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.     

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.     

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.     

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.     

An energy management strategy based on dynamic power factor for fuel cell/battery hybrid locomotive

In order to efficiently absorb more regenerative braking energy which sustains much longer compared with the conventional vehicle, and guarantee the safety of the hybrid system under the actual driving cycle of locomotive, an energy management control based on dynamic factor strategy is proposed for a scale-down locomotive system which consists of proton exchange membrane fuel cell (PEMFC) and battery pack. The proposed strategy which has self-adaption function for different driving cycles aims to achieve the less consumption of hydrogen and higher efficiency of the hybrid system. The experimental results demonstrate that the proposed strategy is able to maintain the charge state of battery (SOC) better than Equivalent Consumption Minimization Strategy (ECMS), and the proposed strategy could keep the change trend of SOC, which the final SOC is closed to the target value regardless of the initial SOC of battery. Moreover, the hydrogen consumption has been reduced by 0.86g and the efficiency of overall system has been raised of 2% at least than ECMS under the actual driving cycle through the proposed strategy. Therefore, the proposed strategy could improve the efficiency of system by diminishing the conversion process of energy outputted by fuel cell.     

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.     

Neural network-based energy management of multi-source (battery/UC/FC) powered electric vehicle

Due to increased environmental pollution and global warming concerns, the use of energy storage units that can be supported by renewable energy resources in transportation becomes more of an issue and plays a vital role in terms of clean energy solutions. However, utilization of multiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure. Hybrid energy storage system (HESS) consists of battery, ultra-capacitor (UC), fuel cell (FC) and the vehicle is propelled with two complementary propulsion machines. To increase powertrain efficiency, traction power is simultaneously shared at different rates by propulsion machines. Propulsion powers are shared by HESS units according to following objectives: extending battery lifetime, utilizing UC and FC effectively. Primarily, to optimize the power split in HESS, a convex optimization problem is formulated to meet given objectives that results 5 years prolonged battery lifetime. However, convex optimization of complex systems can be arduous due to the excessive number of parameters that has to be taken into consideration and not all systems are suitable for linearization. Therefore, a neural network (NN)-based machine learning (ML) algorithm is proposed to solve multi-objective energy management problem. Proposed NN model is trained with convex optimization outputs and according to the simulation results the trained NN model solves the optimization problem within 92.5% of the convex optimization one.     

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.     

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.     

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.     

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.     

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.     

Online management of lithium-ion battery based on time-triggered controller area network for fuel-cell hybrid vehicle applications

This paper introduces a state of charge (SOC) estimation algorithm that was implemented for an automotive lithium-ion battery system used in fuel-cell hybrid vehicles (FCHVs). The proposed online control strategy for the lithium-ion battery, based on the Ah current integration method and time-triggered controller area network (TTCAN), incorporates a signal filter and adaptive modifying concepts to estimate the Li₂MnO₄ battery SOC in a timely manner. To verify the effectiveness of the proposed control algorithm, road test experimentation was conducted with an FCHV using the proposed SOC estimation algorithm. It was confirmed that the control technique can be used to effectively manage the lithium-ion battery and conveniently estimate the SOC.     

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.     

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.     

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.     

Application of Pontryagin's Minimal Principle to the energy management strategy of plugin fuel cell electric vehicles

This paper proposes an optimal real-time energy management strategy based on the Pontryagin's Minimal Principle (PMP) targeting at minimizing operation cost for a plug in proton electrolyte membrane (PEM) fuel cell city bus. The Dynamic Programming (DP) and PMP strategies are firstly deduced. Influences of the initial co-state value on the PMP strategy are analyzed. The DP, PMP, Charge Depleting Charge Sustaining (CDCS) and Blended strategies are compared in a simulation model. Results show that, major factors that influent the operation cost are the end value of battery State of Charge (SoC), the SoC trajectory curve, and the distribution of the working points of the fuel cell system. From a statistic viewpoint, the operation cost increases almost linearly with the end value of the SoC with a gradient of 1.41 Yuan.%⁻¹. Compared with a CDCS strategy, the operation cost can be reduced by 7.2% through taking the DP strategy, and by 5.9% through taking the PMP strategy. The PMP strategy leads to an operation cost that is 1.4% higher than the DP, but it is applicable in the real-time control system. An online energy management strategy based on PMP was set up and applied to an embedded controller. Tests in the 30 “China city bus typical cycles” showed that, the fuel economy was 5.8 kg (100 km)⁻¹, and the operation cost was 271.3 Yuan (100 km)⁻¹.     

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.     

Fuzzy energy management of hybrid renewable power system with the aim to extend component lifetime

In this paper, a fuzzy energy management algorithm for a hybrid renewable power system based on lifetime extending is presented. When the system contains two storage elements or more, the selection of the suitable element to be charged or discharged becomes of paramount importance. When the storage elements are of different types, the decision will be difficult. Conventional algorithms that make series of tests to select the storage element choose always the first available element. This way of testing affects badly the most used element and may affect the other storage elements too as they rarely operate under hard load scenarios. In this study, and in order to solve this problem, two fuzzy controllers have been used to manage the energy flow for a hybrid renewable power system. It is composed of: a photovoltaic generator as a main source, a fuel cell and batteries as a storage elements. The controllers operate as master and slave. The master controller gives orders to all the system power converters and to the slave controller as well. The latter is activated only when the storage elements are at the same state of charge. It is charged, instead of the master's, to select the suitable element to be charged or discharged. Its orders are given based on lifetime functions for each element. To examine the proposed algorithm, simulations have been performed under Matlab /Simulink (The MathWorks, Inc., Massachusetts, USA). Comparison and statistics have been carried out to give the percentage of the worked hours for each element in each operating mode. The obtained results show the high performance of the proposed algorithm. Copyright © 2017 John Wiley & Sons, Ltd.     

Real time optimal energy management strategy targeting at minimizing daily operation cost for a plug-in fuel cell city bus

This paper proposes an optimal real-time energy management strategy targeting at daily operation optimization for a plug in proton exchange membrane fuel cell electric vehicle (PFCEV) for public transportations. A novel real-time optimal energy management strategy based on the determined dynamic programming (DDP) strategy is proposed, namely the DBSD (charge Depleting – Blended – Sustaining – Depleting) strategy. A simulation model is set up to compare the DDP strategy, the DBSD strategy and the CDCS (Charge Depleting and Charge Sustaining) strategies. Compared to the CDCS strategy, the daily operating cost can be reduced by 6.4% with the DBSD strategy, and it can be reduced by 9.5% with the DDP strategy. On-road testing with the DBSD strategy shows that, the daily operation cost is 510.2 Sig. $ (100 km)⁻¹. The electric energy consumption in pure battery driven mode is about 1.68 kWh km⁻¹, and the equivalent hydrogen consumption in hybrid driven mode is about 0.14 kg km⁻¹.