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 comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles

The attention on green and clean technology innovations is highly demanded of a modern era. Transportation has seen a high rate of growth in today's cities. The conventional internal combustion engine‐operated vehicle liberates gasses like carbon dioxide, carbon monoxide, nitrogen oxides, hydrocarbons, and water, which result in the increased surface temperature of the earth. One of the optimum solutions to overcome fossil fuel degrading and global warming is electric vehicle. The challenging aspect in electric vehicle is its energy storage system. Many of the researchers mainly concentrate on the field of storage device cost reduction, its age increment, and energy densities' improvement. This paper explores an overview of an electric propulsion system composed of energy storage devices, power electronic converters, and electronic control unit. The battery with high‐energy density and ultracapacitor with high‐power density combination paves a way to overcome the challenges in energy storage system. This study aims at highlighting the various hybrid energy storage system configurations such as parallel passive, active, battery–UC, and UC–battery topologies. Finally, energy management control strategies, which are categorized in global optimization, are reviewed. Copyright © 2017 John Wiley & Sons, Ltd.     

Optimal energy management strategy of fuel-cell battery hybrid electric mining truck to achieve minimum lifecycle operation costs

Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency-oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta-model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed-road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium-ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV.     

Jaya algorithm-based energy management system for battery- and ultracapacitor-powered ultralight electric vehicle

To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the Jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real-time applications. The simulation results show that Jaya-EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO-EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real-time applications.     

An energy matching method for battery electric vehicle and hydrogen fuel cell vehicle based on source energy consumption rate

The smart cities development requires reducing energy consumption and using as much renewable energy as possible, so the widespread use of new energy vehicles is a very important measure. In this work, for the energy system configuration and energy efficiency balance of new energy vehicles, we propose an energy matching method to study its energy efficiency from the view point for energy life cycle. Nowadays, new energy vehicles mainly include battery electric vehicles (BEV) and hydrogen fuel cell vehicles (HFCEV). Firstly, we proposed the Source to Range (STR) model. Then, based on STR model, we used energy efficiency analysis chart to visually represent the conversion, delivery and consumption of the vehicle energy life cycle. Furthermore, we proposed a Source Energy Consumption Rate (SECR), which is used to evaluate the vehicles energy efficiency. Finally, based on STR model, we obtained the dividing line of the same SECR for new energy vehicles and equivalent fuel vehicles, which provides constraints on the vehicle energy system design. The results show that STR model can provide an effective tool for energy matching and energy efficiency analysis of new energy vehicles, and has a reference for product development of new energy vehicles.     

A robust online energy management strategy for fuel cell/battery hybrid electric vehicles

Traditional optimization-based energy management strategies (EMSs) do not consider the uncertainty of driving cycle induced by the change of traffic conditions, this paper proposes a robust online EMS (ROEMS) for fuel cell hybrid electric vehicles (FCHEV) to handle the uncertain driving cycles. The energy consumption model of the FCHEV is built by considering the power loss of fuel cell, battery, electric motor, and brake. An offline linear programming-based method is proposed to produce the benchmark solution. The ROEMS instantaneously minimizes the equivalent power of fuel cell and battery, where an equivalent efficiency of battery is defined as the efficiency of hydrogen energy transforming to battery energy. To control the state of charge of battery, two control coefficients are introduced to adjust the power of battery in objective function. Another penalty coefficient is used to amend the power of fuel cell, which reduces the load change of fuel cell so as to slow the degradation of fuel cell. The simulation results indicate that ROEMS has good performance in both fuel economy and load change control of fuel cell. The most important advantage of ROEMS is its robustness and adaptivity, because it almost produces the optimal solution without changing the control parameters when driving cycles are changed.     

An optimization scheduling method of electric vehicle virtual energy storage to track planned output based on multiobjective optimization

Electric vehicle virtual energy storage technology can effectively improve the utilization of renewable energy. Aiming at the impact of the uncertainty of electric vehicle on the power grid, an optimized dispatching method of hybrid energy storage systems based on multiobjective optimization in the scenario of tracking plan output is proposed in this paper. The predicted value of the photovoltaic power obtained by the particle swarm optimization (PSO)-back propagation (BP) neural network is used to formulate the planned output of photovoltaic power generation, and the principle component analysis algorithm is used to extract the main features affecting photovoltaic power generation to further improve the prediction accuracy of photovoltaic output power. From the perspective of the service life of electric vehicles, a two-stage optimal control method of hybrid energy storage systems based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to achieve energy distributions between electric vehicles and supercapacitors. Fully consider the benefits of electric vehicle users and the capacity of tracking plans, a multiobjective optimization model of hybrid energy storage systems to track planned output is established, and the nondominated sorted genetic algorithm-III is adopted to solve the model. The validity of the model is verified by a simulation test of actual operating data of a business park in China. The simulation results show that after the optimized control, the average absolute error of the deviation power reduces from 1.092 to 0.0528 MW, power fluctuating times of electric vehicles decreases from 151 to 80, and the daily income benefit increases from $404.468 to $483.116 in the cloudy day. The method proposed in this paper can effectively improve the controllability of renewable energy, and provide a theoretical basis for the application of electric vehicle virtual energy storage technology.     

Fuel cell-battery hybrid powered light electric vehicle (golf cart): Influence of fuel cell on the driving performance

A light electric vehicle (golf cart, 5 kW nominal motor power) was integrated with a commercial 1.2 kW PEM fuel cell system, and fuelled by compressed hydrogen (two composite cylinders, 6.8 L/300 bar each). Comparative driving tests in the battery and hybrid (battery + fuel cell) powering modes were performed. The introduction of the fuel cell was shown to result in extending the driving range by 63–110%, when the amount of the stored H₂ fuel varied within 55–100% of the maximum capacity. The operation in the hybrid mode resulted in more stable driving performances, as well as in the increase of the total energy both withdrawn by the vehicle and returned to the vehicle battery during the driving. Statistical analysis of the power patterns taken during the driving in the battery and hybrid-powering modes showed that the latter provided stable operation in a wider power range, including higher frequency and higher average values of the peak power.     

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.     

Hybrid polymer electrolyte membrane fuel cell–lithium‐ion battery powertrain testing platform – hybrid fuel cell electric vehicle emulator

A testing and validation platform for hybrid fuel cell (FC)–lithium‐ion battery (LIB) powertrain systems is investigated. The hybrid FC electric vehicle emulator enables testing of hybrid system components and complete hybrid power modules up to 25 kW for application in electric light‐duty vehicles, light electric vehicles and so forth. A hybrid system comprising a 10‐kW_el low‐temperature polymer electrolyte membrane FC stack and an 11.5‐kWh LIB pack is installed. The system supplies power to a 20‐kW permanent magnet synchronous motor and a 25‐kW alternating current asynchronous, electrically programmable dynamometer is used to simulate the vehicle load during testing at dynamic drive cycle. The steady‐state performance tests of the direct current (DC) motor, DC/DC converter, low‐temperature polymer electrolyte membrane FC stack and LIB are performed as well as dynamic tests of the complete hybrid system. The Economic Commission for Europe driving cycle is selected as a reference cycle to validate the investigated hybrid FC–LIB powertrain. An efficiency of 83% and 95% is measured for electric motor and DC/DC converter, respectively. An average stack efficiency of 50% is achieved. An average hydrogen consumption of 3.9 g * km^?1 is reached during the Economic Commission for Europe driving cycle test. Copyright © 2017 John Wiley & Sons, Ltd.     

Development of new energy management strategy for a household fuel cell/battery hybrid system

This work aims to construct an efficient and robust fuel cell/battery hybrid operating system for a household application. The ability to dispatch the power demands, sustain the state of charge (SOC) of battery, optimize the power consumption, and more importantly, ensure the durability as well as extend the lifetime of a fuel cell system is the basic requirements of the hybrid operating system. New power management strategy based on fuzzy logical combined state machine control is developed, and its effectiveness is compared with various strategies such as dynamic programming (DP), state machine control, and fuzzy logical control with simulation. Experimental results are also presented, except for DP because of difficulties in achieving real‐time implementation and much faster response to load variation. The given current from the energy management system (EMS) as a reference of the fuel cell output current is determined by filtering out various harmful signals. The new power management strategy is applied to a 1‐kW stationary fuel cell/battery hybrid system. Results show that the fuel cell hybrid system can run much smoothly with prolonged lifetime.     

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.     

A comparison study of battery size optimization and an energy management strategy for FCHEVs based on dynamic programming and convex programming

Aiming to address the hydrogen economy and system efficiency of a fuel cell hybrid electric vehicle, this paper proposes comparison research of battery size optimization and an energy management strategy. One approach is based on a bi-loop dynamic programming strategy, which selects the optimal one by initializing the battery parameters in the outer loop and performs energy distribution in the inner loop. The other approach is a framework based on convex programming, which can simultaneously design energy management strategies and optimize battery size. In the dynamic programming algorithm, the influence of the different discrete steps of state variables on the results is analysed, and a discrete step that can guarantee the accuracy of the algorithm and reduce computational time is selected. The results based on the above two algorithms and considering the transient response limitations of the fuel cell are analysed as well. Finally, two driving cycles are chosen to verify and compare the performance of the proposed methodology. Simulation results show that the dynamic programming-based energy management strategy and battery size provide more accurate results, and the transient response of the fuel cell has little effect on the optimization results of the battery size and energy management strategies.     

Energy management strategy and capacity optimization for CCHP system integrated with electric-thermal hybrid energy storage system

In order to improve the comprehensive energy utilization rate of combined cooling, heating, and power (CCHP) system, a hybrid energy storage system (HESS) is proposed in this paper consisting of electric and thermal energy storage systems. And the overall optimization design and operation of CCHP system with HESS are the main problems to be solved in application. Therefore, the topology and the energy flow model of CCHP system with HESS are established and analyzed according to the energy conversion characteristics of the component equipment. Moreover, combined with five evaluative restrictions for HESS system, a rule-based energy management strategy is designed to realize the decoupling regulation of electric energy and thermal energy in CCHP system. On this basis, a multi-objective optimization model is studied by taking the indicators of annual cost ratio, the primary energy consumption ratio, and loss energy ratio, and then the capacity parameters are optimized by particle swarm optimization algorithm (PSOA). Finally, a case is carried out to compare the energy allocation situations and capacity optimization results between CCHP system with HESS and CCHP system with single thermal energy storage system (ST). Results show that the capacity of ICE is reduced by 34%, and the annual cost and the primary energy consumption are saved about 7.69% and 18.47%, respectively, demonstrating that HESS has better optimization effect and applicable for small-scale CCHP system.     

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.     

Numerical analysis of an energy storage system based on a metal hydride hydrogen tank and a lithium-ion battery pack for a plug-in fuel cell electric scooter

This work investigates on the performance of a hybrid energy storage system made of a metal hydride tank for hydrogen storage and a lithium-ion battery pack, specifically conceived to replace the conventional battery pack in a plug-in fuel cell electric scooter. The concept behind this solution is to take advantage of the endothermic hydrogen desorption in metal hydrides to provide cooling to the battery pack during operation.The analysis is conducted numerically by means of a finite element model developed in order to assess the thermal management capabilities of the proposed solution under realistic operating conditions.The results show that the hybrid energy storage system is effectively capable of passively controlling the temperature of the battery pack, while enhancing at the same time the on-board storage energy density. The maximum temperature rise experienced by the battery pack is around 12 °C when the thermal management is provided by the hydrogen desorption in metal hydrides, against a value above 30 °C obtained for the same case without thermal management. Moreover, the hybrid energy storage system provides the 16% of the total mass of hydrogen requested by the fuel cell stack during operation, which corresponds to a significant enhancement of the hydrogen storage capability on-board of the vehicle.     

Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

To improve the economic performance of dual‐motor battery electric vehicles, a novel driving pattern recognition–based energy management strategy (NDPREMS) is proposed in this paper. The NDPREMS firstly employs principal component analysis method to reduce the dimension of characteristic parameters of driving patterns and uses hierarchical cluster method for classifying driving patterns to construct a database of typical driving patterns, based on which a driving pattern recognizer is achieved using generalized regression neural network (GRNN) and the accuracy of this recognizer reaches 96.08%. In order to reasonably allocate the power between two motors, on the basis of rule‐based energy management strategy (REMS), a dynamic programming–based energy management strategy (DPEMS) under typical driving patterns is formulated. By doing so, the logic thresholds of REMS are optimized, and thus, the NDPREMS is achieved. Comparison simulations of control effect concerning the REMS, DPEMS, and NDPREMS are performed under typical driving patterns. Results indicate that the proposed NDPREMS exhibits greater energy conservation compared with REMS, the economic improvement under urban driving pattern is the most obvious at 11.04%, the improvement under the comprehensive test driving pattern is 5.65%, and the performance of the NDPREMS is similar to that of DPEMS.     

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.     

Experimental investigation on the dynamic performance of a hybrid PEM fuel cell/battery system for lightweight electric vehicle application

A hybrid system combining a 2 kW air-blowing proton exchange membrane fuel cell (PEMFC) stack and a lead–acid battery pack is developed for a lightweight cruising vehicle. The dynamic performances of this PEMFC system with and without the assistance of the batteries are systematically investigated in a series of laboratory and road tests. The stack current and voltage have timely dynamic responses to the load variations. Particularly, the current overshoot and voltage undershoot both happen during the step-up load tests. These phenomena are closely related to the charge double-layer effect and the mass transfer mechanisms such as the water and gas transport and distribution in the fuel cell. When the external load is beyond the range of the fuel cell system, the battery immediately participates in power output with a higher transient discharging current especially in the accelerating and climbing processes. The DC–DC converter exhibits a satisfying performance in adaptive modulation. It helps rectify the voltage output in a rigid manner and prevent the fuel cell system from being overloaded. The dynamic responses of other operating parameters such as the anodic operating pressure and the inlet and outlet temperatures are also investigated. The results show that such a hybrid system is able to dynamically satisfy the vehicular power demand.