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The energy management strategy (EMS) is a decision-making algorithm for effective power allocation between storage devices in a hybrid energy storage system (HESS). Source voltages, state of charge (SOC), the terminal voltage of the load, and the rate of change in the battery current must be considered while implementing the EMS and, hence, they are termed as performance indicators. This research work focuses on the development of an EMS, designed to manage the performance indicators of the sources (terminal voltage and battery current rate) and ensure efficient power distribution through a shared bus topology. A shared bus topology employs individual converters for each source, offering efficient control over these sources. Rule-based fuzzy logic control ensures efficient power distribution between batteries and ultracapacitors. Additionally, hardware has been developed to validate the power allocation strategy and regulate the DC-link voltage in the energy management system (EMS). dSPACE MicroLabBox is utilized for the implementation of real-time control strategies. A battery and an ultracapacitor bank are utilized in a hybrid energy storage system. The simulation outcomes have been corroborated by experimental data, affirming the efficacy of the proposed energy management strategy. The proposed EMS achieves a 2.1% battery energy saving compared to a conventional battery electric vehicle over a 25 s duration under the same load conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT This article deals with the modeling and control of a solid‐state transformer (SST) based on a dual active bridge (DAB) and modular multilevel converter (MMC) for integrating solar photovoltaic (SPV) and battery energy storage (BES) systems into the grid. SST uses DABs for bidirectional DC‐DC conversion and an MMC for DC‐AC conversion. A hybrid control method is developed in this study, which combines proportional‐integral (PI) controllers and model predictive control (MPC) to achieve the multiple objectives of the SST. The DABs' control system uses PI controllers for power extraction from the SPV system and to regulate the charging and discharging of the BES according to power generation and demand fluctuations. MPC is applied to control the active power injection, regulate the DC‐link and sub‐module capacitor voltages of the MMC. Moreover, the developed hybrid control method ensures reliable SST performance even under adverse conditions like grid voltage distortion, unbalance, and frequency variations. An energy management strategy is developed based on total power generation, reference active power injection, and the state of charge of the BES. This strategy ensures accurate reference power injection to the grid despite dynamic changes in operating conditions. The article also presents a detailed mathematical model of the DAB and MMC components, and the stability of the control algorithm is analyzed theoretically. The effectiveness of the SST and the proposed hybrid control scheme is demonstrated through MATLAB/Simulink simulations and hardware‐in‐the‐loop experimental results under various operating conditions. [ABSTRACT FROM AUTHOR]

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems. [ABSTRACT FROM AUTHOR]

This paper investigates microgrid systems characterized by the coexistence of discrete events and continuous events, a typical hybrid system. By selecting the charging and discharging processes of the energy storage unit as logical variables, a mixed logical dynamic (MLD) model for the microgrid in islanded mode is established. Based on this model, model predictive control (MPC) theory is employed to optimize the energy management strategy, aiming to stabilize the DC bus voltage of the photovoltaic (PV) unit and minimize the switching frequency of the energy storage unit's charging and discharging processes during system operation. [ABSTRACT FROM AUTHOR]

The energy management system of a fuel cell electric robot should be highly responsive to provide the required power for various tactical operations, navigation of different routes, and acceleration. This paper presents a new multi-level online energy management strategy for a fuel cell electric robot based on the proposed functions of equivalent hydrogen fuel value evaluation, classification of the battery state of charge via the squared combined efficiency function, identification of the robot maneuver condition based on the proposed operation state of robot function, improvement of the overall energy efficiency based on the proposed function of the battery overcharge control, and separation of the functional points of the fuel cell based on the operational mode control strategy. The simulation study of the proposed online multi-level energy management strategy was carried out with MATLAB R2018b software to verify its superiority by comparing with other strategies. The results indicate a reduction in hydrogen consumption, reduction in fuel cell power fluctuations, prevention of battery overcharging, and incrementation in the total energy efficiency of energy storage systems compared to other energy management strategies. [ABSTRACT FROM AUTHOR]

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems. [ABSTRACT FROM AUTHOR]

Continuous efforts to preserve the environment and to reduce gaseous emissions due to the massive growth of urban economic development and heightened concerns over crude oil depletion have accelerated researchers to find long‐term solutions, particularly in the transportation sector with the focus on powertrain electrification. This article delivers a comprehensive overview of electric vehicle architectures, energy storage systems, and motor traction power. Subsequently, it emphasizes different charge equalization methodologies of the energy storage system. This work's contribution can be identified in two points: first, providing an overview of different energy management methods to researchers and scholars. Second, to highlight the state‐of‐the‐art leanings in major components and to highlight promising approaches to hybrid electric vehicle future development. [ABSTRACT FROM AUTHOR]

Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners' net income by 1.25 times compared to merely installing charging infrastructure. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been regarded as one of the promising alternatives to zero-emission transportation. New EV registrations have increased as a result of government policies and consumers' awareness of climate change. Moreover, EV technology is being improved through ongoing research and development efforts. Among these, a powertrain with a combination of two electric motors has been proposed for high driving and efficiency performance. The study presents a comprehensive state-of-the-art review of architectures and energy distribution for a dual-motor equipped with dual-source EV system. In detail, various dual-motor configurations, and energy management strategies (EMSs) used in the literature are investigated and categorized. A comparison of the benefits and drawbacks of existing topologies and the EMSs of hybrid energy storage systems (HESSs) is also discussed. Following that, research gaps have been considered. This study can be used as a reference for researchers who are interested in the design and optimal control of the dual-motor dual-source EVs. [ABSTRACT FROM AUTHOR]

Summary: The design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures. [ABSTRACT FROM AUTHOR]

High-speed railways generate a large amount of regenerative braking energy during operation but this energy is not utilized efficiently. In order to realize the recycling of regenerative braking energy of high-speed railways, the hybrid energy storage type railway power conditioner (RPC) system is proposed. The working principle and the control strategy of the system are studied. The energy management strategy consisting of a hybrid energy storage system charging and discharging strategy and variational modal decomposition (VMD) power allocation strategy is proposed. Three system operation modes are proposed: the power of the hybrid energy storage system is decomposed by VMD and an interrelationship number is proposed to determine the lithium battery and supercapacitor power. The hardware-in-the-loop test experiments are conducted by the StarSim power electronics small-step real-time simulator from Modeling Tech and the validation analysis is carried out on MATLAB/Simulink with the actual measurement data of a traction substation on the Lanzhou–Xinjiang line. The results verify that the proposed strategy can effectively recycle the regenerative braking energy, realize the peak-shaving effect on the load, and reduce the energy consumption of the train. [ABSTRACT FROM AUTHOR]

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The integration of PV and energy storage systems (ESS) into buildings is a recent trend. By optimizing the component sizes and operation modes of PV-ESS systems, the system can better mitigate the intermittent nature of PV output. Although various methods have been proposed to optimize component size and achieve online energy management in PV-ESS systems, the optimal interconnection between them has received less attention. In order to maximize the effectiveness of systems with limited component sizes and address the impact of uncertainty on the system, an optimization framework is proposed for determining the optimal size of the PV-ESS system. The proposed framework consists of five parts: determination of optimal size, analysis of component output characteristics, system state prediction, parameter calibration of energy management strategies, and update of system components output features, and it considers uncertain factors, including climate, different components, and battery degradation caused by irregular charging and discharging, to establish the model for energy saving. To validate the results, four different climates in a year were considered. The obtained results indicate that the proposed framework can effectively achieve the optimal working state of the system, realizing a matching degree of 94.55% between the offline size optimization and online management strategy. The proposed framework's universality and effectiveness were demonstrated through simulation analysis across four cities with different climates in China. [ABSTRACT FROM AUTHOR]

The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV's wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation coupled with proton exchange membrane (PEM) water electrolysis favors improving the solar energy utilization and producing green hydrogen. But few systems proposed focus on achieving all-day stable hydrogen production, which is important for the future large-scale hydrogen utilization. Herein, a PV-Battery-PEM water electrolysis system for hydrogen production was constructed. An energy management strategy (EMS) was proposed to achieve the goal of all-day stable hydrogen production, improve energy utilization efficiency and reduce light discard rate. The PV power generation system, battery system and PEM electrolyzer for hydrogen production system were first established by Matlab/Simulink platform. Then, the overall PV-Battery-PEM electrolyzer system for hydrogen production was constructed, and the effectiveness of EMS was verified. The energy efficiency of the system under different working conditions with and without battery for energy storage was analyzed. The results show that the proposed energy management strategy can meet the purpose of all-day stable hydrogen production. Under the same working conditions, the energy efficiency of the system with battery for energy storage increases by 2–4% compared with the system without battery for energy storage, which indicates that the addition of energy storage can improve the energy utilization and reduce the light discarding. [Display omitted] [ABSTRACT FROM AUTHOR]

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A frequency-decoupling-based power split was used in this study to manage a direct-current microgrid (DC-MG)-based PV and hybridized energy storage system (HESS), which consisted of a battery and a supercapacitor. The HESS control integrated a dual-loop structure for bus voltage regulation and recovery and HESS charge/discharge control. Hysteresis current control (HCC) tracked the supercapacitor setpoint current using low-pass filtering (LPF) control. The system reliability was assessed using pulsing, smooth, and disturbed PV and load waveforms with standard and advanced LPF methods. The Matlab simulation results confirmed a superior HESS control performance under varying resource waveforms. Key performance indexes (KPIs) were used to evaluate the bus voltage, HESS current management, loading efficiency, and loading mismatch. [ABSTRACT FROM AUTHOR]

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration. [ABSTRACT FROM AUTHOR]

Predictable and unpredictable variations are two major causes of renewable energy-related intermittency. Such a drawback could be overcome by applying new energy management strategies, particularly storage systems. This paper investigates the feasibility of introducing a photovoltaic management string, enabling the maintenance of continuous energy supply storage capacity. Accordingly, the PV system is liable to perform at maximum efficiency following the implementation of an MPPT-controlled DC/DC boost converter of the Perturb and Observe (P&O) type. Most of the energy-management approaches target goals lie mainly in optimizing the energy resources' operational and storage capacities. The current MATLAB/Simulink-based simulation study achieved results that turned out to testify well to the advanced design's high performance and efficiency in maintaining the load's energy autonomy while increasing the battery's life span. [ABSTRACT FROM AUTHOR]

For years, developing energy management strategies (EMS) for hybrid energy storage systems (HESS) of electric vehicles (EV) has been a topic of great interest thanks to the mutual support of energy sources. In this paper, we approach the energy management problems from the control point of view to exploit the remarkable advantages of control techniques in treating state constraints, system stability, and optimality. By that, we propose a sliding-mode strategy for the EMS of the battery–supercapacitor HESS on EVs. In order to prolong the lifespan of the battery, the rate of change in battery reference current is directly handled as the control input of the management system which is, to our best knowledge, novel in literature. Control parameters of the proposed EMS are optimally tuned by using Particle Swarm Optimization. The performance of the proposed EMS is validated by off-line simulation as well as real-time experiments on a Signal Hardware-in-the-Loop system with various comparisons, testing scenarios, and quality indices. The results and the approach of the paper illustrate the effectiveness and feasibility of the management system that can be applied not only to EVs but also to larger-scale energy networks in further research. • A novel approach to the energy management problem from the control aspect viewpoint • The nonlinear sliding-mode-based management strategy for HESS of EVs is proposed • The rate of change of battery current is directly used as a control variable • The performance of the proposed EMS is shown by various quality indices • The proposed EMS can feasibly realized on low-cost real-time platforms [ABSTRACT FROM AUTHOR]

The complexity and nonlinearity of components in large-scale thermal facilities have resulted in a lack of recognized energy management models, and simple rule-based energy management strategies are still the main approach, which reduces their operating efficiency. In this study, a dynamic programming (DP) method for globally optimal power distribution and operation mode decision for two-by-one combined cycle gas turbine is proposed. First, the energy management model of system is established. Then the drum pressure of the heat recovery steam generator, which indicates the thermal energy storage of the system, is chosen as the state variable, while the control variables are the gas turbine power, turbine power and operation mode. In addition, the system response time is considered to re-evaluated the mode switch command. The simulation results show that the DP optimizes the thermal storage management, which allows the gas turbine to run in the high-efficiency operating range for a longer time. The DP-based strategy saves 6.25 %, 5.89 %, and 4.92 % of fuel at initial drum pressure 8 MPa, 9 MPa, and 10 MPa, respectively, compared to the rule-based strategy. The results of this study can be used as a benchmark to evaluate online energy management strategies in future work. • An energy management model of 2 × 1 CCGT unit is established. • The state transfer equations are different for different unit's operation modes. • The response time of the thermal facility is considered in the EMS. • DP is used to obtain the optimal mode switching point and the power distribution. • The optimal value of EMS can be well tracked by the control system. [ABSTRACT FROM AUTHOR]

For more efficient, reliable, and stable energy provision, energy storage plays a key role in the transition towards renewable energy sources. Compressed air energy storage (CAES) has been recognized as one of the most promising technology due to its high energy capacity, flexibility, scalability, long lifespan, maintainability, economical, and environmental viability. These potentials can be further improved by hybridizing CAES systems with thermal energy storage system. However, to realize the potentials of hybrid CAES systems, a control strategy is essential to manage the energy flow between the system components. Therefore, in this work, a novel energy management strategy is proposed to control a hybrid CAES system for a prototype of a partially floating photovoltaic plant (PFPV). The proposed control strategy is based on the rule-based approach and a mathematical model is presented to evaluate the system performance. The results indicate that, for an average hourly profile of the 5 kW PFPV platform through the year, a system round-trip efficiency of 34.1% can be obtained while the cycle and exergy efficiencies are 37.7% and 41%, respectively. Higher efficiency can be obtained by controlling the compressors operational range and rated power. Therefore, future work includes experimental work for results validation and optimization. • A novel rule-based energy management strategy for hybrid CAES system is proposed. • Energy and exergy analyses are performed to understand the system characteristics. • Power distribution ratio of hybrid CAES systems is crucial to the performance. • Operating range of variable speed compressors alters the performance significantly. [ABSTRACT FROM AUTHOR]

As the mobile energy storage and load-side demand response device, energy dispatch potential of electric vehicles (EVs) in energy supply system is yet to be fully explored. This study presented a multi-energy incorporating integrated energy system (IES) with carbon capture system and power-to-gas for residential building and EVs, five optimal scheduling strategies considering EVs charging/discharging, co-dispatch of EVs and IES, and stationary battery characteristic are proposed. Optimization objectives, such as annual total cost (ATC), annual carbon emission (ACE) and total grid interaction (TGI), considering configuration and operation scheduling for IES under five scenarios can be determined through augmented ε-constraint and entropy weight-TOPSIS method, furthermore the impacts of carbon tax, battery degradation cost and the number of EVs on system performances are also studied. The results indicated that optimal ATC of IES is 6361.8 thousand yuan in Case 4 considering EVs ordered charging/discharging, optimum ACE and TGI are 4711.5 t and 2850.7 MWh in Case 5 with EVs ordered charging/discharging and state of fixed battery. Peak values and durations for charging/discharging electricity of fixed battery decrease in Case 4 and 5. This research offers a innovative idea for optimal design and operational management of integrated energy system with EVs orderly charging/discharging. [ABSTRACT FROM AUTHOR]

• An EMS integrating future road information and cabin comfort control is proposed. • Quantified the impact of multi-source information fusion and ACS control on EMS. • Comprehensive control of cabin comfort and fuel cell/battery durability is achieved. • Enhanced FCB energy efficiency via onboard sensors and vehicle-cloud infrastructure. Conventional energy management strategy (EMS) for fuel cell vehicles (FCVs) aims to optimize powertrain energy consumption while ignoring the air conditioning regulation, whereby the overall energy efficiency cannot be optimal. To enhance the cabin-powertrain holistic energy utilization without compromising energy storage system degradation and passenger temperature comfort, this paper proposes a novel energy management paradigm. The comprehensive control of cabin comfort and fuel cell/battery durability is achieved by comprehensively utilizing onboard sensors and vehicle-cloud infrastructure. Specifically, the vehicle energy- and thermal-coupled control problem is formulated by considering energy consumption, component ageing, and cabin's dynamic thermal model. In addition to regular state space in energy management problems, future road information and environmental temperature are innovatively integrated into the energy management framework. A twin delayed deep deterministic policy gradient algorithm is used to solve the problem to enhance the overall energy efficiency. Simulation results indicate that, compared with rule-based EMSs, the proposed strategy achieves cabin comfort while extending the battery life by at least 3.79 % and reducing the overall vehicle operating cost by at least 2.71 %. [ABSTRACT FROM AUTHOR]

The growing concern on climate change and environmental pollution has created enormous pressure on shifting the generation toward cleaner and environmental friendly sources. Among different clean energy producers, wind power and solar photovoltaic (PV) systems are the most attractive ones due to their abundant availability, substantial power production capability, and cost effective attributes. But, their intermittent availability makes the system operation extremely vulnerable. Therefore, optimum utilization of renewable energy in hybrid power system (HPS) may not be achievable without the integration of energy storage facilities. In this context, integration of battery energy storage (BES) systems with Wind+PV+Thermal (WPT) hybrid configuration is considered in this work to meet the sudden and short term disharmony between actual and available renewable power output. Moreover, to maintain a secure system voltage of the WPT+BES system, the impact of reactive power (Q) capability of doubly fed induction generator has also been analyzed. The scheduling problem of the proposed HPS is solved by formulating in an optimization framework. Intelligent technique is applied to evaluate the optimal operational paradigm at multiple percentage of renewable energy penetration scenarios at which the proposed HPS may operate in a voltage secure and cost effective manner. The validation of the proposed operating strategy on the comparative performance evaluation of WPT and WPT+BES systems is portrayed on IEEE30 bus benchmark power system configuration. [ABSTRACT FROM AUTHOR]

This paper presents a simulation of an insulated micro-grid system based on the three-level converters control for energy management. Different renewable power sources (wind turbine and Photovoltaic (PV) energy systems) are used to energize the micro-grid. However, a battery energy storage system (BESS) and a variable diesel generator are also used to improve the reliability of the system. The contribution of this research is focused on the power control method based on improving the quality of energy transfer, mastering dynamic interactions and maximum energy production from renewable energies to reduce the fuel consumption by the diesel. Firstly, the proposed control model for each renewable energy was carried out through simulation in the environments of Matlab and Simulink to test the robustness and performance. The second part of this research is dedicated to managing the sharing of power between load, generators, and storage systems by extracting the references of power. The three-level PWM rectifiers for variable speed diesel generators was used to maintain and control the DC bus voltage of the isolated micro-grid. The results obtained from simulations show a good correlation between static and dynamic systems even for fluctuating sun power and wind speed. [ABSTRACT FROM AUTHOR]

Voltage fluctuation and power mismatch which are caused by the decentralization, randomness, and intermittence of distributed energy resource along with load variability cause severe impacts on the security and quality of power network operation. Aiming at alleviating this issue, the structure of an AC/DC hybrid microgrid based on solid-state transformer is presented in this paper. A proper control coordination is developed to guarantee stable and reliable operation of the system. An energy management strategy is proposed to coordinate the power flow among the solid-state transformer, AC microgrid, DC microgrid and energy storage and to effectively suppress the fluctuation of the DC bus voltage. A novel adaptive droop control for the energy storage is proposed to prolong the supercapacitor life and achieve an optimum economic benefit to consumers. The droop coefficient is obtained by fuzzy logic controller of which the state of charge of the supercapacitor and unit-time electricity charge are assumed to be the input parameters. Simulation attest the feasibility of the proposed control coordination and energy management strategy.

• Day-ahead EMS considering multiple operation modes can balance the requirements of operating cost and power quality. • The existence of forecast errors will bring adverse effects on the economy of EMS. • When supercapacitor is at the safe operating boundary, the adjustment effect of the hybrid energy storage system is weak. • Intraday REMS can reduce the impact of forecast errors on operation economy. • The power distribution strategy of hybrid energy storage system can improve its regulation effect in the whole cycle. Hybrid energy storage system (HESS) can stabilize renewable energy power generation, but unreasonable energy storage power distribution and photovoltaic-load forecast errors will affect the economic benefits of the whole system. Aiming at the microgrid (MG) composed of photovoltaic (PV) and HESS, an energy management strategy (EMS) of MG considering forecast errors is proposed. Firstly, an optimization model considering the depreciation cost of battery is established. Secondly, day-ahead EMS is implemented under multiple operating modes considering minimum fluctuation and optimal economy. Then, according to the real-time forecast results and the feedback of system operation status, the intraday rolling energy management strategy (REMS) is developed to alleviate the impact of forecast errors. Finally, the real-time state of charge (SOC) of the supercapacitor (SC) is introduced to adjust the filter coefficient, which avoids the SC working in the charge/discharge restricted area for a long time and improves the adjustment effect. The results of the case analysis show that the proposed intraday REMS can effectively reduce the influence of forecast errors on energy management. [ABSTRACT FROM AUTHOR]

• A photovoltaic integrated building cooling system with energy storage is proposed. • Battery and ice-storage tank balance the storage benefits of power and cold. • A two-layer coordinated optimization method under multiple uncertainties is proposed. • A scenario analysis method is constructed to analyze source-load uncertainties. • The influences of CO 2 emission cost on system performance are compared. Solar photovoltaic integration into buildings provides an effective and attractive method to decrease fossil energy consumption and reduce greenhouse gases emission. This study proposes a flexible photovoltaic integrated building cooling system with electricity and ice storages to address the challenges posed by the instability of solar energy and the integration and coordination of multiple components. Uncertainties in solar irradiance, ambient temperature, and cooling load are characterized using a scenario analysis method that combines Latin Hypercube Sampling and the K-Means++ algorithm. Then, aiming to achieve the minimal total cost, including investment, operational, and CO 2 emission costs, a two-layer stochastic optimization model is developed to simultaneously determine the optimal capacities and operational strategies for each device. Genetic algorithm and mixed integer linear programming are, respectively, used to solve the two-layer optimization problem. The comparisons between optimum schemes with and without CO 2 emission cost demonstrates that when the base chiller's capacities are 1179 kW and 1081 kW in cases 1 and 2 respectively, it can independently meet 72.37 % and 61.27 % of the cooling load demand throughout the cooling season. In addition, the optimal capacity of photovoltaics, considering CO 2 emission, increases by 109.7 %, and its penetration rate reaches 18.3 %, which is 9.7 % higher than the optimal scheme not considering CO 2 emission. However, the annualized investment cost increases by 52.3 %. The developed methods provide effective tools for the system design and operation management of solar energy integrated building cooling system. [ABSTRACT FROM AUTHOR]

The hydraulic accumulator has the advantages of high power density, fast response, stable operation and high cost performance. However, compared with the electric energy storage method, the hydraulic accumulator has low energy density and large pressure fluctuation while absorbing and discharging energy, which severely limits its application in hydraulic excavators. To improve the potential energy loss of the boom during the lowering process, an electro-hydraulic drive and energy recovery system for excavator booms (EHDR-EEB) based on a battery and accumulator is proposed. As a result, a simulation model of the electro-hydraulic drive and energy management strategy of a 1.6 t pure electric hydraulic excavator is built to investigate the energy regeneration and utilization. The simulation outcomes show that the potential energy recovery rate is as high as 92%. This research on EHDR-EEB makes a significant contribution to the economic improvement of electric hydraulic excavators. [ABSTRACT FROM AUTHOR]

As a typical energy storage in hydraulic hybrid powertrain, the hydraulic accumulator has high power density but low energy density. There are some efforts in improving the energy density of hydraulic energy storage to achieve balanced performance. Therefore in this study an electric-hydrostatic energy storage system is proposed to replace hydraulic accumulator in a hydraulic hybrid wheel loader. Through active control of proposed energy storage, constant system pressure is possible to provide good vehicle drivability. A rule-based energy management strategy without estimating final drive power is proposed. The vehicle performance and fuel consumption with different constant system pressures are studied. Results show the vehicle fuel consumption is reduced by 5.9% with constant system pressure reduced from 33 to 27 MPa. The energy density and power density of proposed energy storage are calculated, showing a much higher energy density and slightly lower power density than gas-charged accumulator. [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]

The installation of a ground energy storage system (ESS) in the substation can improve the recovery and utilization of regenerative braking energy. This paper proposes an energy management strategy (EMS) of adaptive threshold adjustment for ground ESS. In this regard, this paper analyzes the energy flow in traction power supply system (TPSS) with different headways and no-load voltage. According to the influence of charge and discharge threshold on the energy flow in TPSS, the laws of energy flow are formulated. Subsequently, an EMS based on fuzzy logic control (FLC) is designed. The proposed EMS is adaptive to the variation of headway and no-load voltage. By monitoring the charge-discharge effect of ESS and the variation of substation output energy within two adjacent periods, the charge-discharge threshold is adjusted adaptively to guarantee the recovery effect of regenerative braking energy. Finally, the simulation and field experiment of proposed EMS are carried out with ground energy storage device and actual line conditions of Beijing Batong line. The experiment results indicate the good performance of proposed EMS in terms of energy saving. [ABSTRACT FROM AUTHOR]

In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model. • Data-driven Koopman MPC for EVs under vehicle-following scenarios is proposed. • A Koopman prediction model is developed considering parameters such as slope. • The proposed method reduces energy consumption by 5.55%. [ABSTRACT FROM AUTHOR]

This study presents an optimization-based model with weighted and adjustable parameters, incorporating both deterministic and stochastic variants to enable a smooth transition between control strategies for microgrid management. The motivation to conduct this study arises from an identified gap in the literature on microgrid management, especially as not many studies tackle non-scheduled loads under different energy market policies. Considering this factor, the research objective is to ascertain the technical and economic advantages that the proposed approach offers to a microgrid characterized by a load profile that undergoes significant variations based on the day of the week. A Model Predictive Control (MPC)-based control system is employed as the system manager, using key performance indicators to assess the model's effectiveness. The control strategy, implemented at the reference signal generator level, is designed to minimize operational costs and curb the Energy Storage System (ESS) degradation. The results demonstrate that the deterministic variant of the proposed model provides a significant quantitative return for the microgrid, especially in more stable load profiles. Additionally, the deterministic variant is a method that allows elucidating a range of values for the weighted parameters of the proposed model, which will be used in the approach stochastic variant. Conversely, the stochastic variant stands out by offering more pronounced benefits to the microgrid in scenarios with abrupt load profiles. Both versions of the model under study exhibit remarkable performance compared to the benchmark models. • A management system to control a non-scheduled load microgrid is proposed. • The deterministic and the stochastic variants of the proposed model are evaluated. • Two types of loads are being tested: more constant loads throughout the days and more variable loads. • In the case study, various scenarios involving different energy market policies are explored. • The results of the proposed model are compared to those of benchmark microgrid management models. [ABSTRACT FROM AUTHOR]

One of the main technological stumbling blocks in the field of environmentally friendly vehicles is related to the energy storage system. It is in this regard that car manufacturers are mobilizing to improve battery technologies and to accurately predict their behavior. The work proposed in this article deals with the advanced electrothermal modeling of a hybrid energy storage system integrating lithium-ion batteries and supercapacitors. The objective is to allow the aging aspects of the components of this system to be taken into account. The development of a model including the electrothermal behaviors makes it possible to evaluate the progressive degradation of the performance of the hybrid energy storage system. The characterization of both components constituting the hybrid system is carried out via a hybrid particle swarm–Nelder–Mead (PSO–NM) optimization algorithm using the experimental data of an urban electric vehicle. The obtained results show the good performance of the developed model and confirm the feasibility of our approach. The use of the PSO–NM optimization algorithm facilitated the identification of the parameters of the developed model with high efficiency, as the error observed is less than 3%. The advanced model associated with an adapted sizing method can be used in many cases to compare energy management strategies in electric vehicle applications. [ABSTRACT FROM AUTHOR]

This paper introduces an online multi-level energy management strategy (EMS) based on proposed rule-based and optimization-based approaches for fuel hybrid electric vehicles, including fuel cell, battery, and ultracapacitor systems. Our approach combines equivalent consumption minimization, state machine control, operational mode control, and fuzzy logic control methods. The proposed multi-level EMS reduces fuel consumption, enhances fuel cell operating time in a high-efficiency range, reduces battery power fluctuations, and improves maintaining the battery state of charge (SOC). The proposed EMS is compared with the optimized-fuzzy logic control (Opt-FLC) method. The results show a reduction in fuel consumption, battery power fluctuations, and the SOC difference between the start and end of the driving cycle, compared to Opt-FLC. Hence, fuel economy improvement and lifetime enhancement of hybrid energy storage system are the significant outcomes of new proposed multi-level EMS. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid Fuel Cell-based Power System (FCPS) consisting of fuel cell and hybrid Energy Storage Systems (ESSs), including a battery with high energy density and supercapacitor with high power density to overcome the sudden load demand change and improving the reliability of the delivered power. Any hybrid power system needs Energy Management Strategies (EMS) to balance the power between the different energy sources. In this paper, a comparative analysis of three energy management strategies, including the state machine control method, the classical PI control method and equivalent consumption minimization strategy (ECMS) is performed. The paper's main objective is enhancing the DC-bus voltage profile of a hybrid fuel cell/battery/supercapacitor power system equipped with the developed under-mentioned EMS by using a hybrid modified optimization technique that combines Harris Hawks optimization (HHO) and Sine Cosine Algorithm (SCA). The new hybrid HHO-SCA is employed to determine the optimal control parameters of the DC-bus voltage controller, which significantly assists in enhancing the DC-bus voltage profile as well as the performance of the applicable ESS in terms of improving efficiency and SoC. The effectiveness of the suggested control schemes is simulated using MATLAB/SIMULINK software. The simulation results confirmed that the proposed HHO-SCA is superior and efficient in improving the DC-bus voltage. • A comparative analysis of three energy management strategies is presented. • New hybrid optimization technique is developed. • Optimal tuning for the control parameters of the DC-bus voltage controller. • Enhancing the DC-bus voltage profile and some performance criteria are conducted. [ABSTRACT FROM AUTHOR]

Energy storage system can be used to increase the fuel economy of fuel cell system (FCS). In this study, a new method was introduced for optimizing the energy management strategy (EMS) for fuel cell vehicle (FCV) to reduce fuel consumption. The membership function (MF) in fuzzy control is subjective; thus, 12 design variables in the input‐output MFs were selected using sensitivity analysis, and elliptical basis function neural network method was used to establish a high‐precision approximate model of FCV. Multi‐island genetic algorithm was used to optimize the MFs. The effectiveness of the optimized fuzzy control EMS and the proposed optimization method were demonstrated in simulations of two EMSs under four driving cycles. The simulation results confirmed that the optimized fuzzy control EMS provided smoother and more stable output power from FCS reducing hydrogen consumption by 8.4%, 1.1%, 5.1%, and 7.6%, respectively, compared to the original fuzzy control EMS; and hydrogen saved by the optimized EMS provided extra range of 9.15, 1.10, 5.37, and 8.25 km per 100 km in the four driving cycles, respectively. The optimized EMS can reduce hydrogen consumption to increase fuel economy and extend the life span of the fuel cell. [ABSTRACT FROM AUTHOR]

• An energy management strategy for hybrid energy storage system is elaborated. • A control mechanism for a global system is presented to stabilize the DC bus voltage. • A control mechanism for buck-boost converters is elaborated for batteries and SCs. • The effectiveness of the use of SCs was demonstrated by different simulation tests. The photovoltaic energy enables a variable power generation that is influenced by uncertain fluctuations caused by the weather change (temperature and solar irradiation). Hence, the requirement for an energy storage system is essential to address this major issue. The use of only one energy storage element, such as battery, is insufficient. For this purpose, supercapacitors (SCs) can also be introduced as a power storage device. The combination of batteries and SCs is a viable solution that requires an appropriate energy management strategy. The previous studies are focused on the designing and modeling. In this study, a photovoltaic system with a hybrid energy storage system (HESS) was developed by using batteries and supercapacitors. The development of an energy management strategy requires energy distribution between two different storage mediums i.e. batteries and SCs. Thus, a new dimension for a PI controller design is introduced to stabilize the direct current (DC) bus voltage and command the buck-boost converters associated with batteries and SCs. The DC bus is controlled by voltage to stabilize the DC bus voltage at the reference voltage 400 V. Two other PI controller are used to calculate the reference current of batteries and SCs. The distribution of energy between batteries and SCs is based on a low pass filter in order to eliminate the peak current on batteries and send it to SCs. The energy consumption of batteries and SCs is controlled by the filter constant. The simulation results prove that the proposed sizing design and system model provide good stabilization in the DC bus voltage. Furthermore, the augmented constant filter reduces the state-of-charge (SOC) of the battery; hence the overall lifespans of batteries are increased. The consumption of the SoC bat = 57.60% for τ = 2 s where the consumption of the SoC bat = 48.96% for τ = 16 s. [ABSTRACT FROM AUTHOR]

Uncertainties in the solar photovoltaic (PV) power generation, random behaviour of consumer load power demand, and unexpected failures are the major factors for the consumer power interruption (CPI) hours, which reduce the reliable power supply rate or reliability index, of an islanded microgrid (IMG). One way to address this challenge is to complement the IMG with solar PV, energy storage system (ESS), and diesel generator (DG). This study first defines the reliability index of an IMG that equipped with solar PV, ESS, and DG. Then the authors propose an energy management strategy (EMS) for an IMG to maintain the reliability index above a given threshold limit while lowering the cost through higher utilisation of PV, lower CPI hours, and lower DG operating hours. They utilise real IMG consumers load power demand and solar radiance data for simulation studies to show how the proposed EMS achieves the desired trade-off in reliability and cost.

The increments of the emission tiers and global warming, as well as the reduction of fossil fuels, have led to a change in the power system of vehicles. Even in maritime transportation, there are more restrictions due to environmental regulations of some ports. In this study, the energy management problem of a marine power system is investigated, such a system includes a diesel generator, solar system, energy storage system (ESS), electrical boiler system, and electrical heat pump. The proposed risk-based strategy for scheduling multiple energy systems of a hybrid cruise ship (HCS) is able to provide the electrical and thermal demands related to the cruise services in a reliable and optimal way. The examined HCS travels daily in the Baltic Sea between Stockholm in Swedish mainland and Mariehamn on the Åland islands and carries hundreds of passengers in various tour programmes, representing an uncertain shipboard loading profile. Therefore, an information gap decision theory-based (IGDT-based) methodology is applied to model the uncertain HCS load. Furthermore, the proposed multiple energy system can adopt various strategies namely risk-averse and risk-seeker to face different consumption patterns. Finally, the numerical results obtained from actual test case validate the applicability and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to absorb the regenerative braking energy of trains, supercapacitor energy storage systems (ESS) are widely used in subways. Although wayside ESS are widely used, because of the influence of no-load voltage, and so on, a wayside ESS cannot absorb all the regenerative braking energy in some special cases, and the brake resistor is still activated, which leading to the wasting of energy. In order to completely replace the on-board brake resistor, this paper configures a certain on-board super-capacitor, and based on a DC-side series super-capacitor topology, proposes a hierarchical optimization energy management strategy (EMS). The EMS is divided into three layers: Firstly, the strategy can increase the inverter-side voltage in a short time without changing the traction network voltage, and improve the train braking characteristic curve by utilizing the short-time overvoltage capability of the inverter and the motor; Secondly, by coordinated control with a wayside supercapacitor, the residual regenerative braking energy can be absorbed even in special cases; Finally, based on loss calculation and current prediction, this strategy can effectively reduce the system loss by adjusting the DC voltage on the inverter-side. The proposed control strategy is validated through RT-LAB experiment, and the experimental results agree well with the theory. [ABSTRACT FROM AUTHOR]

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

• Design of EMS determines the benefits of stationary super capacitor energy storage system in urban rail transit power supply system. • Control objectives of stationary super capacitor energy storage system vary at different time scales. • Layered management based on time scales can better meet control objectives. • Proposed multi-time scale management strategy based on GA and FLC has more advantages under long-term operation. The application of stationary super capacitor energy storage systems (SCESS) is an effective way to recover the regenerative braking energy of urban rail transit vehicles. The benefits of these systems' application largely depend on the design of the energy management strategy (EMS). In this paper, the EMS core demand of SCESS is analyzed and divided into different levels based on the considered time scale. On this basis, control objectives and mathematical models were analyzed considering different time scales. Then, an EMS based on a genetic and fuzzy algorithm was presented. The feasibility and effectiveness of the proposed EMS were verified through the simulation of actual line parameters. The results indicated that the EMS can satisfy the control requirements of multiple time scales of SCESS. It is capable of effectively achieving energy management and coordinated control of multiple SCESS. As a result, it also for energy savings in the urban rail transit power supply system. [ABSTRACT FROM AUTHOR]

Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases. • The stochastic programming problem is established for the power dispatch of FTPSS. • The LOT-based algorithm is proposed to enable problem solving in real-time, with the proof of the gap with the offline optimal solution. • The algorithm is extended to the EMS for Hybrid ESSs. • The approach is model-free and can serve without the load predictive model or statistical information. [ABSTRACT FROM AUTHOR]

Optimal scheduling and robust sequence control of a central chiller system with thermal energy storage may not be a straightforward solution to reduce building cooling load demand (CLD) due to unpredictable thermal comfort requirements and the stochastic nature of occupancy-dependent CLD. One potential way to address this challenge is to design and develop an energy management strategy (EMS) for a zone-variable air volume unit. This paper proposes an EMS to reduce CLD by deciding the zone reference setpoint temperature based on fuzzy logic strategy (FLS) and zone thermal models. The objective is to reduce zone CLD while maintaining zone temperature and zone thermal comfort margin within the threshold limits. In this context, first, we modeled the zone thermal behavior using an artificial neural network (ANN) approach to predict zone temperature for next time slot, modeled the zone cooling energy behavior using a regression approach to predict zone CLD for the next time slot, and estimated the zone occupancy status using noise data. Second, we implemented zone thermal feedback model and comfort margin based on zone thermal conditions and predicted mean vote index, respectively. Finally, we implemented an EMS that includes both demand supply strategy and cooling load demand reduction strategy based on one time-slot ahead prediction. The novelty of the proposed EMS lies within the zone CLD reduction using FLS, predicting zone thermal behaviors using an ANN and a regression approach, estimating zone occupancy using noise data, and maintaining zone thermal comfort margin within the threshold limits. Historical data from a commercial building in Singapore are used to demonstrate the effectiveness of the proposed EMS. [ABSTRACT FROM AUTHOR]

Nowadays, the emissions from conventional vehicles significantly contribute to increasingly serious environmental issues. In addition, the energy crisis and the low energy efficiency of conventional vehicles also offer a good opportunity to develop electric vehicles. Hybrid electric vehicles have better fuel economy compared to conventional vehicles, but they are just an interim step in vehicle development and pure electric vehicles are the ultimate goal. Currently, the technologies of hybrid electric vehicles can be found in numerous literature surveys, however there is a lack of published papers to present a comprehensive technical review for pure electric vehicles. In this study, the characteristics and typical models of energy sources of pure electric vehicles are firstly described. Then the existing pure electric vehicle types are depicted and the environmental impacts of the typical pure electric vehicles are evaluated. Moreover, energy management strategies for pure electric vehicles and charging technologies are investigated. The main challenges faced by pure electric vehicles and corresponding solutions are discussed, whilst the latest developments of pure electric vehicles are presented. The awareness of environmental issues and the energy crisis as well as the incentives from the governments of many countries continuously enhance the rapid development of pure electric vehicles. • The main reasons to develop pure electric vehicles (PEVs) are presented. • The characteristics and typical models of energy sources for PEVs are described. • The existing configurations in PEV types up to present are illustrated. • A brief study on energy management strategies employed in PEVs is conducted. • The main challenges for PEVs and general problem-solving methods are discussed. [ABSTRACT FROM AUTHOR]