monitoring techniques are potential candidates to deal with the uncertainties arising from the mentioned factors. In this respect, this paper first puts forward a concise review of the general modeling techniques which are essential for developing precise health monitoring techniques and in turn EMSs. Subsequently, the utilized methods for prognosis, diagnosis, and health state tracking of each of the mentioned power sources in a FCHEV are introduced. Then, a new taxonomy for the classification of the EMSs based on their health-awareness is proposed based on which three categories of prognostic-based, diagnostic-based, and systemic EMSs are formed. Each category is thoroughly explained, and a state-of-the-art review of these health-aware EMSs is presented. Finally, future perspectives of this new line of research and development are discussed before drawing a conclusion.     

Multi-mode control strategy for fuel cell electric vehicles regarding fuel economy and durability

A proton electrolyte membrane (PEM) fuel cell system and a Li-ion battery (LIB) are two power sources in a fuel cell electric vehicle (FCEV). The fuel cell system is composed of a fuel cell stack and subsystems for air/hydrogen supply and cooling water. The operation procedure of the fuel cell system can be generally separated into several processes, e.g. starting up, normal/abnormal working and shutting down. In this paper, a multi-mode real-time control strategy for a FCEV is proposed. The strategy is established based on three typical processes (starting up, normal working, shutting down) of the fuel cell system, taking the fuel economy and system durability into consideration. The strategy is applied into a platform vehicle for the 12th 5-year project of “the next generation technologies of fuel cell city buses”. Experiments of the “China city bus typical cycle” on a test bench for the bus were carried out. Results show that, the fuel economy is 7.6 kg (100 km)⁻¹ in the battery charge-sustaining status. In a practical situation, a total driving mileage of more than 270 km can be achieved. Cycle testing also showed that, the degradation rate of the fuel cell was reduced to half of the original level. No performance degradation of the LIB system was observed in the cycling test.     

Lifespan-consciousness and minimum-consumption coupled energy management strategy for fuel cell hybrid vehicles via deep reinforcement learning

Energy management strategy (EMS) based on optimized deep reinforcement learning plays a critical role in minimizing fuel consumption and prolonging the fuel cell stack lifespan for fuel cell hybrid vehicles. The deep Q-learning (DQL) and deep deterministic policy gradient (DDPG) algorithms with priority experience replay are proposed in this research. The factors of fuel economy and power fluctuation are incorporated into the multi-objective reward functions to decline the fuel consumption and extend the lifetime of fuel cell stack. In addition, the degradation rate is introduced to reflect the lifetime of fuel cell stack. Furthermore, compared to the referenced optimally energy management strategy (dynamic planning), the DQL-based and DDPG-based EMS with prioritized experience replay (DQL-PER, DDPG-PER) are evaluated in hydrogen consumption and cumulative degradation of fuel cell stack under four driving cycles, FTP75, US06-2, NEDC and LA92-2, respectively. The training results reveal that the DQL-PER-based EMS performances better under FTP75 and US06-2 driving cycles, whereas DDPG-PER-based EMS has better performance under NEDC driving cycle, which provide a potential for applying the proposed algorithm into multi-cycles.     

Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: Degradation models and strategies

Considering the overwhelming pressure on worldwide demand of fossil fuels and the climate change caused by air pollution, hybrid electric vehicles have seen a promising future thanks to the development of renewable energy sources. Among various kinds of energy sources that have been used in hybrid electric vehicles, lithium-ion battery and proton exchange membrane (PEM) fuel cell exist to be the most favorable ones owing to their high energy density and power density. However, the degradation issues of the energy sources tend to be neglected when designing the energy management strategies for the hybrid electric vehicles. Concerning existing literature, degradation modelling methods of lithium-ion batteries and PEM fuel cells are reviewed and the possibility of integrating them into health-conscious energy management is discussed. Besides, a variety of energy management strategies that have taken the influence of degradations into consideration are reviewed and classified. The contribution of this paper is to investigate the possibility of developing a health-conscious energy management strategy based on accurate estimation of degradation to improve the durability of the system.     

Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles

The energy management strategy (EMS) is a key to reduce the equivalent hydrogen consumption and slow down fuel cell performance degradation of the plug-in fuel cell hybrid electric vehicles. Global optimal EMS based on the whole trip information can achieve the minimum hydrogen consumption, but it is difficult to apply in real driving. This paper tries to solve this problem with a novel hierarchical EMS proposed to realize the real-time application and approximate global optimization. The long-term average speed in each future trip segment is predicted by KNN, and the short-term speed series is predicted by a new model averaging method. The approximate global optimization is realized by introducing hierarchical reinforcement learning (HRL), and the strategy within the speed forecast window is optimized by introducing upper confidence tree search (UCTS). The vehicle speed prediction and the proposed EMS have been verified using the collected real driving cycles. The results show that the proposed strategy can adapt to driving style changes through self-learning. Compared with the widely used rule-based strategy, it can evidently reduce hydrogen consumption by 6.14% and fuel cell start-stop times by 21.7% on average to suppress the aging of fuel cell. Moreover, its computation time is less than 0.447 s at each step, and combined with rolling optimization, it can be used for real-time application.     

Disruptive innovations: The case for hydrogen fuel cells and battery electric vehicles

The investment of private money in technological innovation is driven by the expectation of successful market penetration. This decision to invest is less risky when the innovation represents gradual improvement of existing technologies. The term disruptive innovation is used to describe the opposite case, i.e. innovations that are so different that their establishment in the market causes a disruption to the pre-existing system. The existing literature on disruptive innovations provides us with historic case studies of successful market capture by new technologies, but this in itself is insufficient to clarify the chances of success for nascent technologies. This paper sets out to bring greater clarity to the characteristics of disruptive innovation in a way that informs the debate on the viability of emerging technologies. Whilst existing definitions are based on technologies that were successful, this paper proposes a three part criteria to define candidate disruptive technologies: disruption should relate to manufacturers and/or infrastructure (the two often being inter-related), whilst innovation must provide more than the equivalence of service to the end-user. A review of seven historical case studies of successful disruptive technologies reveals seven characteristics of candidate disruptive technologies at the stage of niche market penetration. Examining battery electric and hydrogen fuel cell vehicles against these seven characteristics, shows that both candidate disruptive technologies share the same challenges as those identified in the successful historic case studies and also helps to identify potential pathways to greater market penetration in the future for these technologies.     

A comparison of rule-based and model predictive controller-based power management strategies for fuel cell/battery hybrid vehicles considering degradation

Traditional power management systems for hybrid vehicles often focus on the optimization of one particular cost factor, such as fuel consumption, under specific driving scenarios. The cost factor is usually based on the beginning-of-life performance of system components. Typically, such strategies do not account for the degradation of the different components of the system over their lifetimes. This study incorporates the effect of fuel cell and battery degradation within their cost factors and investigates the impact of different power management strategies on fuel cell/battery loads and thus on the operating cost over the vehicle's lifetime. A simple rule-based power management system was compared with a model predictive controller (MPC) based system under a connected vehicle scenario (where the future vehicle speed is known a priori within a short time horizon). The combined cost factor consists of hydrogen consumption and the degradation of both the fuel cell stack and the battery. The results show that the rule-based power management system actually performs better and achieves lower lifetime cost compared to the MPC system even though the latter contains more information about the drive cycle. This result is explained by examining the changing dynamics of the three cost factors over the vehicle's lifetime. These findings reveal that a limited knowledge of traffic information might not be as useful for the power management of certain fuel cell/battery hybrid vehicles when degradation is taken into consideration, and a simple tuned rule-based controller is adequate to minimize the lifetime cost.     

Experimental investigation of power management and control of a PV/wind/fuel cell/battery hybrid energy system microgrid

This paper presents an experimental study of a standalone hybrid microgrid system. The latter is dedicated to remote area applications. The system is a compound that utilizes renewable sources that are Wind Generator (WG), Solar Array (SA), Fuel Cell (FC) and Energy Storage System (ESS) using a battery. The power electronic converters play a very important role in the system; they optimize the control and energy management techniques of the various sources. For wind and solar subsystem, the speed and Single Input Fuzzy Logic (SIFL) controllers are used respectively to harvest the maximum power point tracking (MPPT). To maintain a balance of energy in the hybrid system, an energy management strategy based on the battery state of charge (SOC) has been developed and implemented experimentally. The AC output voltage regulation was achieved using a Proportional Integral (PI) controller to supply a resistive load with constant amplitude and frequency. According to the obtained performances, it was concluded that the proposed system is very promising for potential applications in hybrid renewable energy management systems.     

Health-aware frequency separation method for online energy management of fuel cell hybrid vehicle considering efficient urban utilization

Frequency separation methods (FSMs) are frequently used to implement energy management of fuel cell hybrid vehicle (FCHV), due to their flexible online implementation and resilience under diverse driving environments. However, predefined static rules of FSM generally result in inefficient operation of FCHV and rapid deterioration of sources. Additionally, allocated limits of storage devices are likely to be violated in the conventional FSM. With this inspiration, the paper proposes a novel health-aware FSM (HFSM) to appropriately distribute the traction power among energy sources of FCHV with efficient urban utilization. The power separation rules of HFSM are tuned in an instantaneous manner to concurrently realize the fuel economy, lifespan extension and allocated storage limits. Within HFSM, an online optimizer is formulated, which introduces the concept of soft/hard limitations and rationalized cost structure to adequately quantify the fuel consumption and health degradation of fuel cell. An adaptive droop adjustment is then integrated with HFSM to consistently realize the storage limitations. Compared to conventional FSM, considerable improvements in the fuel economy and fuel cell service life are observed over an extended iterative loop of standard urban driving cycles.     

The fuel cell electric vehicles: The highlight review

Transportation sector is the important sector and consumed the most fossil fuel in the world. Since COVID-19 started in 2019, this sector had become the world connector because every country relies on logistics. The transportation sector does not only deal with the human transportation but also relates to logistics. Research in every country has searched for alternative transportation to replace internal combustion engines using fossil fuel, one of the most prominent choices is fuel cells. Fuel cells can use hydrogen as fuel. Hydrogen can be fed to the fuel cells to provide electric power to drive vehicles, no greenhouse gas emission and no direct combustion required. The fuel cells have been developed widely as the 21st century energy-conservation devices for mobile, stationary, and especially vehicles. The fuel cell electric vehicles using hydrogen as fuel were also called hydrogen fuel cell vehicles or hydrogen electric vehicles. The fuel cells were misconceived by several people that they were batteries, but the fuel cells could provide electric power continuously if their fuel was provided continuously. The batteries could provide electric power as their only capacities, when all ions are released, no power could be provided. Because the fuel cell vehicles play important roles for our future transportation, the overall review for these vehicles is significantly interesting. This overall review can provide general and technical information, variety of readers; vehicle users, manufacturers, and scientists, can perceive and understand the fuel cell vehicles within this review. The readers can realize how important the fuel cell technologies are and support research around the world to drive the fuel cell vehicles to be the leading vehicles in our sustainable developing world.