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This paper describes a methodology to control a multi-source battery-capacitor hybrid EV incorporating a dynamic power splitting strategy. Considering the requirement for precise speed control characteristics of the EV and the non-linear behavior of traction motor with system disturbances, a non-linear state feedback controller (NLSFC) with a disturbance observer is suggested as the speed control technique for the EV traction motor. To interface a parallel configured multi-source system, a non-inverted buck-boost H-bridge is proposed as the source side converter for effective recovery of the braking energy. Detailed control design for traction motor and converter is also presented for efficient control of EV. Besides, an adaptive energy management strategy considering power profile and dynamic source characteristics is incorporated into the overall control system. The simulation results from various case studies indicate that the proposed system can follow the required vehicle speed and torque despite the sudden change in EV load torque. Furthermore, the comparative analysis between NLSFC and PI controller shows improvement with steady-state error reduction from 0.43 to 0.0014 and peak overshoot drop from 12.07% to 3.51% during acceleration of the system when proposed NLSFC is adopted. The proposed solutions will be implemented in a prototype EV system in the future.

An energy storage system works in sync with a photovoltaic system to effectively alleviate the intermittency in the photovoltaic output. Owing to its high power density and long life, supercapacitors make the battery–supercapacitor hybrid energy storage system (HESS) a good solution. This study considers the particularity of annual illumination due to climate conditions in Harbin, China. A global optimal PV-HESS sizing method is proposed by constructing a PV-HESS operating cost model and taking the annual system operating cost as the objective function. To consider the effect of battery life degradation due to different charge and discharge rates and charge and discharge times, a semi-empirical model based on the Arrhenius model was used to quantify the battery life degradation. Based on the effects of different seasons and different photovoltaic panel sizes, batteries, and supercapacitors on the optimization results, four scenarios are proposed. The feasibility of the system configuration corresponding to the four scenarios is discussed, and an optimal sizing configuration of the system is obtained. The simulation results show that the proposed method can effectively balance the degradation of the ESS due to irregular charging and discharging and determine the minimum operating cost and a reasonable sizing configuration of the system.

A direct drive wave power generation system (DDWPGS) has the advantages of a simple structure and easy deployment, and is the first choice to provide electricity for islands and operation platforms in the deep sea. However, due to the off-grid, the source and load cannot be matched, so accommodation is an important issue. Hydrogen storage is the optimal choice for offshore wave energy accommodation. Therefore, aiming at the source-load mismatch problem of the DDWPGS, an electric-hydrogen hybrid energy storage system (HESS) for the DDWPGS is designed in this paper. Based on the characteristics of the devices in the electric-hydrogen HESS, a new dynamic power allocation strategy and its control strategy are proposed. Firstly, empirical mode decomposition (EMD) is utilized to allocate the power fluctuations that need to be stabilized. Secondly, with the state of charge (SOC) of the battery and the operating characteristics of the alkaline electrolyzer being considered, the power assignments of the battery and the electrolyzer are determined using the rule-based method. In addition, model predictive control (MPC) with good tracking performance is used to adjust the output power of the battery and electrolyzer. Finally, the supercapacitor (SC) is controlled to maintain the DC bus voltage while also balancing the system’s power. A simulation was established to verify the feasibility of the designed system. The results show that the electric-hydrogen HESS can stabilize the power fluctuations dynamically when the DDWPGS captures instantaneous power. Moreover, its control strategy can not only reduce the start-stop times of the alkaline electrolyzer but also help the energy storage devices to maintain a good state and extend the service life.

Nowadays, nations are moving toward the electrification of the transportation section, and the widespread development of EV charging stations and their infrastructures supplied by the grid would strain the power grid and lead to overload issues in the network. To address this challenge, this paper presents a method for utilizing the braking energy of trains in railway stations to charge EVs located in strategic areas like park-and-ride regions close to railway stations improving energy efficiency and preventing grid overload. To validate the feasibility of the proposed system, a metro substation in Milan city is considered as a case study located in outskirts of the city and contains large number of parking space for vehicles. Three different scenarios are evaluated including DC fast charging station, AC low charging station and collaborative hybrid energy storage based AC charging station as EV charging station type. The results are studied for different EV population number, charging rate and the contractual power grid. Meanwhile, the possibility of proposed system in participating as V2G technology and taking advantage of the EV’s batteries to provide ancillary support to accelerating trains is investigated regarding peak shaving objective. The results indicated that the suggested interconnected system operates effectively when a significant quantity of EVs are parked at the station. However, the results revealed that the performance of the proposed system is notably influenced by other factors and a limited number of EVs during the early morning and late evening periods. Overall, this study confirms the feasibility of energy transfer between two types of transportation means in intermodal areas.

This paper analyzes a hybrid power system containing a fuel cell (FC) and proposes an improved scheme involving the replacement of a single energy storage system with a hybrid energy storage system. In order to achieve a reasonable power distribution between fuel cells and energy storage units and stable operation of the power grid, an efficient energy management system (EMS) based on the equivalent consumption minimization strategy (ECMS) is proposed. To enhance the dynamic response capability of hybrid energy storage systems, a low-pass filter with a variable time constant based on the ultracapacitor SOC feedback is proposed. This paper describes the design of a fuzzy logic controller that adaptively improves the equivalent consumption minimization control strategy, adjusts the equivalent factor in real time, optimizes the operating points of the fuel cell system, and improves system efficiency. The simulation model of the fuel cell hybrid system was established using MATLAB/Simulink. The proposed adaptive equivalent consumption minimization strategy (A-ECMS) was simulated and compared to the state-based and fuzzy logic energy management systems under the simulation of the real operating conditions of the ship. The results show that the proposed strategy can maintain a fuel cell system efficiency above 60% under most operating conditions and can significantly suppress the fluctuation of a fuel cell’s output power. The proposed strategy outperforms the state-based and fuzzy logic-based EMS in terms of stabilizing the hybrid power system and reducing hydrogen consumption. The effectiveness of the proposed strategy has been verified.

The capacity configuration of the energy storage system plays a crucial role in enhancing the reliability of the power supply, power quality, and renewable energy utilization in microgrids. Based on variational mode decomposition (VMD), a capacity optimization configuration model for a hybrid energy storage system (HESS) consisting of batteries and supercapacitors is established to achieve the optimal configuration of energy storage capacity in wind–solar complementary islanded microgrids. Firstly, based on the energy mapping relationship between the frequency domain and time domain, the decomposition mode number K of VMD is determined based on the principle of minimum total mode aliasing energy. Then, considering the smoothing fluctuation characteristics of different energy storage components, the dividing point N of high frequency and low frequency in the unbalanced power between the source and load in the microgrid is selected to allocate charging and discharging power instructions for the battery and supercapacitor. Finally, taking the annual comprehensive cost of the HESS as the objective function, a hybrid energy storage capacity optimization configuration model is established, and the dividing point N is used as the optimization variable to solve the model in order to obtain the optimal configuration results. The case study results show that the proposed method is more economical and feasible than the existing energy storage configuration methods.

The coordination between a hybrid energy storage system (HESS) and photovoltaic (PV) power station can significantly reduce grid-connected PV power fluctuations. This study proposes a HESS capacity optimal allocation method considering the grid-connected PV requirements. Firstly, based on the power fluctuation requirements in the PV power station grid-connected regulations, the maximum power point tracking working point switching control is performed for the PV power station, from which the grid-connected PV power and HESS power are obtained. Then, a capacity optimal allocation method and frequency division energy management strategy (EMS) for HESS is proposed to find the energy response and power response of each energy storage source. Furthermore, a multi-objective optimization function with HESS cutoff frequency as the independent variable is constructed, and the input cost of HESS and the life loss of the lithium battery are optimized. Finally, the overall strategy is compared and analyzed under the scenarios of three typical PV power fluctuations. Simulation results show that the control strategy has a good smoothing effect on PV power fluctuations. From the perspective of the annual comprehensive input cost, HESS realizes the optimal capacity allocation when the cutoff frequency is 0.0066 Hz.

The huge success of electric vehicles across the world is challenged by a lack of infrastructure and a major increase in battery material prices. This challenge positions internal combustion engine vehicles (ICEVs) to remain a vehicle of choice. The majority of these vehicles use a lead-acid battery (LAB) for starting, lighting, and ignition (SLI) functions. However, these LABs are faced with challenges of short lifespan and low storage capacity because of improved electronic systems in modern ICEVs. In this manuscript, we propose an extension application of a hybrid LAB and lithium-ion energy storage system (ESS) for a vehicle using a single source of 70 Ah and 90 Ah capacity. Whereas previously, a hybrid energy storage system (HESS) for use in a vehicle using a source of 50 Ah battery capacity was proposed. Hence, the unique contribution of the study is using an integrated fuzzy-logic and triple-loop-proportional-integral-based battery management strategy (BMS) to improve LAB performance in a wide range of vehicles with different battery capacities sizes. The results show that the proposed BMS can help increase LAB lifespan and improve the storage capacity of the system, thus ensuring reliability. Additionally, compared to a single use of LAB, the combined energy storage system shows superior performance. [ABSTRACT FROM AUTHOR]

There is a problem that how to optimize the allocation of hybrid energy storage system (HESS) economically on user-side. To solve this, a planning and scheduling method of HESS considering load smoothing is proposed. Firstly, an optimization model of energy storage system (ESS) considering energy storage life and peak-shaving rate is established, which is used to determine the rated power, rated capacity of ESS, and the charge and discharge power at each moment of the day. Then, this paper constructs a HESS consisting of lead-carbon battery (Pb-C) and supercapacitor. Variational mode decomposition (VMD) algorithm was used to extract peak load. The peak load is smoothed by super capacitor, and the composite load after peak load optimization is smoothed by Pb-C. Finally, solving the optimization model of HESS by mixed integer linear programming (MILP). And the results of the calculation examples show that the optimization method of HESS proposed in this paper not only reduces peak load effectively, but also improves benefits of HESS under the background of time-of-use electricity price. [ABSTRACT FROM AUTHOR]

For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)—and depends on the battery type—while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results. [ABSTRACT FROM AUTHOR]

Globally, Internal combustion (IC) engine-based vehicles are being replaced by electric vehicles to decrease fossil fuel depletion, pollution, and the effects of global warming. But in electric vehicles, the primary concerns are battery power consumption and battery life, which affects the range of the electric vehicle (EV). In this paper, a hybrid storage system comprising a battery and a supercapacitor is simulated in MATLAB software to enhance the range, duration, and performance of the EV by controlling the charging and discharging of the supercapacitor and battery and control logic is used to integrate a li-ion battery with a supercapacitor based on the state of charging of the battery and supercapacitor. with the use of a hybrid energy storage system, the range and performance of EVs has improved. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative optimal power split (COPS) method for a group of intelligent electric vehicles with battery/supercapacitor hybrid energy storage systems (HESSs). To achieve good performance, the proposed COPS method is made up of the upper and lower layers: the upper layer aims at obtaining the optimal power demand sequence and sending it to the lower layer; the lower layer attempts to optimize the power split for HESS. Firstly, to ensure the performance of the proposed method in practice, the resistance of the battery and supercapacitor packs as well as the DC/DC converter efficiency are not assumed to be constants, but modeled well in the paper. Secondly, to achieve the optimal power demand sequence, an upper power demand sequence optimization problem is formulated based on distributed model predictive control (DMPC), in which energy demand is incorporated into the cost function. Thirdly, after receiving the optimal power demand sequence, a hybrid power split optimization strategy is presented to obtain the optimal power split, in which the lower HESS power split optimization problem is formulated based on DMPC to decrease battery aging process and energy consumption. Finally, an improved particle swarm optimization algorithm with multiple dynamic populations is used to solve the formulated upper and lower optimization problems. Simulation results demonstrate that, compared with the benchmark, the proposed COPS method can significantly extend battery lifespan and slightly decrease energy consumption. [ABSTRACT FROM AUTHOR]

The Filter-Based Method (FBM) is one of the most simple and effective approaches for energy management in hybrid energy storage systems (HESS) composed of batteries and supercapacitors (SC). The FBM has evolved from its conventional form in such a manner that more flexibility and functionalities have been added. A comparative study and analysis of the most recent and relevant proposals based on the FBM for HESS are provided in this paper. In this way, the improvements for this energy management system (EMS) are in the form of adaptive filters, rules, Fuzzy logic control, sharing coefficients, and additional control loops. It is shown how these enhancements seek to avoid the premature degradation of the storage devices that are caused by deep discharge, overcharge, and fast current variations in the case of batteries and overcharge in the SC case. Therefore, the enhancements are focused on keeping the battery and SC working within safe operational limits. This paper presents new comparisons regarding the SoC evolution in the storage devices, specifically how the SC SoC is used in the EMS to establish the power sharing. Numerical simulations are added to compare the performance of the different EMS structures. The analysis of the results shows the effectiveness of the FBM in achieving power allocation and how the latest proposed improvements help to add flexibility to HESS as well as to avoid premature degradation of the storage devices. [ABSTRACT FROM AUTHOR]

Hybrid energy storage system (HESS) is an attractive solution to compensate power balance issues caused by intermittent renewable generations and pulsed power load in DC microgrids. The purpose of HESS is to ensure optimal usage of heterogeneous storage systems with different characteristics. In this context, power allocation for different energy storage units is a major concern. At the same time, the wide integration of power electronic converters in DC microgrids would possibly cause the constant power load instability issue. This paper proposes a composite model predictive control based decentralized dynamic power sharing strategy for HESS. First, a composite model predictive controller (MPC) is proposed for a system with a single ESS and constant power loads (CPLs). It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system disturbances. Then, a coordinated scheme is developed for HESS by using the proposed composite MPC with a virtual resistance droop controller for the battery system and with a virtual capacitance droop controller for the supercapacitor (SC) system. With the proposed scheme, the battery only supplies smooth power at steady state, while the SC compensates all the fast fluctuations. The proposed scheme achieves a decentralized dynamic power sharing and optimized transient performance under large variation of sources and loads. The proposed approach is verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

The four-wheel distributed drive pure electric mining truck, featuring a hybrid energy storage system with battery and supercapacitor, is a promising solution for achieving zero-emission in the transportation process of open-pit mines. The challenge of precisely coordinating and controlling key components such as battery, supercapacitor, and motor to fully unleash their energy-saving potential under complex and harsh off-road conditions is an urgent issue that needs to be addressed. This paper innovatively adopts a new perspective of minimizing global energy transfer chain losses and proposes a mining truck energy efficiency optimization control framework based on vehicle-cloud integration to exploit the energy-saving potential of mining trucks. Furthermore, the proposed control strategy is simulated and compared with the baseline control strategy based on typical mining truck operating conditions. The results indicate that the proposed control strategy effectively maximizes the vehicle's energy efficiency, resulting in an annual reduction of 356,864 RMB in the comprehensive operational cost per mining truck and increasing the energy-saving economic efficiency by 2.7%. These achievements hold promise for propelling the energy efficiency optimization of pure electric mining trucks, which bears significant implications for the construction of a clean and low-carbon open-pit mining transportation system. • New perspective in designing energy-saving control strategies for mining trucks. • Propose optimal energy efficiency control for mining trucks via vehicle-cloud integration. • Develop power control strategy utilizing model predictive control for batteries and supercapacitors. • Construct electro-hydraulic decoupled torque distribution strategy for energy management. [ABSTRACT FROM AUTHOR]

Hybrid energy storage system (HESS) is an ESS integrated with renewable energy source (RES), allowing PV owners to participate in the electricity market. By investing in HESS, PV owners can yield additional revenue by providing services to system operators, such as avoiding and delaying transmission and distribution network investment, relieving grid congestion, and providing ancillary services (AS). However, in some jurisdictions, the high installation expenses of HESS make the investment by PV owners economically unviable. In this paper, a business model is proposed to improve the investment value of HESS, and a mixed-integer linear programming (MILP) optimization problem is modelled to calculate the optimal capacity of HESS that maximizes the PV owner's profit. The simulation results of the proposed model were used to assess economic feasibility through internal rate of return (IRR) and levelized cost of energy (LCOE). Based on economic evaluations, the appropriate level of capacity incentive needed to motivate investment in HESS was identified. The simulation used the historical system marginal price (SMP) of Jeju-island, Korea, and the PV generation patterns of various regions. The Monte-Carlo method was used to obtain results that are robust to the regional and temporal uncertainty of RES. Simulation results using the 2021 SMP data indicate that at a capacity incentive level betweem 29 and 31%, the NPV, IRR, and LCOE meet the required thresholds, demonstrating the economic viability of HESS investment. In scenarios with negative pricing, HESS investment is feasible without providing a capacity incentive. The IRR derived from the simulation is above the cost of equity, and the LCOE is below the maximum LCOE of unsubsidized gas peak resources confirming that the level of capacity incentives is appropriate. • Energy storage system is a key resource for integrating renewable energy sources • Proposals of various business model for photovoltaic owner to obtain revenue from energy storage system • Determination of the optimal hybrid energy storage system capacity considering the value of grid service • Economic evaluation to establish the appropriate capacity incentive level [ABSTRACT FROM AUTHOR]

The combination of thermal power and hybrid energy storage is an effective way to improve the response ability of automatic generation control (AGC) command in thermal power plants. Notably, the configuration of hybrid energy storage capacity is directly related to improvement of the frequency modulation ability of thermal power plants and the coordination of economic benefits. However, the constant efficiency model adopted in capacity configuration will misjudge the actual operating status of each energy storage unit, resulting in unreasonable capacity allocation. In this context, a fire-storage capacity optimization configuration model considering the dynamic charge–discharge efficiency of hybrid energy storage is established. The model presents the functional relationship between charge and discharge power and the efficiency of different types of energy storage. Simulation proves that the proposed strategy can meet the tracking demand of area control error signal in thermal power plants and reduce the planning and operation cost of energy storage.

[Display omitted] • Optimal hybrid system based on AGTO, ARO, BOA, CGO, PFA, TSO. • Hybrid energy storage system with battries and supercapacitors. • Sensitivity analysis of Hybrid energy System, effect of Loss of power supply probability. • Real-time meteorological data of solar, wind and temperature were used in the optimization process. • The most suitable hybrid energy system design for hourly changing load demands was examined. This study investigates the optimization of a grid-connected hybrid energy system integrating photovoltaic (PV) and wind turbine (WT) components alongside battery and supercapacitor storage. The research addresses the critical need for efficient energy storage solutions in renewable energy integration. Six optimization algorithms—AGTO, ARO, BOA, CGO, PFA, and TSO—are evaluated for their efficacy in determining optimal system configurations. The system's adaptability to dynamic scenarios is examined through comprehensive sensitivity analyses, shedding light on its robustness. The findings reveal that the CGO algorithm outperforms others by achieving optimal solutions with fewer iterations, highlighting its efficiency. Key conclusions include the identification of an optimal configuration comprising a 589.58 kW PV system, 664 kW WT, a 675-kW supercapacitor, and a 1000 kWh battery bank. This configuration achieves an 80 % renewable energy fraction (REF), reduces the annual system cost (ACS) to $603,537.8522, and maintains a competitive levelized cost of electricity (LCOE) at $0.2380 per kWh. The research underscores the significance of integrated energy storage solutions in optimizing hybrid energy configurations, offering insights crucial for advancing sustainable energy initiatives. The study contributes valuable insights to the scientific community, paving the way for more efficient and resilient renewable energy systems. [ABSTRACT FROM AUTHOR]

Energy storage is one of the hot points of research in electrical power engineering as it is essential in power systems. It can improve power system stability, shorten energy generation environmental influence, enhance system efficiency, and also raise renewable energy source penetrations. This paper presents a comprehensive review of the most popular energy storage systems including electrical energy storage systems, electrochemical energy storage systems, mechanical energy storage systems, thermal energy storage systems, and chemical energy storage systems. More than 350 recognized published papers are handled to achieve this goal, and only 272 selected papers are introduced in this work. A comparison between each form of energy storage systems based on capacity, lifetime, capital cost, strength, weakness, and use in renewable energy systems is presented in a tabular form. Selected studies concerned with each type of energy storage system have been discussed considering challenges, energy storage devices, limitations, contribution, and the objective of each study. The integration between hybrid energy storage systems is also presented taking into account the most popular types. Hybrid energy storage system challenges and solutions introduced by published research are summarized and analyzed. A selection criteria for energy storage systems is presented to support the decision-makers in selecting the most appropriate energy storage device for their application. For enormous scale power and highly energetic storage applications, such as bulk energy, auxiliary, and transmission infrastructure services, pumped hydro storage and compressed air energy storage are currently suitable. Battery, flywheel energy storage, super capacitor, and superconducting magnetic energy storage are technically feasible for use in distribution networks. With an energy density of 620 kWh/m3, Li-ion batteries appear to be highly capable technologies for enhanced energy storage implementation in the built environment. Nonetheless, lead-acid batteries continue to offer the finest balance between price and performance because Li-ion batteries are still somewhat costly. The applications of energy storage systems have been reviewed in the last section of this paper including general applications, energy utility applications, renewable energy utilization, buildings and communities, and transportation. Finally, recent developments in energy storage systems and some associated research avenues have been discussed. Academics and engineers interested in energy storage strategies might refer to this review. • The complexity of the review is based on the analysis of 250+ Information resources. • Various types of energy storage systems are included in the review. • Technical solutions are associated with process challenges, such as the integration of energy storage systems. • Various application domains are considered. [ABSTRACT FROM AUTHOR]

The unbalanced load distribution, the single-phase connection of renewable energy, and the uncoordinated charging of electric vehicles (EVs) will bring a severe issue corresponding to the three-phase unbalance in modern distribution networks. To deal with this issue, a novel multistage optimal dispatching model with the hybrid energy storage system (HESS), consisting of the battery energy storage system (BESS), EV, and supercapacitor (SC), is proposed in this paper. The first stage is conducted on the day-ahead stage, driven by minimizing the total operation cost and relieving power unbalance, the unbalanced penalty cost is introduced into the optimal dispatching model with the electricity purchasing cost and the degradation cost of HESS. Also, the chance-constraint stochastic programming (SP) model with the coordinated operation of HESS is accommodated to handle the diverse uncertainties of renewable energy, load demand, and EV users' behaviors. In the intra-day operation stage, a rolling optimization-based dispatch model is formulated to re-optimize the operation of the SC for the rapid response of mitigating the real-time three-phase unbalance caused by renewable energy. The numerical experiments are executed on the IEEE 34-bus three-phase test system. Compared with the cases without considering SC and the degradation cost of HESS, the power unbalance in the proposed model is reduced by 3.27% and 3.62% per day at the real-time stage, respectively, while total operation cost is reduced by 46.77 US$ and 350.62 US$ per day, respectively. The results validated that the proposed model is efficient in reducing the three-phase unbalance under multi-time scales, improving the economic operation of the distribution network. • Three-phase power unbalance reduction is achieved by hybrid energy storage system. • Supercapacitor is embedded with BESS and EV in day-ahead and intra-day stages. • Multi-stage optimization scheduling model proposed to minimize power unbalance. • Rolling optimization employed in intra-day stage to address short-term unbalance. [ABSTRACT FROM AUTHOR]

• A power control scheme is proposed for distributed DFIG based on cooperation of HESS and GSC, which can effectively control the overall output power of the distributed system. And it can reduce the voltage fluctuation in the DN. • The SOC state of HESS is considered to adjust the pitch angle control and power dispatching command. This can avoid overcharging and over-discharging of the HESS to maintain long-term operation for managing output power. • The SMES with high power density is used to deal with high-frequency power by controlling the DC-link voltage of DFIG. It enhances the stability of DC-link voltage and extend the battery's service lifetime. • Finally, the model of distributed DFIG and HESS is built in RTDS platform, and the simulation results are analyzed for verifying the effectiveness and superiority of the proposed scheme. Due to the uncertainty of wind energy, the wind power is difficult to be dispatched and may cause the voltage fluctuations for distributed network. Therefore, a novel power control scheme is proposed to manage the output power of the distributed doubly-fed induction generator (DFIG) by cooperating the hybrid energy storage system (HESS). In the proposed scheme, the grid-side converter of DFIG is controlled to manage overall output power of the DFIG/HESS hybrid system in accordance to the power dispatching command. The HESS is utilized to deal with the power difference between DFIG generation power and the power dispatching command. The HESS is embedded in the DC-link bus of DFIG and is composed of superconducting magnetic energy storage and batteries. Additionally, in order to avoid HESS from overcharging and over-discharging, the pitch angle control and power dispatching command are adjusted by considering the state of charge (SOC) of HESS. Finally, the simulation model of distributed DFIG and HESS is built on the real-time digital simulation (RTDS) platform, and the simulation results validate the effectiveness and reliability of the proposed power control scheme. [ABSTRACT FROM AUTHOR]

The primary purpose of this paper is to investigate energy regeneration and conversion technologies based on mechanical–electric–hydraulic hybrid energy storage systems in vehicles. There has been renewed interest in hydraulic storage systems since evidence has been presented that shows that they have the distinct advantages of high energy output and energy recuperation compared to electrical energy recovery systems, which are widely applied in electric vehicles; however, they are known to be high-cost, with a complicated structure and not zero carbon. In this paper, we first review recent research on hydraulic energy regeneration and conversion technologies. Secondly, as the main part of this paper, the latest technological progress and breakthroughs of the mechanical–electric–hydraulic hybrid energy storage systems in vehicles—which are divided into four categories: passenger, minibus and bus, commercial vehicle and special vehicle—are analyzed and discussed in depth. In addition, the current research status of energy management techniques is presented and summarized. Finally, prospects and challenges are suggested and explained. It is evident from the literature review that the mechanical–electric–hydraulic hybrid systems perform excellently in vehicles. Clearly, this review will be helpful to understand, explore and define the hydraulic vehicle of the future concerning energy optimization and environmental friendliness.

Employment of a battery energy storage system to compensate for the generation-consumption mismatch is a vital element for a resilient microgrid. However, the frequent (i.e., short-term) cyclic activity and the abrupt current variations (i.e., high di/dt) have adverse effects on the energy-type battery’s lifespan. On the other hand, the power-type supercapacitor energy storage system (SCES) has almost zero lifetime degradation when it is subjected to irregular charging/discharging patterns. Therefore, the hybridization between them can reduce battery stress levels. This study proposes a droop control algorithm with multiple segments for a SCES/battery hybrid energy storage system (HESS) that is employed in a solar/wind DC microgrid. The presented control scheme is decentralized since it avoids the central controller (CC) and the communication links between the controllers of SCES and battery units. To eliminate the short-term cyclic operation of the battery, the SCES regulates the bus voltage inside a narrow zone with a droop curve that exploits all its available energy capacity. Inside this zone, the battery is kept inactive. As soon as the bus voltage deviates from this band, the SCES enters the idle mode, and the battery is triggered following a droop curve different for charging or discharging mode to stabilize the bus voltage. To evaluate the effectiveness of the proposed system over a battery-only system and a hybrid SCES/battery system controlled with the classical droop method, a comparative analysis under different scenarios is presented.

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy.

The performance of electric vehicle (EV) drivetrains depends on the power capability of individual components, including the battery pack, motor drive, and electric motor. To ensure safety, maximum power must be limited by considering the constraint of the weakest component in the drivetrain. While there exists a large body of work that discusses state-of-power (SoP) estimation for individual components, there is no work that considers all the components’ limiting factors at once. Moreover, research on how to use these limits to adjust the performance at the system level has been rare. In this paper, the SoPs of the components are used to estimate the state-of-function (SoF) of the EV drivetrain. The SoF is defined as the maximum charge/discharge power that can be sourced and/or sunk by the drivetrain without violating the safety limits of its components. The component-level SoP estimations are fulfilled using several digital algorithms based on recursive least-squares (RLS) and Kalman filters (KFs), as well as by taking into account specific limiting conditions such as high driving altitude and ambient temperatures. An EV driven by a hybrid energy storage system based on a battery/supercapacitor, and a permanent-magnet synchronous motor is considered the use case. Based on the drivetrain SoF estimation, we propose two de-rating schemes to ensure that the drivetrain safety limits will be respected: adaptive cruise control and adaptive adjustment of pedal sensitivity. The de-rating schemes are introduced to a so-called recommender system that is implemented in MATLAB/STATEFLOW. The recommender system provides advisory feedback to the driver to switch to a different driving mode to ensure safety. The simulation results over a standard drive cycle using MATLAB/SIMULINK and STATEFLOW show the effectiveness of the proposed design at both component and system levels. The paper also proposes an implementation concept for the integration of the proposed recommender system into the advanced driver assistance system (ASAS).

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. [Display omitted] • Energy management and trajectory tracking control are coordinated for hybrid UAV. • Energy management based on online dynamic programming is proposed. • Energy management based on hierarchical model predictive control is proposed. • Model predictive control and disturbance observer-based position tracking is proposed. [ABSTRACT FROM AUTHOR]

With greater power density, a hybrid power source that combines supercapacitors and batteries has a wide range of applications in pulse-operated power systems. In this paper, a supercapacitor/battery semi-active hybrid energy storage system (HESS) with a full current-type control strategy is presented. The studied HESS is composed of batteries, supercapacitors, and a bidirectional buck–boost converter. The converter is controlled such that supercapacitors supply load power pulses, and batteries provide the power in steady state. To realize the fast compensation of the supercapacitors to the load power pulses, a power distribution module based on hysteresis control theory is designed in the control system. Moreover, the control strategy does not require the model parameters of the converter and supercapacitors, so the control system is simplified. A complete configuration scheme and cost analysis of the proposed HESS are also presented. Obtained results show that the proposed supercapacitor/battery semi-active HESS has good performance in terms of dynamic response, weight, and energy utilization coefficient (EUC). [ABSTRACT FROM AUTHOR]

The acceptance of hybrid energy storage system (HESS) Electric vehicles (EVs) is increasing rapidly because they produce zero emissions and have a higher energy efficiency. Due to the nonlinear and strong coupling relationships between the sizing parameters of the HESS components and the control strategy parameters and EV's performances, energy consumption rate, running range and HESS cost, how to design the HESS EVs for different preferences is a key problem. How to get the real time performances from the HESS EV is a difficulty. The multiobjective optimization for the HESS EV considering the real time performances and the HESS cost is a solution. A Li-ion battery (BT) semi-active HESS and optimal energy control strategy were proposed for an EV. The multiobjectives include energy consumption over 100 km, acceleration time from 0–100 km per hour, maximum speed, running range and HESS cost of the EV. According to the degrees of impact on the multiobjectives, the scaled factors of BT capacity, the series number of Li-ion BTs, the series number of super-capacitors (SCs), the parallel number of SCs, and charge power of the SCs were chosen as the optimization variables. Two sets of different weights were used to simulate the multiobjective optimization problem in the ADVISOR software linked with MATLAB software. The simulation results show that some of the multiobjectives are sensitive to their weights. HESS EVs meeting different preferences can be designed through the weights of different objectives. Compared with the direct optimization algorithm, the genetic algorithm (GA) has a stronger optimization ability, and the single objective is more sensitive to its corresponding weight. The proposed optimization method is practical for a Li-ion BT and SC HESS EV design. [ABSTRACT FROM AUTHOR]

Conventional vehicles, having internal combustion engines, use lead-acid batteries (LABs) for starting, lighting, and ignition purposes. However, because of new additional features (i.e., enhanced electronics and start/stop functionalities) in these vehicles, LABs undergo deep discharges due to frequent engine cranking, which in turn affect their lifespan. Therefore, this research study seeks to improve LABs' performance in terms of meeting the required vehicle cold cranking current (CCC) and long lifespan. The performance improvement is achieved by hybridizing a lead-acid with a lithium-ion battery at a pack level using a fully active topology approach. This topology approach connects the individual energy storage systems to their bidirectional DC-DC converter for ease of control. Besides, a battery management strategy based on fuzzy logic and a triple-loop proportional-integral (PI) controller is implemented for these conversion systems to ensure effective current sharing between lead-acid and lithium-ion batteries. A fuzzy logic controller provides a percentage reference current needed from the battery and regulates the batteries' state-of-charge (SoC) within the desired limits. A triple-loop controller monitors and limits the hybridized system's current sharing and voltage within the required range during cycling. The hybridized system is developed and validated using Matlab/Simulink. The battery packs are developed using the battery manufacturers' data sheets. The results of the research, compared with a single LAB, show that by controlling the current flow and maintaining the SoC within the desired limits, the hybrid energy storage system can meet the desired vehicle cold cranking current at a reduced weight. Furthermore, the lead-acid battery lifespan based on a fatigue cycle-model is improved from two years to 8.5 years, thus improving its performance in terms of long lifespan. [ABSTRACT FROM AUTHOR]

This study proposes an efficient estimator and uses it to estimate the health of a lithium-ion battery and a supercapacitor in the hybrid energy storage system (HESS). A new type of online health estimator that uses a fuzzy brain emotional learning neural network (FBELNN) is proposed. This neural network is different to a conventional brain emotional learning neural network where the fuzzy inference system and a new reward signal are used. The effect capacity fading on the output of energy storage components is also determined. The proposed method uses a discrete wavelet transform (DWT) and principal component analysis (PCA) to extract features from the response signal for the impulse load. The DWT-PCA can reduce the workload for feature extraction. The parameter adaptation laws and convergence analysis for the FBELNN are derived and the internal parameters for the FBELNN are optimized using a genetic algorithm (GA). A neural network estimates the capacity of a supercapacitor and lithium-ion battery in real-time to better ensure the safety of HESS. The sample set is collected from the voltage response signal in the HESS simulation platform and practical experimental platform. Simulation and experimental results show that the proposed method has a faster learning speed and is more accurate than other methods. [ABSTRACT FROM AUTHOR]

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In the last years, renewable energy (RE) is increasing widely in the energy sector, and microgrid technology is overgrowing. In this paper, stand-alone microgrid using solar photovoltaic (PV) energy as a source of renewable energy is simulated to provide power for direct current (DC) loads with hybrid energy storage system (HESS) which consists of battery and supercapacitor bank. The proposed microgrid system is tested under various cases of load and variable irradiance to confirm and validate the proposed management strategy to remain the DC bus voltage within a stable limit. The performance of DC microgrid is comparing with and without supercapacitor (SC) bank and notes a desirable decrease in the magnitude of transient voltage when using HESS. The sun power SPR-E19-320 standard was simulated to analyze system performance taking into account the constant load demand. Note that HESS helps reduce transient of DC voltage very effectively in all situations. Very large transients arise due to sudden changes in load demand is also compensated by HESS. The results obtained indicate that the stand-alone DC microgrid with HESS is very beneficial for reducing transient of DC-link voltage that occurs due to sudden change in load or fault. The proposed system is performed by MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of new optimal control algorithms for battery-supercapacitor hybrid energy storage system (HESS) with input saturation and time delay. Different from the existing research results, a new state feedback system to track the output DC bus voltage and battery current is developed via the state-space averaged model method. By fully considering the state delay information, a proper Lyapunov-Krasovskii functional (LKF) is constructed. In order to reduce the inherent conservation, Wirtinger-based integral inequality is firstly employed to establish novel stability criteria of battery-supercapacitor HESS. Meanwhile, for the sake of shortening the transition time and realizing the smooth transition of some key variables during the load switch, new optimal control algorithms with actuator saturation can be achieved in terms of linear matrix inequalities (LMIs). Finally, numerical results are presented to verify the effectiveness and superiority of the analysis results.

The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). This paper investigates the improvements of the frequency response of fully decoupled wind power generators (FDWG) by proposing a novel generic model implementation of ultracapacitors (UC) within a hybrid scheme in real-time simulations of wind power plants. UCs are selected as ideal power sources in fast active power-frequency control due to their high power density and fast-reacting speed. Batteries and UCs combined hybrid energy storage systems (HESS) are formed to complement their characteristics. Droop-based and frequency derivative-based control and virtual synchronous power (VSP) are the selected control strategies to support power system frequency stability. The best trade-off between frequency performance and HESS cost is found by solving a proposed optimization problem formulation. The proposed optimization problem is used to define the HESS size and the controller parameters. The optimization results show how the fast active power-frequency response is enhanced by the fast UC power injection. It also shown that VSP leads to faster frequency support than the droop-based control and the frequency derivative control.

The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.

Lithium-ion (Li-ion) batteries are still the best technology to power the Electric Vehicle (EV), due to their high power and energy density. However, the use of these batteries can be limited in cars with a high demand for peak power and very high energy density. One way to improve the performance of the Li-ion battery and reduce its weight is to associate this battery with another technology of higher specific energy as a second energy source, e.g. Lithium-sulfur (Li-S). The development of Hybrid Energy Storage Systems (HESSs) is a promising solution optimizing the energy management of EVs. In this paper, we present experimental results obtained with a high specific energy and power capability HESS prototype, composed of i) a Lithium-Titanate-Oxide battery to ensure high power capabilities, ii) a Li-S battery to improve specific energy, and iii) a power converter based on Gallium Nitride (GaN) devices to link both battery modules, minimizing at the same time system weight, volume and power losses. The developed GaN-based power converter achieves high efficiency (96.5%) operating at 300 kHz with a reduced size (0.4 L). Besides, the behavior of the developed HESS prototype is experimentally evaluated under standard automotive profiles, for different driving scenarios. The HESS prototype achieves an increase of energy density and specific energy of +5.56% and +28.21%, respectively, compared to a battery system composed only of Li-ion cells. The limitation of the developed system for automotive application are highlighted, and the critical research needs are clearly identified to accelerate the implementation of such systems on commercial EV solutions.

Purpose – A standalone microgrid (MG) is able to use local renewable resources and reduce the loss in long distance transmission. But the single-phase device in a standalone MG can cause the voltage unbalance condition and additional power loss that reduces the cycle life of battery. This paper proposes an energy management strategy for the battery/supercapacitor (SC) hybrid energy storage system (HESS) to improve the transient performance of bus voltage under unbalanced load condition in a standalone AC microgrid (MG). Design/methodology/approach – The SC has high power density and much more cycling times than battery and thus to be controlled to absorb the transient and unbalanced active power as well as the reactive power under unbalanced condition. Under the proposed energy management design, the battery only needs to generate balanced power to balance the steady state power demand. The energy management strategy for battery/SC HESS in a standalone AC MG is validated in simulation study using PSCAD/EMTDC. Findings – The results show that the energy management strategy of HESS maintains the bus voltage and eliminates the unbalance condition under single-phase load. In addition, with the SC to absorb the reactive power and unbalanced active power, the unnecessary power loss in battery is reduced with shown less accumulate depth of discharge and higher average efficiency. Originality/value – With this technology, the service life of the HESS can be extended and the total cost can be reduced.

Due to weather, seasons, and other factors, photovoltaic power generation cannot be predicted accurately. A Hybrid energy storage system can effectively reduce the impact of unbalanced power generated by this situation. In this paper, retired power batteries and super capacitors are used to form an energy storage system. A community system model is established. An improved lumped average empirical mode decomposition algorithm is used to decompose unbalanced power. Hilbert transform is used to obtain the reference boundary frequency of the energy storage system. Taking the minimum community investment cost as the objective function, a dimensional gravity center reverse mutation particle swarm optimization algorithm is used to solve the problem. The capacity allocation with good investment economy is determined. Two cases of conventional battery energy storage and retired power batteries are analyzed through numerical simulation. The results show that the hybrid energy storage system based on retired power batteries proposed in this paper can reduce investment and has a better economy.

The hybrid energy storage system (HESS) is composed of lithium-ion battery packs and supercapacitors (SCs), it can better stabilize the output of photovoltaic (PV) in the microgrid. The wavelet packet analysis method is used to decompose the output power of PV, and the power components of different frequency bands can be obtained. However, the traditional wavelet packet decomposition method has a fixed number of decomposition layers and cannot track changes in the output power of PV in real time. This paper proposes an improved wavelet packet decomposition method: considering the fluctuation of solar cell output power and the current working status of the HESS, the fuzzy control algorithm is used to adjust the number of wavelet packet layers in real-time to realize the adaptive process of power fluctuation in the stable process, and taking into account the state of charge of lithium-ion batteries and SCs, fuzzy control is used to allocate their secondary output power to improve the stability effect. Experiments show that compared with the traditional wavelet packet-based control method, the control method proposed in this paper improves the control effect by 4.5%. [ABSTRACT FROM AUTHOR]

To solve the problem of battery capacity degradation caused by high current magnitudes and frequent current variations in electric vehicles (EVs), a hybrid energy storage system (HESS) incorporating high energy density storage (battery) and high power density storage (ultracapacitor) is proposed. The HESS contains a multi‐port DC‐DC converter, which controls the energy flow among the battery pack, the ultracapacitor (UC) pack, and the port of output. Considering the state of charge (SOC) of UC, the speed, and demand power of EV, an energy management strategy (EMS) based on fuzzy logic control (FLC) for the HESS is proposed in this paper. According to the strategy, the battery pack serves as a base power source while the UC pack serves as a transient power source so as to smooth the battery pack current and prolong the battery life. Simulation and experiment results illustrate the effectiveness of the proposed strategy for UC/battery HESS in EVs. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the increase in new energy power generation and the continuous augment in the penetration rate of electric vehicles, it is of crucial importance to use electric vehicles as energy storage devices to promote the consumption of new energy. Aiming at the uncertainty of electric vehicles, this paper proposes a method based on multiobjective optimization for electric vehicle‐supercapacitor hybrid energy storage system to track PV project output. The hybrid system consists of electric vehicles and supercapacitor. Electric vehicles and supercapacitors supplement the deviation of PV actual power and predicted power by charging and discharging. The electric vehicle is regarded as a nonspecific way to label a piece of equipment that can store energy. First and foremost, on the basis of traditional method of predicting PV output, a PSO‐BP prediction method based on PCA is proposed to improve the accuracy of PV output prediction. Secondarily, according to the different characteristics of electric vehicles and supercapacitors, the empirical mode decomposition (EMD) method is used to decompose the deviation that the hybrid energy storage system needs to bear with the purpose of initially allocating the energy. Furthermore, a multiobjective optimization model is established for the precise energy distribution of the hybrid energy storage system, and the NSGA‐III algorithm is used to solve it. Ultimately, the data of a domestic PV power station are used for simulation. After optimized control, the result shows that the standard deviation of the system output is reduced from 1967 to 75.77. The research in this article provides a theoretical basis for the application of electric vehicle virtual energy storage technology in the field of auxiliary new energy grid connection. [ABSTRACT FROM AUTHOR]

The widespread use of internal combustion engines (ICEs) to power vehicles has serious negative consequences for the environment and human health. Therefore, for three decades, changes in the automotive industry have been observed aimed at total or partial electrification of vehicle drive systems. The effect of these activities are successively introduced to the consumer market vehicles with hybrid (HEV) and electric (EV) drives. This article proposes an original version of the energy management system in a hybrid vehicle. The capabilities of a hybrid energy storage system (HESS) consisting of batteries and ultracapacitors units were explored through simulation. The most important parameters of the proposed strategy have been optimized. The simulation tests conducted confirmed many benefits of using an additional energy source. The applied changes in the energy management system reduced the level of fuel consumption, the maximum temperature, and the value of the charging and discharging currents of the batteries. This makes it possible to extend the service life of primary energy storage and reduce operating costs. It also has a positive effect on the environment, not only by reducing the emission of harmful effects of combustion but also by reducing the costs of disposal of used batteries. [ABSTRACT FROM AUTHOR]

In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion battery and supercapacitor (SC) as the EV's energy sources, known as a hybrid energy storage system (HESS), is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from the vehicular network could not directly be used to improve HESS, a two-level control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in level two control structure. The ANN provides a direct prediction that could approximate the future power demand prediction with the assumption that the vehicular network scenario is deployed to obtain surrounding information. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3%, respectively, compared to the battery-only case. [ABSTRACT FROM AUTHOR]

Battery/Ultracapacitor (UC) Hybrid Energy Storage Systems (HESS) for Electric Vehicles (EVs) have been frequently proposed in the literature to increase battery cycle life. The HESS consists of a Power Management Strategy (PMS) and an Energy Management Strategy (EMS). Existing EMS are quite empirical, such as setting constant target UC energy levels regardless of load. This work presents an improved complete HESS management strategy. The EMS involves a more comprehensive method of setting the target UC energy level using a speed-dependent band. This allows the UC to achieve two goals – contain sufficient energy for future accelerations and have sufficient space for capturing energy from future regenerative braking – without knowledge of the future drive profile. The PMS involves a speed-dependent battery power limit, which also achieves two goals – better UC utilization and allowing the battery to supply the steady state power. Simulations show existing works cannot achieve the four goals simultaneously unless their UCs are sized twice as large compared to the proposed rule-based HESS. In addition, the proposed HESS extends battery cycle life by up to 42% compared to a battery-only system. Lastly, a reduced-scale experiment was built to show that the proposed HESS is able to run in real-time. [ABSTRACT FROM AUTHOR]

To satisfy the high-rate power demand fluctuations in the complicated driving cycle, electric vehicle (EV) energy storage systems should have both high power density and high energy density. In order to obtain better energy and power performances, a combination of battery and supercapacitor are utilized in this work to form a semi-active hybrid energy storage system (HESS). A parameter matching method of battery-supercapacitor HESS for electric vehicles (EVs) is proposed. This method can meet the performance indicators of EVs in terms of power and energy for parameter matching. The result shows that optimized parameter matching is obtained by reducing the weight and cost. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the costs and specifications of 100% VRE (Variable Renewable Energy) systems that consist of VRE and energy storage systems. For that purpose, a model was developed to comprehensively analyze the costs and the specifications of 100% VRE systems. Using the model, the following results were obtained: (1) A hybrid storage system of battery and hydrogen storage that are optimally combined, as well as the optimal combination of solar and wind power, could reduce supply costs of electric energy. Concerning the hybrid storage system, the battery that operates with a daily cycle cooperates with the hydrogen storage that operates with a multiple day cycle, and; (2) multi-production systems that supply electric energy as well as hydrogen energy could reduce energy supply costs when an optimal combination of electric energy and hydrogen energy is achieved. However, the supply costs of stable electric energy as well as hydrogen energy from the 100% VRE systems are more expensive than the target costs in 2030 in Japan. In order to reduce the energy supply costs, it is necessary to reduce the costs of elemental technologies such as solar, wind power, battery, and hydrogen-related technologies. • A model was developed to minimize stable energy supply costs from 100% VRE systems. • Hybrid battery and hydrogen storage systems could reduce electric energy costs. • Systems supplying electric and hydrogen energy could reduce energy supplies costs. [ABSTRACT FROM AUTHOR]

The dual inverter topology driving an open-winding motor is well known in high voltage motor drive applications. This structure allows two energy sources to be directly connected to an open-winding motor. This enables the integration of supercapacitors into a battery electric vehicle (EV). Unlike existing solutions, this article demonstrates dynamic power sharing between the dual energy sources by controlling the active and reactive voltages of the twin inverters, thus enabling the use of the supercapacitor for either active power assist and/or reactive power assist. The dedicated vector-controlled power sharing method and energy management is shown to achieve power sharing in the dual inverter drive integrating a battery and supercapacitor, thereby eliminating the need for an additional cascaded dc/dc converter to extract/supply energy to the supercapacitor. It also enables improved efficiency by eliminating switching losses in the supercapacitor inverter during low power operation. The proposed voltage vector splitting method is also shown to achieve battery-to-supercapacitor power exchange for regulating the net energy in the supercapacitor without affecting motor operation. A laboratory prototype utilizing a 110-kW liquid-cooled EV motor and supercapacitor bank is developed to verify the practical implementation of the power and energy management strategy. [ABSTRACT FROM AUTHOR]