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To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics. [ABSTRACT FROM AUTHOR]

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a novel power management strategy (PMS) is proposed for optimal real-time power distribution between battery and supercapacitor hybrid energy storage system in a DC microgrid. The DC-bus voltage regulation and battery life expansion are the main control objectives. Contrary to the previous works that tried to reduce the battery current magnitude and its fluctuation to increase the battery life, the battery thermal considerations, which are a key factor in battery life, are also considered as an objective function in the proposed PMS. Also, the quick charging capability of the battery is another objective in the optimization problem. To solve this multi-objective problem, the Karush–Kuhn–Tucker conditions have been used to make the PMS suitable for real-time operation. In addition, one of the important inputs of conventional PMS is the amount of grid excess/deficiency power. Due to the use of the adaptive Kalman filter estimator, another superior of the proposed PMS is using local data and no need for communication and knowledge of the load or generation power. Using simulation, the performance of the proposed method is compared with three traditional methods filter-based, droop control, and fuzzy logic in terms of controlling the battery current, battery temperature, and battery health, and its superiority has been shown. Finally, the validation of the proposed PMS is experimentally tested on a real setup. [ABSTRACT FROM AUTHOR]

Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power and control stages of a hybrid energy storage system formed by a battery, a supercapacitor, and a bidirectional Zeta converter. The control stage involves an adaptive sliding-mode controller co-designed with the power circuit parameters. The design algorithm ensures battery protection against high-frequency transients that reduce lifespan, and provides compatibility with low-cost microcontrollers. Moreover, the continuous output current of the Zeta converter does not introduce current harmonics to the battery, the microgrid, or the load. The proposed solution is validated through an application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance in comparison with a classical cascade PI structure. [ABSTRACT FROM AUTHOR]

The modern era is witnessing a growing demand for sustainable and eco-friendly power sources. An interconnected power system capable of seamlessly integrating electric vehicles and renewable energy resources is being considered as a viable solution. However, this technology has some drawbacks, such as its lower system inertia, which limits its ability to respond to load capabilities. To overcome this issue, a Hybrid Energy Storage System (HESS) can be integrated with new techniques to enhance performance. This paper proposes a new Quasi Opposition Arithmetic Optimization Algorithm (QOAOA) optimized Fractional Order Proportional Integral Derivative with Filter (FOPIDN) controller cascaded One Plus Tilted Derivative (1 + TD) controller with HESS of super-capacitor (SC) and Redox Flow Battery (RFB) to mitigate frequency and tie-line power variations in a multi-area restructured smart-grid system. The proposed controller's performance is evaluated under various conditions, such as load fluctuations, wind speed variations, solar irradiation, Governor Dead Band (GDB), and generation rate limitations. For unsystematic load the improvement in the performance of the proposed controller indicated by maximum variation in frequency ( Δ f 1 , Δ f 2 , Δ f 3 ) and power change on the tie-lines ( Δ P tie 12 , Δ P tie 13 ) over FOPIDN controller is (98.65%, 48.78%, 52.52%) and (75.0%, 76.1%). The system performance is also analyzed when HESS devices are integrated into the three-area system, taking into account the varying nature of wind speed and solar irradiation. The improvement in performance indicated by maximum variation in frequency ( Δ f 1 , Δ f 2 , Δ f 3 ) and power change on the tie-lines Δ P tie 23 by incorporating HESS in the system is (45.21%, 33.33%, 29.59%) and 46.95% over without energy storage system. The proposed controller's outcomes are validated using real-time hardware-in-the-loop (HIL) simulation with OPAL-RT. [ABSTRACT FROM AUTHOR]

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery's capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. [ABSTRACT FROM AUTHOR]

Summary: Hybrid energy storage technology, which consists of lithium‐ion batteries (LiB) and super capacitors (SC), is an effective way to ensure the safety of power supply and realize energy saving in metro by reusing the braking power. Aiming at the optimal configuration and control of the metro hybrid energy storage system (HESS), an energy management strategy (EMS) based on dual DC/DC architecture and voltage droop method is proposed. And then the control parameters are adjusted in segments by combining the DC bus voltage and values of SOC of LiB and SC. What's more, a hardware‐in‐loop (HIL) simulation platform of Metro DC power supply system is built to verify the control strategy. The experimental results show that HESS could stabilize the metro voltage within a safe voltage of 580 V and achieve 100% braking energy recovery by optimal energy distribution between two different types of energy storage systems, which are only 79.9% and 39.2% in other single energy storage system by contrast. At the same time, the HIL simulation platform can be used to verify the control effect of different configuration capacities and different control strategies of the HESS, helping to provide the optimal solution of HESS for actual subway lines. [ABSTRACT FROM AUTHOR]

This paper presents a two-level hierarchical control method for the power distribution between the hybrid energy storage system (HESS) and the main dc bus of a microgrid for ultrafast charging of electric vehicles (EVs). The HESS is composed of a supercapacitor and a battery and is an essential part to fulfill the charging demand of EVs in a microgrid made up of a 220 V RMS ac bus, two dc buses of 600 V and 1500 V, respectively, and four charging points. A state machine defines the four operating modes of the HESS and establishes the conditions for the corresponding transitions among them, namely, charging the battery and the supercapacitor from the bus, injecting the current from the HESS into the 1500 V dc bus to ensure the power balance in the microgrid, regulating the bus voltage, and establishing the disconnection mode. The primary level of the control system regulates the current and voltage of the battery, supercapacitor, and dc bus, while the secondary level establishes the operating mode of the HESS and provides the appropriate references to the primary level. In the primary level, sliding mode control (SMC) is used in both the battery and supercapacitor in the inner loop of a cascade control that implements the standard constant current–constant voltage (CC-CV) charging protocol. In the same level, linear control is applied in the CV phase of the protocol and for bus voltage regulation or the current injection into the bus. PSIM simulations of the operating modes and their corresponding transitions verify the theoretical predictions. [ABSTRACT FROM AUTHOR]

Summary: The peak and transient components of demand power caused by the complex and variable traffic environment could induce the accelerated degradation of the battery lifespan for electric vehicle (EV). This paper proposes a wavelet transform‐based real‐time energy management strategy (EMS) to fully exploit the advantages of the hybrid energy storage system (HESS). First, to adapt the characteristics of battery and ultracapacitor, wavelet transform is employed to decompose driving cycle into high frequency power and low frequency power. Second, since the wavelet transform is difficult to be directly implemented in real‐time control, a power prediction model including four neural network predictors is established, which are trained by the data obtained from wavelet transform to online predict the power of different frequencies. Third, the high frequency power and the low frequency power are distributed to the battery and ultracapacitor by fuzzy logic control (FLC) algorithm, which can significantly reduce the damage caused by current rapid changes into the battery. Accordingly, the battery lifespan is effectively extended because it significantly avoids the impact of rapidly changing and peak current. Finally, simulation results verify the effectiveness of wavelet transform‐based real‐time EMS, and the proposed EMS robustness against temperature is verified at different temperatures. Compared with the wavelet transform strategy (WTS), the peak currents of wavelet transform‐fuzzy logic control strategy (WTFLCS) are decreased by 26.07%, 25.66%, and 25.85% at 10°C, 25°C, and 40°C, respectively. [ABSTRACT FROM AUTHOR]

Supercapacitors attract attention due to their superior values in the parameters like capacitance, discharge currents and cycle lifespan. Supercapacitors are designed and used in many applications where they partially or completely substitute conventional batteries. On the other side, supercapacitors are used in applications which are not so far suitable for these devices. To avoid wrong design and misuse of the supercapacitors it is necessary to correctly understand their properties, key advantages and disadvantages. Similar situation can be found in the field of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

Integrating hybrid energy storage system (HESS) consisting of battery and supercapacitor provides balance over the power generation and load demand, ensuring the system's stability under transient conditions. Along with battery storage, to control its rate of charging/discharging a rate limiter (RTL) is usually employed. However, the impact of the RTL on battery storage efficiency and system performance has not been explored in the existing literature. This paper investigates the performance of a grid‐tied photovoltaic (PV) system with HESS under dynamic scenarios with different RTL values applied to the battery storage. The paper explores the simulation analysis considering transient, linear, and steady‐state conditions with varying PV generation and load demand. The simulation study shows that a lower RTL value may slow down the battery response, leading to inefficient utilization of battery storage. Alternatively, a very high RTL value will not appropriately regulate the battery current, which may lead to frequent charging and discharging and deterioration of battery life. For the proposed system, RTL with a value of 100 A/s seems appropriate, providing 1.29 Wh of energy through battery storage with an energy efficiency of 91.81% for the simulated time. The DC voltage is efficiently maintained at 400 V with a ripple factor of 0.45%. [ABSTRACT FROM AUTHOR]

The integration of a renewable energy and hybrid energy storage system (HESS) into electrified railways to build an electric railway smart microgrid system (ERSMS) is beneficial for reducing fossil fuel consumption and minimizing energy waste. However, the fluctuations of renewable energy generation and traction load challenge the effectiveness of the energy management for such a complex system. In this work, an energy management strategy is proposed which firstly decomposes the renewable energy into low-frequency and high-frequency components by an integrated empirical mode decomposition (IEMD). Then, a two-stage energy distribution approach is utilized to appropriately distribute the energy flow in the ERSMS. Finally, the feasibility and effectiveness of the proposed solution are validated through case study. [ABSTRACT FROM AUTHOR]

A Hybrid Energy Storage System (HESS) uses DC-DC converters to couple its energy sources. However, this device represents a "dead weight" in the system and must be reduced to a minimum in order to maximize the HESS' performance. This work proposes a new coupling architecture to reduce the converter's volume and mass. Not yet addressed in the literature, this architecture is based on a series coupling of the sources. In this case, a DC-DC converter is used to control the current difference between the two sources. If this difference is zero, so is the power processed by the converter. By reducing the power processed by the converter, its mass and volume can be reduced. Simulation and experimental tests were carried out to validate the architecture concept. For the latter, power supplies were used to emulate the batteries and the load, and a 2 kg, 3.3 kW evaluation board served as the DC-DC converter. The results show that, compared to a conventional solution that is usually adopted in the literature, with the series architecture, it is possible to reduce the converter sizing power by almost 3.7. [ABSTRACT FROM AUTHOR]

Electricity generation and consumption must undergo a substantial transformation as part of the global energy transition towards decarbonization. Hydrogen-based hybrid energy storage systems (HESS) have the potential to replace the existing fossil fuel-based energy generation due to their high energy density and long storage capacity. This study has introduced a novel indicator "usage count" instead of "citation analysis" to obtain the top 100 articles in the field of hydrogen-based HESS because of the factors such as reduction of time lag, recentness and domain independency. After the filtration process, the top 100 articles with the highest usage count are obtained by providing an extensive search in the Scopus database from the year 2012–2021. An extensive comparative study between "usage count" and "citation analysis" among the selected top 100 articles is provided. Moreover, a detailed keyword co-occurrence network (KCN) analysis along with comprehensive reviews concerning HESS modelling, optimization objectives, algorithms, system constraints, and research gaps are presented. The review emphasizes the benefits and drawbacks of hydrogen-based HESS and analyzes the obstacles associated with their application, such as the system's high cost, technical complexity, socio-economic and environmental impact and safety concerns, which can provide the researchers with a clearer picture for future research and development. Finally, the article conveys some suggestions and directions, which can act as a roadmap toward achieving reliable and sustainable next-generation hydrogen-based HESS. • Hydrogen is a potential energy due to its energy density and storage capacity. • Usage count is a novel indicator for future research trends and directions. • This study highlights usage count of hydrogen-based hybrid energy storage system. • Issues and challenges of the existing hydrogen-based storages are highlighted. • Suggestions and directions are provided for future hydrogen storage applications. [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]

Environmental issues such as air pollution and abnormal climate are global concerns. To overcome these problems, the automobile industry is prioritizing the development of eco-friendly vehicles that reduce greenhouse gas emissions Among these, fuel-cell electric vehicles (FCEVs) use hydrogen as a fuel and do not emit exhaust gas and their higher mileage and shorter fuel charging time compared to electric vehicles make them promising next-generation eco-friendly vehicles. However, conventional energy management strategies have not effectively implemented both real-time capability and optimal energy distribution in FCEVs. To address these issues, a powertrain utilizing multi-energy sources is utilized, and a real-time energy control strategy based on long short-term memory (LSTM) is proposed. The training data for LSTM is obtained from the results of dynamic programming, utilizing six-city bus driving cycles, and the Braunschweig city driving cycle is chosen for test simulation. As a result, the LSTM prediction performance is evaluated, resulting in the development of an effective algorithm for real-time energy management of multi-energy sourced FCEVs. [ABSTRACT FROM AUTHOR]

With the rapid development of hybrid energy storage technology, thermal-energy storage frequency regulation has become an essential method of grid frequency regulation. A double-layer control strategy is proposed to address the lack of frequency regulation demand allocation of thermal-energy storage frequency. The upper layer is frequency regulation demand precision control layer, using frequency deviation and frequency deviation change rate as input, by fuzzy control algorithm to output the system optimal frequency regulation demand signal, which was sent to the lower layer; The lower layer is frequency real-time optimization layer, under the given constraints, frequency deviation is minimized as the optimiza tion objective, by improving the gray wolf algorithm to achieve the optimal allocation of frequency regulation demand signal. Aiming at the global and local search ability unbalance of the Gray Wolf algorithm, chaotic mapping and nonlinear decreasing improvement strategies were introduced to improve the allocation ability of the frequency regulation demand in hybrid energy storage systems. Finally, a thermal-energy storage frequency regulation simulation model was established, verifying the effectiveness of the proposed strategy by simulating and analyzing two typical load disturbances. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic power distribution strategy for the hybrid energy storage systems (HESSs) in electric vehicles (EVs). First, the power loss of a HESS is analyzed based on its structure and model. Second, the optimal objectives for EV range extension, battery degradation mitigation, and HESS energy loss reduction are set, and the corresponding optimization variables are determined. Then, a multi-objective collaborative optimization (MOCO) function is established. It is further transformed into a linear programming problem with the battery current as the control variable. Finally, the dynamic power distribution scheme is obtained by analyzing the MOCO problem. The dynamic power distribution strategy using the MOCO is studies through simulations and experiments under the worldwide harmonized light vehicles test cycle. The obtained results indicate that the performances of the three optimal objectives are collaboratively improved. [ABSTRACT FROM AUTHOR]

With the promotion of carbon peaking and carbon neutrality goals and the construction of renewable-dominated electric power systems, renewable energy will become the main power source of power systems in China. Therefore, ensuring frequency stability and system security will emerge as pivotal challenges in the future development process. Created by combining a Li-ion battery and a supercapacitor, a hybrid energy storage system (HESS), which possesses robust power regulation capabilities and rapid response capabilities, holds promise for supporting the frequency stability of power systems. In this context, the assessment of the economic viability of HESSs providing multitype frequency response services becomes a critical factor in their deployment and promotion. In this paper, an economic analysis approach for a Li-ion battery–supercapacitor HESS towards a multitype frequency response is presented. First, a multitype frequency response-oriented operational mode for the HESS is designed, outlining the roles and functions of the Li-ion battery and the supercapacitor in delivering distinct services. Moreover, building upon the analysis of the power trajectory of Li-ion batteries, a lifetime model for the HESS is proposed based on the rain-flow counting method. Furthermore, considering the competitive landscape for the HESS in the frequency regulation ancillary service market, a full lifecycle economic assessment model is proposed. Finally, case studies on actual power system frequency data and PJM market data are performed to verify the effectiveness of the proposed method, and the simulation results confirm that the HESS exhibits robust performance and a competitive advantage in providing multitype frequency response services. Additionally, it demonstrates commendable economic benefits, establishing its potential as a valuable contributor to frequency response services. [ABSTRACT FROM AUTHOR]

In order to ensure reliable connection of microgrid, a hybrid energy storage system was used to effectively suppress the active power fluctuation caused by random output of wind-photovoltaic. In order to give full play to the characteristics of different types of energy storage and achieve accurate power distribution of hybrid energy storage system(HESS), a hybrid energy storage power distribution strategy based on whale optimization algorithm-variational mode decomposition(WOA-VMD) two-layer decomposition was proposed. The moving average algorithm was used to determine the grid-connected power and the fluctuation power required by HESS, and WOA-VMD was used to decompose the fluctuation power of wind-photovoltaic. Considering that the decomposition allowance of VMD may contain rich information, the allowance was further decomposed to complete the primary power allocation of HESS. Fuzzy control was adopted to optimize the state of charge(SOC) of energy storage equipment and complete the second modification of HESS power. Example analysis shows that the proposed strategy can adaptively decompose the fluctuation power of wind-photovoltaic, realize the reasonable distribution of HESS power, effectively suppress the power fluctuation of wind-photovoltaic, and ensure the reliable grid connection. In addition, the SOC of energy storage is kept in an ideal range to avoid excessive charge and discharge of energy storage equipment and ensure safe and stable operation of HESS. [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]

Storing energy in hydrogen deposits balances the operation of energy systems and is an effective tool in the process of energy transformation towards achieving Sustainable Development Goals. To assess the validity of its use as an alternative renewable energy carrier in dispersed energy systems of hybrid configuration, a comprehensive review of scientific literature was conducted in this study, based on bibliometric analysis. The bibliographic database used in the study was the international Web of Science database. This review contributes to a better understanding of the characteristics of the selected research area. The evolution of research trends implemented in the design of energy systems associated with hydrogen technologies is revealed, clearly indicating that it is a developing field. In recent years, there has been an increase in the number of publications, although the territorial range of research (mainly simulation) conducted in the domain does not include areas with the most favourable infrastructural conditions. The analysis reveals weak cooperation between South American, African, East Asian, and Oceanic countries. In the light of earlier, thematically similar literature reviews, several research gaps are also identified and proposals for future research are presented. They concern, in particular, the parallel implementation and optimization of the operation of hydrogen (HRES—Hybrid Renewable Energy System and HESS—Hybrid Energy Storage System) solutions in terms of economics, ecology, lifespan, and work efficiency, as well as their feasibility analysis. With the support of other researchers and those involved in the subject matter, this review may contribute to the further development of hybrid hydrogen systems in terms of increasing competitiveness and promoting the implementation of these technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The steady and transient performance of a bidirectional DC–DC converter (BDC) is the key to regulating bus voltage and maintaining power balance in a hybrid energy storage system. In this study, the state of charge of the energy storage element (ESE) is used to calculate the converter current control coefficient (CCCC) via Hermite interpolation. Moreover, the charging and discharging currents of the BDC are controlled by the CCCC. Thus, the ESE runs in the best working region. The linear active disturbance rejection control is used in the current inner loop of BDC to solve the problems of slow dynamic response and parameter tuning in traditional PI control. The method can deal with the great transient response and a vast range of uncertainties, improve the BDC response speed by more than 20%, compensate for the unbalanced power timely, reduce the bus voltage fluctuation, and enhance the disturbance immunity of the system. The proposed method is verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: The uncertainties in generation from renewable energy sources and randomness in electric vehicle charging/discharging make the generation and load in the grid more volatile. The large‐scale integration of renewable energy sources and electric vehicles has necessitated the remodeling of conventional control schemes in the microgrid. This article presents an adaptive two‐level hierarchical controller that enriches hybrid AC/DC microgrid performance. The virtual synchronous machine‐based primary controller determines the operating mode of the interlinking converter. In addition, it maintains the system's stability by providing virtual inertia support. The communication‐based secondary controller adaptively chooses the scaling factors based on hybrid AC/DC microgrid operating conditions. The adaptive secondary control loop implemented with the primary controller and hybrid energy storage system reduces the frequency/voltage deviations under extreme load volatility and enhances the transient and steady‐state performance. The novelty of the proposed two‐level hierarchical controller is that the general purpose adaptive scheme improves the power sharing in the microgrid under normal and volatile source and load conditions. The simulations and experimental results confirm the efficacy of the proposed controller. Furthermore, the hierarchical two‐level controller performance is validated using real‐time experiments on a hardware‐in‐the‐loop simulator, OPAL‐RT, and Raspberry Pi microcontroller. The real‐time analysis ascertains that the proposed two‐level hierarchical controller effectively preserves the frequency and voltage profiles and provides accurate power sharing between the subgrids. [ABSTRACT FROM AUTHOR]

The battery/supercapacitor (SC) hybrid energy storage system (HESS) is widely applied in electric vehicles (EVs) in recent years due to the hybrid system which combines the benefits of both devices. This paper proposes an adaptive power distribution scheme for battery/SC HESS to maximise the usage of SC according to its stored energy and load current. In the approach, the low-pass filter is developed with adaptive algorithm to calculate the suitable cut-off frequency to allocate the power demand between the battery and SC. The approach can adjust the cut-off frequency but not change the structure of the control system, and thus its original property of simple implementation and stability is not affected. The comprehensive simulation study verifies the effectiveness of the proposed adaptive power distribution scheme in a battery/SC HESS and its stability is further validated using Lyapunov method. The result shows that the adaptive method performs better than a traditional control system with 20%–40% less battery energy throughput during operation and can adjust the dynamic response of the HESS according to the energy capacity of SC to further improve system efficiency. The proposed adaptive power distribution scheme is verified able to extend the service life of the HESS system in EV applications. [ABSTRACT FROM AUTHOR]

This study proposes a combined hybrid energy storage system (HESS) and transmission grid (TG) model, and a corresponding time series operation simulation (TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Energy storage technology provides efficient energy management in renewable driven power system. The long duration time-scale fluctuation in unbalance power becomes more obvious and prominent with the elevated renewable penetration level. However, this issue is not widely considered in current energy storage system. In this paper, a green hydrogen-electric coupled energy storage system based on hydrogen-fueled compressed air energy storage (CAES) and power-to-gas-to-power (PtGtP) device is proposed. The hydrogen-based PtGtP device, including proton exchange membrane fuel cell (PEMFC) and PEM electrolzyer, is employed to smooth out the long duration time-scale fluctuation. Whereas, the hydrogen-fueled CAES is used to settle the remaining time-scale fluctuations. Moreover, the coupled feature is reflected by the hydrogen medium. The hydrogen only generates in PtGtP device, but consumes in both PtGtP device and hydrogen-fueled CAES. The performance assessment by considering the actual operation constraints is conducted based on historical data from real world. The simulated results show that the proposed system can provide an effective and flexible power management in the high share renewable power system. The loss of power supply probability (LPSP) is 5.40%, which is higher than that of any single energy storage system. However, the wind curtailment ratio (WCR) is 8.81%, illustrating an insufficient energy storage capacity. Furthermore, the energy shifting occurs in both several days scale and seasonal scale. This is obvious evidence for function of long duration energy storage (LDES) for the proposed coupled energy storage. • A green hydrogen-electric coupled energy storage system is proposed. • The power-to-gas-to-power device is used to smooth long duration power fluctuation. • The actual operation constraints of system are considered in performance evaluation. • The multiple time-scale fluctuations in unbalance power can be well smoothed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The bus voltage of the ship DC microgrid is sensitive to the change of loads, which has an influence on the power supply quality. This paper introduces a hybrid energy storage system (HESS) that is composed of a battery set and a supercapacitor set, and further studied the control method of HESS. First of all, the topological structures of the ship DC microgrid and HESS are described. Second, combined with the frequency division droop control and voltage PI control, a sliding mode control (SMC) method is proposed to control the charge and discharge of HESS based on an improved reaching law. Finally, the simulation model of the ship DC microgrid is established for the verification of the control method. Simulation results show that: (1) HESS can overcome the shortage of the dynamic response ability of the diesel rectifier generator to the steep change of load power. The supercapacitor set and the battery set successfully respond to the high-frequency and low-frequency components of the differential power in the system, respectively. (2) Compared with the traditional PI control method, SMC can reduce the current chattering of HESS and the voltage fluctuation amplitude of the DC bus. The proposed SMC method can provide a reference for the stable and reliable operation of the ship DC microgrid. [ABSTRACT FROM AUTHOR]

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

This manuscript proposes a hybrid method for optimum sizing and energy management (EM) of hybrid energy storage systems (HESSs) in Electric vehicle (EV). The proposed hybrid method is combined performance of Honey Badger Algorithm (HBA) and recalling-enhanced recurrent neural network (RERNN), commonly called HBA-RERNN method. The major objective of proposed system is reducing the vehicle life time cost. The HESSs are incorporated with battery and super capacitor (SC). The proposed method is utilized to solve combined energy management and optimization size. Based on the variables, such as size of battery pack and super capacitor pack, HESS size is reflected. Depend on various sensitivity factors, optimum hybrid energy storage systems size and financial costs are analyzed. At last, the performance of proposed system is implemented on MATLAB site and compared with several existing systems. From this simulation outcome, it concludes that the proposed system diminishes the overall cost and battery degradation cost as 66625 USD than the existing systems. The efficiency of the proposed system achieves 94.8763%. [ABSTRACT FROM AUTHOR]

Energy security and the resilience of electricity networks have recently gained critical momentum as subjects of research. The challenges of meeting the increasing electrical energy demands and the decarbonisation efforts necessary to mitigate the effects of climate change have highlighted the importance of microgrids for the effective integration of renewable energy sources. Microgrids have been the focus of research for several years; however, there are still many unresolved challenges that need to be addressed. Energy storage systems are essential elements that provide reliability and stability in microgrids with high penetrations of renewable energy sources. This study provides a systematic review of the recent developments in the control and management of energy storage systems for microgrid applications. In the early sections, a summary of the microgrid topologies and architectures found in the recent literature is given. The main contributions and targeted applications by the energy storage systems in the microgrid applications is defined for each scenario. As various types of energy storage systems are currently being integrated for the reliable operation of the microgrids, the paper analyses the properties and limitations of the solutions proposed in the recent literature. The review that was carried out shows that a hybrid energy storage system performs better in terms of microgrid stability and reliability when compared to applications that use a simple battery energy storage system. Therefore, a case study for a DC microgrid with a hybrid energy storage system was modelled in MATLAB/Simulink. The presented results show the advantages of hybrid energy storage systems in DC microgrids. [ABSTRACT FROM AUTHOR]

The use of the conventional dual closed-loop control strategy by photovoltaic (PV) plants with grid-connected inverters may weaken the damping of a power system, which may aggravate low-frequency oscillations (LFOs). This influence will become more severe as the penetration of PV plants increases. Therefore, it is necessary to incorporate damping controls into PV plants to suppress LFOs. This paper proposed an additional compound damping control (ACDC) system that combines additional damping control (ADC) for the inverter with ADC-based dynamic power compensation control (DPCC), allowing hybrid energy storage systems (HESSs) to suppress LFOs. First, the feasibility of suppressing low-frequency oscillations in PV plants is demonstrated by the torque method and a small signal model. Then, an additional damping controller is added to the active power control link of the PV inverter to enhance the damping abilities of the system. However, given that the damping performance of PV plants with only ADC is limited by the compensated power, PV plants require devices that can rapidly compensate for the damping power. Therefore, we added the HESS to the DC bus and proposed DPCC. Finally, a three-machine nine-node system for a PV plant was modeled and simulated in the PSCAD platform. The simulation results showed that the proposed control strategy could provide effective damping for interarea oscillation. [ABSTRACT FROM AUTHOR]

This paper deals with the power smoothing of the wind power plants connected to a microgrid using a hybrid energy storage system (HESS). In a HESS, the power should be distributed between the battery and capacitor such that the capacitor supplies the peaks of power and its high‐frequency fluctuations, and the battery compensates for the rest. Besides, due to the relatively low lifetime of the batteries compared to the capacitors, it is preferred to transfer power fluctuations to the capacitor as much as possible. In this paper, methods for calculating the output, battery, and capacitor powers are presented. The output power is determined based on the grid restrictions and the battery SOC. The battery and capacitor powers are decided via an adaptive low‐pass filter. The cut‐off frequency of the low‐pass filter is specified by a fuzzy controller such that not only the power spikes and high‐frequency fluctuations are transferred to the capacitor but also the battery SOC variations are reduced. The simulation results show that for the presented wind speed profile, the output power determination reduces 13.7% of the HESS exchanged energy compared to the ramp limiting method. Besides, the proposed power distribution method reduces 92.6% of the unbeneficial charges and discharges of HESS and 8% of the battery exchanged energy compared to the condition that the constant cut‐off frequency filter is utilised. The experimental results confirm the effectiveness of the proposed output power determination and HESS power distribution methods. Analysing the methods' cost proves that although the utilisation of the capacitor bank increases the system initial investment cost, it will return after a while relying on the units' capacity, power fluctuations, control method etc. [ABSTRACT FROM AUTHOR]

In order to give full play to the advantages of power battery and super-capacitor in the hybrid energy storage system (HESS) of hybrid electric vehicles (HEV), a new control strategy based on the subtractive clustering (SC) and adaptive fuzzy neural network (AFNN) was proposed to solve the problem of power distribution between the two energy sources when the driving schedule changes. Firstly, we used the SC to determine the structure of AFNN. Secondly, in order to improve the learning efficiency of AFNN, the back-propagation hybrid least square algorithm was applied to optimize the antecedent and conclusion parameters of network. Finally, the fuzzy membership function and rule set automatically generated by the neural network were used to the power distribution control of HESS in HEV. We verified the SC and AFNN control strategy by simulation and experiment based on the ADVISOR 2002 simulation software and the experimental platform, and the results show that the proposed control strategy can give full play to the advantages of HESS, and improve the energy storage performance of HEV. [ABSTRACT FROM AUTHOR]

• Modification-free retrofit of battery-alone storage with supercapacitors. • Thermal-electrical management of the battery for longer service life. • Peak battery current decreased by up to 26.20% under normal operation. • Battery temperature slope decreased by up to 38.15% under high temperature conditions. • Battery current maximized under low temperature conditions. Batteries are gaining momentum in the dawn of the global energy transition. Their propensity to early ageing remains, however, a serious concern. Reasons for this include thermal and electrical stresses experienced during operation. The combination of batteries with supercapacitors (SC) into hybrid energy storage systems (HESS) is currently regarded as an effective means of reducing electrical stress on batteries. However, battery-SC HESS still plays a marginal role in practice. Anticipating a possible resurgence of interest in this technology, controllers should be designed to enable easy conversion of existing battery energy storage systems (BESS) into battery-SC HESS. Currently, most control models in the literature would require significant modifications to the existing infrastructure, hindering the expected transition to the HESS. This paper introduces a fuzzy logic controller for plug-in SC aiming for straightforward conversion of BESS into battery-SC HESS. In addition to relieving batteries from fast-varying currents, the SC can contribute in the supply of slow-varying currents to further relieve the battery from electrical stresses and assist in regulating the battery temperature. Extensive simulations indicate that peak currents of batteries can be reduced by up to 26.20% under normal operation, and their temperature slope by up to 38.15% under high temperature conditions. [ABSTRACT FROM AUTHOR]

In an electric vehicle (EV), using more than one energy source often provides a safe ride without concerns about range. EVs are powered by photovoltaic (PV), battery, and ultracapacitor (UC) systems. The overall results of this arrangement are an increase in travel distance; a reduction in battery size; improved reaction, especially under overload; and an extension of battery life. Improved results allow the energy to be used efficiently, provide a comfortable ride, and require fewer energy sources. In this research, energy management between the PV system and the hybrid energy storage system (HESS), including the battery, and UC are discussed. The energy management control algorithms called Artificial Neural Network (ANN) and Aquila Optimizer Algorithm (AOA) are proposed. The proposed combined ANN–AOA approach takes full advantage of UC while limiting the battery discharge current, since it also mitigates high-speed dynamic battery charging and discharging currents. The responses' behaviors are depicted and viewed in the MATLAB simulation environment to represent load variations and various road conditions. We also discuss the management among the PV system, battery, and UC to achieve the higher speed of 91 km/h when compared with existing Modified Harmony Search (MHS) and Genetic Algorithm-based Proportional Integral Derivative (GA-PID). The outcomes of this study could aid researchers and professionals from the automotive industry as well as various third parties involved in designing, maintaining, and evaluating a variety of energy sources and storage systems, especially renewable ones. [ABSTRACT FROM AUTHOR]

The powertrain performance in an electric vehicle is fully dependent on the electrical and thermal constraints of the static converters ensuring the power transfer taking place between the energy storage systems and the electromechanical machines. These constraints depend on the architectures of the power converters, and their control strategies. Particularly, the maximal limits are reached in maneuvers such as hard regenerative braking circumstances. Indeed, braking recovery is a critical phase in the vehicle's operation, and its duration and intensity may strongly impact the vehicle's battery behavior or integrated hybrid storage system. The innovative objective of the paper is to propose an electrothermal multicriteria comparative study based on electrical and thermal criteria for two competitive powertrains. These semi-active power configurations (a 3-level DC/DC converter-based, and a Z-source converter-based) are implemented in a two-front wheel driven electric vehicle during extreme regenerative braking conditions. Open-loop and closed-loop controls were implemented in the Z-source using the maximal constant boost control with 3rd harmonic injection modulation technique. We considered two paralleled IGBT modules instead of the single shoot-through structure. Our approach is based on simulation during an extreme braking maneuver leading to heavy repercussions on the overall powertrain system. The aim is to investigate the challenging structure of the Z-source. Results showed that the proposed 3-level DC/DC-based topology has better performances in terms of power losses, efficiency, thermal behavior, and electromagnetic interference. [ABSTRACT FROM AUTHOR]

[Display omitted] • Portable solar-powered system with integrated supercapacitor-battery storage. • System controller switches between two independent modes: direct and off-grid. • Automatic hybrid mode with an algorithm to prioritizes a load support. • System verification under varying simulated sunlight intensity and outdoor scenarios. • Demonstration of loads usage in outdoor scenarios: sunny, mixed, and cloudy days. Increasing climate change-caused natural disasters calls for mobile self-powered backup solutions for rescue and survival. However, existing portable solar systems rely on single storage with high risk of suspension in emergency and prolonged cloudy period. This work presents a portable solar-dual storage system, which enables essential loads to function continuously regardless of weather. The system operates with a supercapacitor to buffer fluctuating solar power in the Direct mode, a battery-supercapacitor integration to enable extended low light load usage in the Off-grid mode, and a developed mode selector controller for overall energy management. Optimum size of the supercapacitor was determined to buffer the load and quickly recharge within the average low light period of 5–12 min. The simulated solar irradiance from 400-1000 W/m2 was utilized to verify automatic selection between two independent modes: direct and off-grid. Furthermore, outdoor behavior of the system has been investigated on a representative sunny, cloudy, and mixed day. Moreover, different combinations of DC and AC loads including an 80 W notebook with a power larger than the solar panel (20 W) have been demonstrated to function steadily for a few hours outdoor. This system's signatures include manual mode selection possibility by users and automatic switching ability to a direct mode for when the battery is completely discharged or malfunction for continuous operation of the loads. This portable solar-powered system can be used in variety of scenarios and provides clean solar energy to essential electrical appliances for lighting, communication etc., thus increasing the chance of survival during emergency. [ABSTRACT FROM AUTHOR]

Summary: Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium‐ion battery (LIB) and a supercapacitor (SC)‐based HESS (LIB‐SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal‐sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system‐level optimizations, include component sizing (CS) and power‐energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB‐SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB‐SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB‐SC HESS are provided to inspire researchers for further studies. Highlights: Lithium‐ion battery (LIB) and supercapacitor (SC)‐based hybrid energy storage system (LIB‐SC HESS) suitable for EV applications is analyzed comprehensively.LIB‐SC HESS configurations and suitable power electronics converter topologies with their comparison are provided.System‐level optimization of LIB‐SC HESS and generalized steps involved in implementing the optimization algorithm for component sizing (CS) and power‐energy management strategy (PEMS) are discussed.A rigorous study on CS and PEMS is presented to develop efficient LIB‐SC HESS.Current challenges and future recommendations for the development of LIB‐SC HESS for EV applications are provided. [ABSTRACT FROM AUTHOR]

In adverse sea conditions, changes in the propeller emergence depth can cause propulsion load fluctuations, resulting in significant variations in ship-integrated power systems (IPS). These fluctuations can severely affect the power quality of ships and the operational efficiency of electrical devices. To develop appropriate mitigation measures against the impact of wind and waves on IPS, a comprehensive model that can simulate the response characteristics of the hull and IPS for different heading angles in different sea conditions is necessary. This paper presents an integrated multi-system dynamic model capable of simulating the response characteristics of a ship's hull and its IPS under the wind and waves interference. A novel method is proposed to estimate propeller thrust and torque loss directly from the ship's pitch and roll angles. Utilizing this dynamic model, the responses characteristics of the hull and IPS in varying sea conditions have been investigated. The findings demonstrate that the model accurately captures variations in ship motions, as well as IPS voltage and frequency, under wind and waves interference. Additionally, this study explores the application of a hybrid energy storage system (HESS) to enhance the IPS power quality in the context of Sea State (SS) 7. Results indicate that the HESS has good potential to compensate for propulsion load fluctuations, thereby mitigating the negative impacts on the ship's IPS. • Calculating propeller thrust and torque losses for different heading angles in different sea states. • Dynamic performance of ship-IPS with different heading angles in different sea states. • Potential of HESS in mitigating fluctuations of IPS in adverse sea conditions. • Theoretical basis for effective power management and propulsion control strategy. [ABSTRACT FROM AUTHOR]

Battery cooling is crucial for electric vehicles' thermal safety, energy consumption, and battery life in hot climatic conditions. For electric vehicles with battery/supercapacitor hybrid energy storage system, battery cooling is deeply coupled with load power split from the electrical-thermal-aging perspective, leading to challenging thermal and energy management issues. This paper proposes a hierarchical multi-horizon model predictive control (MH-MPC) method to optimize battery cooling and energy management simultaneously. First, the electrical-thermal-aging coupling relationship between battery cooling and energy management is systematically analyzed. Then, by decoupling a centralized MH-MPC, an upper-level MH-MPC is designed to optimize the battery capacity loss cost and battery cooling cost by generating optimal compressor power, then a lower-level MH-MPC tends to minimize the battery capacity loss cost by allocating the total load power demand. The prediction horizon and sampling time are determined. Numerical results show that, compared with the centralized method, the proposed hierarchical method provides a lower battery capacity loss for long-term driving with only about 20% computation burden. Compared with standalone energy management without battery cooling, the total cost can be reduced by 12%–16% under long-term driving. Compared with optimizing energy management with Bangbang cooling, the battery degradation and total costs can be reduced by 15%–52% under short-term driving without deteriorating long-term performance. • First integrated battery thermal and energy management work for EVs with HESS. • Multi-horizon MPC for simultaneous battery cooling and power allocation optimization. • Hierarchical optimization for problem decoupling and better computation burden. • Excellent battery degradation and total costs for both short and long-term driving. [ABSTRACT FROM AUTHOR]

Hybrid energy storage systems (HESSs) play a crucial role in enhancing the performance of electric vehicles (EVs). However, existing energy management optimization strategies (EMOS) have limitations in terms of ensuring an accurate and timely power supply from HESSs to EVs, leading to increased power loss and shortened battery lifespan. To ensure an accurate and timely power supply from HESSs to EVs, this paper proposes a dual-layer multi-mode (DLMM) EMOS. This strategy comprises two layers. The upper layer is a backpropagation neural network (BPNN) model enhanced by the particle swarm optimization (PSO) algorithm. It is used for real-time HESS power demand prediction. In the lower layer, a HESS operational mode determination process is formulated, and an objective optimization function is established based on HESS power loss. Under constraints designed according to the HESS state parameters, the PSO algorithm is utilized to search for the optimal power allocation ratio of the HESS in real time. The proposed DLMM-EMOS strategy is capable of providing optimal power reference values for the batteries and ultracapacitors of the HESS. The DLMM-EMOS is tested on an electrical experimental platform using US06, NEDC, and WLTP driving cycles. Results indicate that the DLMM-EMOS effectively reduces the HESS power loss while enhancing the driving range of the battery. [ABSTRACT FROM AUTHOR]

Aiming at the problems of conventional plug-in hybrid electric vehicle (PHEV), a novel PHEV configuration called DH-PHEV is proposed based on double-rotor motor (DRM) and hybrid energy storage system (HESS). For improving the comprehensive efficiency and reducing the charging/discharging rate of battery, the comprehensive energy management strategy (CEMS) is studied. Firstly, a rule-based torque allocation strategy is proposed based on the DH-PHEV configuration, the DRM operating characteristics, and the HESS operating states. The torque allocation of DRM is optimized based on the sequential quadratic programming. Secondly, the power allocation between battery and supercapacitor is optimized based on the dynamic programming (DP), with the goal of reducing the charging/discharging rate of battery and minimizing the energy consumption. The CEMS for DH-PHEV is proposed based on the DP optimization results and DRM torque allocation. Finally, the feasibility and adaptability of DH-PHEV configuration and CEMS are verified. The results show that the adaptability of CEMS under different driving cycles is excellent, and the operating modes are reasonable. The comprehensive efficiency and the charging/discharging rate of battery are optimized effectively. Under 6 WLTCs and 16 actual vehicle driving cycles, the pure electric driving range for DH-PHEV is improved by 1.07 % and 4.36 %, respectively. • A novel PHEV configuration is proposed and its operating modes are analyzed. • For improving system efficiency, torque allocation is optimized based on SQP. • The power allocation strategy with DP joint rules is studied. • The comprehensive energy management strategy for this configuration is proposed. • The real-world operation data is collected and is used to verify the configuration. [ABSTRACT FROM AUTHOR]

This article proposes a novel energy management structure for electric vehicles, consisting of a supercapacitor and two types of batteries, to improve efficiency and navigable distance. The key features of a suitable energy storage system include high power and energy density, low cost and weight, minimal maintenance, and long life. Although batteries are the primary power storage source in electric vehicles, they have power limitations. Therefore, a high-power-density supercapacitor is added to the battery to create a hybrid energy storage system, reducing stress on the battery and increasing its lifespan. The article presents a new structure for hybrid storage systems based on the existence of a main battery, a replaceable battery, a supercapacitor, and a DC-DC converter. An active charge equalizer circuit for the main battery in standby mode and its control system are also introduced. A control method for dividing power between different sources under different conditions is presented, and the design parameters of the energy storage system are optimized using a genetic algorithm to minimize mass and losses. The performance of the proposed system is evaluated through simulation in MATLAB software and tested in a small-scale laboratory sample. The article also provides a detailed analysis of different working modes, including electric motor acceleration, low-speed mode, high-speed mode, and electric regenerative braking mode. Additionally, a control method for energy management between batteries and supercapacitors is introduced, aiming to increase efficiency. The results show that the proposed hybrid energy storage system can effectively improve the efficiency of electric vehicles. [ABSTRACT FROM AUTHOR]

Lithium-ion battery-based hybrid energy storage systems (ESSs) have been widely applied in various fields. Bidirectional DC/DC converters, crucial interfaces linking batteries and DC buses, serve as critical actuators for tasks such as DC bus regulation, on-line battery diagnosis, health-conscious energy management strategy, and fault tolerant control. Despite their importance, the control of bidirectional DC/DC converters in hybrid ESSs has rarely been independently discussed. Their control-related works are usually carried out by the unidirectional DC/DC converter modeling process with proportional–integral (PI)-type controllers. Therefore, recognizing the significance of pulsed power loads and the induced highly dynamic changes in battery voltage during real-world applications, this paper highlights the relevance of applying H ∞ -optimal controllers for bidirectional DC/DC converters based on its detailed control-oriented modeling process. To present a rigorous H ∞ -optimal controller synthesis process, this study includes a detailed experimental determination of the controller's key parameters. Furthermore, extensive numerical tests across diverse battery operation scenarios have been conducted to demonstrate the advantages of applying H ∞ -optimal controllers for battery-based bidirectional DC/DC converters in hybrid ESSs. • Presenting a detailed control-oriented bidirectional DC/DC converter modeling process. • Highlighting the experimental determination of key parameters for controllers. • Emphasizing the significance of employing H ∞ -optimal controllers for health-conscious battery control objectives. [ABSTRACT FROM AUTHOR]