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.     

Powertrain control of a solar photovoltaic-battery powered hybrid electric vehicle

This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation, and maximum power extraction whenever possible from the photovoltaic panels. The powertrain controller consists of two levels of controllers named lower level controllers and a high-level control algorithm. The lower level controllers are designed to perform individual tasks such as maximum power point tracking, battery charging, and load regulation. The perturb and observe based maximum power point tracking algorithm is used for extracting maximum power from solar photovoltaic panels while the battery charging controller is designed using a PI controller. A high-level control algorithm is then designed to switch between the lower level controllers based on different operating conditions such as high state of charge, low state of charge, maximum battery current, and heavy load by respecting the constraints formulated. The developed algorithm is evaluated using theoretical simulation and experimental studies. The simulation and experimental results are presented to validate the proposed technique.     

Preliminary experimental evaluation of a four wheel motors,batteries plus ultracapacitors and series hybrid powertrain

This paper reports the preliminary experimental evaluation of a four wheel motors series hybrid prototype equipped with an internal combustion engine coupled to a generator and an energy recovery system (batteries plus ultracapacitors). The paper analyses global efficiency (energy dissipated to overcome the dissipative forces on energy dissipated in fuel), autonomy in electric configuration, and the efficiency of the regenerative braking system. The tests were carried out in a test cell equipped with a chassis dynamometer. The tests were performed according to the current regulated procedures. A constant speed test was performed in order to evaluate the autonomy of the vehicle in the electric configuration. The results show that the real tank to wheels efficiency is about 30% for HOST as a series hybrid and 79% for HOST as an electric vehicle.     

The study on the power management system in a fuel cell hybrid vehicle

This paper presents a model of a hybrid electric vehicle, based on a primary proton exchange membrane fuel cell (PEMFC) and an auxiliary Li-ion battery, and its dynamics and overall performance. The power voltage from the fuel cell is regulated by a DC/DC converter before integrating with the Li-ion battery, which provides energy to the drive motor. The driving force for propelling the wheels comes from a permanent magnet synchronous motor (PMSM); where the power passes through the transmission, shaft, and the differential.     

Electric vehicle energy consumption modelling and estimation—A case study

Electric vehicles (EVs) have a limited driving range compared to conventional vehicles. Accurate estimation of EV's range is therefore a significant need to eliminate “range anxiety” that refers to drivers' fear of running out of energy while driving. However, the range estimators used in the currently available EVs are not sufficiently accurate. To overcome this issue, more accurate range estimation techniques are investigated. Nonetheless, an accurate power‐based EV energy consumption model is crucial to obtain a precise range estimation. This paper describes a study on EV energy consumption modelling. For this purpose, EV modelling is carried out using MATLAB/Simulink software based on a real EV in the market, the BMW i3. The EV model includes vehicle powertrain system and longitudinal vehicle dynamics. The powertrain is modelled using efficiency maps of the electric motor and the power electronics' data available for BMW i3. It also includes a transmission and a battery model (ie, Thevenin equivalent circuit model). A driver model is developed as well to control the vehicle's speed and to represent human driver's behaviour. In addition, a regenerative braking strategy, based on a series brake system, is developed to model the behaviour of a real braking controller. Auxiliary devices are also included in the EV model to improve energy consumption estimation accuracy as they can have a significant impact on that. The vehicle model is validated against published energy consumption values that demonstrates a satisfactory level of accuracy with 2% to 6% error between simulation and experimental results for Environmental Protection Agency and NEDC tests.     

Energy management of a fuel cell hybrid ultra-energy efficient vehicle

This paper focuses on energy management in an ultra-energy efficient vehicle powered by a hydrogen fuel cell with rated power of 1 kW. The vehicle is especially developed for the student competition Shell Eco-marathon in the Urban Concept category. In order to minimize the driving energy consumption a simulation model of the vehicle and the electric propulsion is developed. The model is based on vehicle dynamics and real motor efficiency as constant DC/DC, motor controllers and transmission efficiency were considered. Based on that model five propulsion schemes and driving strategies were evaluated. The fuel cell output parameters were experimentally determined. Then, the driving energy demand and hydrogen consumption was estimated for each of the propulsion schemes. Finally, an experimental study on fuel cell output power and hydrogen consumption was conducted for two propulsion schemes in case of hybrid and non-hybrid power source. In the hybrid propulsion scheme, supercapacitors were used as energy storage as they were charged from the fuel cell with constant current of 10 A.     

Development and performance characterization of an electric ground vehicle with independently actuated in-wheel motors

This paper presents the development and experimental characterizations of a prototyping pure electric ground vehicle, which is equipped with four independently actuated in-wheel motors (FIAIWM) and is powered by a 72 V 200 Ah LiFeYPO₄ battery pack. Such an electric ground vehicle (EGV) employs four in-wheel (or hub) motors to independently drive/brake the four wheels and is one of the promising vehicle architectures primarily due to its actuation flexibility, energy efficiency, and performance potentials. Experimental data obtained from the EGV chassis dynamometer tests were employed to generate the in-wheel motor torque response and power efficiency maps in both driving and regenerative braking modes. A torque distribution method is proposed to show the potentials of optimizing the FIAIWM EGV operational energy efficiency by utilizing the actuation flexibility and the characterized in-wheel motor efficiency and torque response.     

Development of a hybrid pneumatic-power vehicle

Many complex technologies have been developed and applied to improve the energy efficiency and exhaust emission of an engine under different driving conditions. The overall thermal efficiency of an internal-combustion engine, however, can be maintained at only about 20–30%, with aggravated problems in the design and development, such as overall difficulty, excessive time consumption or excessively high cost. For electric cars, there is still no major technological breakthrough for the rapid recharging of a large capacity battery and detection of remaining power in it. Although all currently available hybrid-power engines are able to lower the amount of exhaust emissions and the fuel consumption of the engine, they are still unable to achieve a stable and optimal running condition immediately after ignition; hence the engine's thermal-efficiency remains low. To solve the aforementioned problems, an innovative concept – a hybrid pneumatic power-system (HPPS), which stores “flow work” instead of storing electrochemical energy of the battery – is introduced. This innovative power system not only ensures that the internal-combustion ensures optimally but also recycles the exhaust flow to propel the vehicle. The optimization of the internal-combustion and recycling of the exhaust energy can increase the vehicle's efficiency from an original 15% to 33%, an overall increase of 18%.     

The development of an exchangeable PEMFC power module for electric vehicles

This paper develops an exchangeable fuel-cell power module that is replaceable on different light electric vehicles (LEVs). The module consists of a proton exchange membrane fuel-cell (PEMFC) and two battery sets, which provide continuous power for LEVs. The study includes three topics: fuel-cell control, power management, and system modularization and vehicle integration. First, we design robust controllers for the PEMFC to provide a steady voltage or current for charging the battery sets. Second, we develop a serial power train that can provide continuous power for driving the vehicle motors. Third, we modularize a power system that can be easily implemented on different LEVs. We build the system on Matlab™ SimPowerSystem for simulation before road tests, and integrate the power module onto a mobility car and an electric motorbike for experimental verification. Based on the results, the proposed systems are deemed effective.     

Robust controller design of heat pump and plug-in hybrid electric vehicle for frequency control in a smart microgrid based on specified-structure mixed H2/H∞ control technique

This paper proposes a new robust controller design of heat pump (HP) and plug-in hybrid electric vehicle (PHEV) for frequency control in a smart microgrid (MG) system with wind farm. The intermittent power generation from wind farm causes severe frequency fluctuation in the MG. To alleviate frequency fluctuation, the smart control of power consumption of HP and the power charging of PHEV in the customer side can be performed. The controller structure of HP and PHEV is a proportional integral derivative (PID) with single input. To enhance the performance and robustness against system uncertainties of the designed controller, the particle swarm optimization based-mixed H₂/H_∞ control is applied to design the PID controllers of HP and PHEV. Simulation studies confirm the superior robustness and frequency control effect of the proposed HP and PHEV controllers in comparison to the conventional controller.     

Load-frequency control of isolated wind-diesel-microhydro hybrid power systems (WDMHPS)

We examine load-frequency control of isolated WDMHPS provided with conventional proportional-plus-integral controllers. The parameters of the controller are optimised for system performance with step or realistic disturbances using an integral-square-error (ISE) criterion. Non-optimum gain settings may result if only step changes are assumed in input wind power or in load. The controller works for a continuous hybrid power system in either a continuous or a discrete mode. System performance deteriorates for discrete control. To evaluate the performance of the hybrid system producing electric power from wind and microhydro by operating with an induction generator and from diesel by using a synchronous alternator, we must consider for the state space model of the hybrid system the load-frequency and blade-pitch controllers in the continuous or discrete mode. A study of the transient responses of the system shows that transient changes in input wind power settle in 12 s while disturbances in load take only 4 s to stabilise.     

A review on battery management system from the modeling efforts to its multiapplication and integration

Progress in battery technology accelerates the transition of battery management system (BMS) from a mere monitoring unit to a multifunction integrated one. It is necessary to establish a battery model for the implementation of BMS's effective control. With more comprehensive and faster battery model, it would be accurate and effective to reflect the behavior of the battery level to the vehicle. On this basis, to ensure battery safety, power, and durability, some key technologies based on the model are advanced, such as battery state estimation, energy equalization, thermal management, and fault diagnosis. Besides, the communication of interactions between BMS and vehicle controllers, motor controllers, etc is an essential consideration for optimizing driving and improving vehicle performance. As concluded, a synergistic and collaborative BMS is the foundation for green‐energy vehicles to be intelligent, electric, networked, and shared. Thus, this paper reviews the research and development (R&D) of multiphysics model simulation and multifunction integrated BMS technology. In addition, summary of the relevant research and state‐of‐the‐art technology is dedicated to improving the synergy and coordination of BMS and to promote the innovation and optimization of new energy vehicle technology.     

Impact of energy supply infrastructure in life cycle analysis of hydrogen and electric systems applied to the Portuguese transportation sector

Hydrogen and electric vehicle technologies are being considered as possible solutions to mitigate environmental burdens and fossil fuel dependency. Life cycle analysis (LCA) of energy use and emissions has been used with alternative vehicle technologies to assess the Well-to-Wheel (WTW) fuel cycle or the Cradle-to-Grave (CTG) cycle of a vehicle's materials. Fuel infrastructures, however, have thus far been neglected. This study presents an approach to evaluate energy use and CO₂ emissions associated with the construction, maintenance and decommissioning of energy supply infrastructures using the Portuguese transportation system as a case study. Five light-duty vehicle technologies are considered: conventional gasoline and diesel (ICE), pure electric (EV), fuel cell hybrid (FCHEV) and fuel cell plug-in hybrid (FC-PHEV). With regard to hydrogen supply, two pathways are analysed: centralised steam methane reforming (SMR) and on-site electrolysis conversion. Fast, normal and home options are considered for electric chargers. We conclude that energy supply infrastructures for FC vehicles are the most intensive with 0.03–0.53 MJ_eq/MJ emitting 0.7–27.3 g CO_2eq/MJ of final fuel. While fossil fuel infrastructures may be considered negligible (presenting values below 2.5%), alternative technologies are not negligible when their overall LCA contribution is considered. EV and FCHEV using electrolysis report the highest infrastructure impact from emissions with approximately 8.4% and 8.3%, respectively. Overall contributions including uncertainty do not go beyond 12%.     

A comparative review on power conversion topologies and energy storage system for electric vehicles

Fossil fuel depletion and its adverse impact on global warming is a major driving force for a recent upsurge in the development of hybrid electric vehicles technologies. This paper is a conglomeration of the recent literature in the usages of an energy storage system and power conversion topologies in electric vehicles (EVs). An EV requires sources that have high power and energy density to decrease the charging time. Commonly used energy storage devices in EVs are fuel cells, batteries, ultracapacitors, flywheel, and photovoltaic arrays. The power output from energy storage sources is conditioned to match load characteristics with the source for maximum power delivery. A DC-DC converter topology performs this task by way of transforming voltage under the condition of power invariance. In addition, power electronics is also required to power DC/AC motors efficiently with precise control as these motors provide tractive efforts and acts as prime movers. This paper therefore brings out a critical review of the literature on EV's power conversion topologies and energy storage systems with challenges, opportunities and future directions by systematic classification of EVs and energy storage.     

Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method

Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy.     

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.     

Dynamic behaviour of Li batteries in hydrogen fuel cell power trains

A Li ion polymer battery pack for road vehicles (48 V, 20 Ah) was tested by charging/discharging tests at different current values, in order to evaluate its performance in comparison with a conventional Pb acid battery pack. The comparative analysis was also performed integrating the two storage systems in a hydrogen fuel cell power train for moped applications. The propulsion system comprised a fuel cell generator based on a 2.5 kW polymeric electrolyte membrane (PEM) stack, fuelled with compressed hydrogen, an electric drive of 1.8 kW as nominal power, of the same typology of that installed on commercial electric scooters (brushless electric machine and controlled bidirectional inverter). The power train was characterized making use of a test bench able to simulate the vehicle behaviour and road characteristics on driving cycles with different acceleration/deceleration rates and lengths. The power flows between fuel cell system, electric energy storage system and electric drive during the different cycles were analyzed, evidencing the effect of high battery currents on the vehicle driving range. The use of Li batteries in the fuel cell power train, adopting a range extender configuration, determined a hydrogen consumption lower than the correspondent Pb battery/fuel cell hybrid vehicle, with a major flexibility in the power management.     

ANFIS-MPPT control algorithm for a PEMFC system used in electric vehicle applications

Fuel cell has been considered as one of the optimistic renewable power technologies for the automotive applications. The output power of a fuel cell is immensely dependent on cell temperature and membrane water content. Hence, a maximum power point tracking controller is essentially required to extract the optimum power from the fuel cell stack. In this paper, an adaptive neuro-fuzzy inference system based maximum power point tracking controller is presented for 1.26 kW proton exchange membrane fuel cell system used in electric vehicle applications. In order to extract the optimum power, a high step-up boost converter is connected between the fuel cell and the BLDC motor. The duty cycle of the converter is controlled by using ANFIS reference model, so that the maximum power is delivered to the BLDC motor. The performance of the proposed controller is tested under normal operating conditions and also for sudden variations in the cell temperatures of the fuel cell. In addition to this, to analyze the effectiveness and tracking behaviour of the proposed controller, the results were compared with those obtained using the fuzzy logic controller. Compared to the fuzzy logic controller, the proposed ANFIS controller has increased the average DC link power by 1.95% and the average time taken to reach the maximum power point is reduced by 17.74%.     

Impacts of power management on a PEMFC electric vehicle

This paper discusses the impacts of different power management strategies on a fuel-cell vehicle. The study was carried out in three steps: fuel-cell control, power management, and system integration and verification. First, we identified the models of a proton exchange membrane fuel-cell (PEMFC) and designed robust controllers to improve the PEMFC's performance and efficiency. Second, we developed two power management structures—a serial power train and a parallel power train—which consisted of the PEMFC and secondary batteries to provide sustainable power for an electric mobility scooter. Lastly, we used the scooter's driving cycles to compare the performance and efficiency of these two power trains. Then we implemented the power trains on a microprocessor for road test. Based on the results, both power trains are deemed effective in providing continuous power for driving the scooter. In addition, the serial power train, although it uses an extra battery set, is shown to be more efficient than the parallel one.     

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.