Search

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]

Summary: To enhance the efficiency and prolong the battery life of hybrid energy systems equipped with battery and supercapacitor for a pure electric loader, this paper proposes a real‐time energy management strategy based on model predictive control (MPC). First, the working characteristics of the loader and the topology structure of the hybrid energy are analyzed. Second, considering the repetitive operation model of electric loaders, a neural network algorithm based on long short‐term memory is used to predict power and a dynamic programming algorithm is used as the rolling optimization solution for MPC strategy. Finally, the MPC is verified using the simulation in MATLAB/Simulink. The results indicate that compared with other strategies, the MPC strategy can improve hybrid energy efficiency and reduce the maximum battery current fluctuations. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system. [ABSTRACT FROM AUTHOR]

A novel framework for adaptive energy management, rooted in deep learning principles, is proposed to minimize fuel consumption in extended‐range electric vehicles amidst intricate driving scenarios. This innovative approach integrates a long short‐term memory (LSTM) network for pattern recognition across three driving patterns and an adaptive fuzzy controller. To mitigate the impact of poor hyperparameter selection on recognition accuracy, Gray Wolf Optimization is employed to optimize the hidden layer nodes, training times, and learning rate of the LSTM. Simultaneously, a genetic algorithm is utilized to optimize the vertex coordinates of the fuzzy control membership function, enabling the adaptive adjustment of parameters in the fuzzy energy management strategy. The condition recognition model accurately identifies the vehicle's driving status and seamlessly transitions to an energy management strategy tailored to the present conditions. This ensures optimal operation, enhancing overall fuel efficiency and performance. The simulation results robustly validate the efficacy of this approach: the GWO‐LSTM network achieves an impressive 97.7% accuracy in recognizing working conditions, surpassing the 88.9% accuracy of the traditional LSTM network. Furthermore, the fuel consumption reduction achieved by the adaptive fuzzy energy management strategy amounts to 11.9% compared with the conventional fuzzy energy management approach. This outcome underscores the tangible enhancement in vehicle fuel economy resulting from the seamless integration of deep learning techniques. [ABSTRACT FROM AUTHOR]

In electric vehicles, the battery is a key component that calculates vehicle performance. Due to their greater efficiency and the lower cost of power, charging an electric vehicle is more affordable than purchasing gasoline or diesel for travel needs. To increase the battery's lifespan, the accuracy of the battery model for electric vehicles must be enhanced. To operate at their peak efficiency, batteries must be managed properly by a battery management system. This research illustrates the functioning of a rechargeable electric vehicle battery's charging system. There are several types of rechargeable batteries used, including lead acid, lithium‐ion, and nickel‐cadmium batteries. In recent trends, are used in the majority of cars use rechargeable lithium‐ion batteries. In general, accumulated heat, rapid utilization, and total energy throughput have an impact on the battery life of electric vehicles. In this study, 50 papers were analyzed about battery charging in an electric vehicle, which utilized different measures, as well as achievements attained by various methods. This survey reviewed the information from a different journal, along with their advantage, disadvantages, and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of electric vehicles by offering a thorough interpretation of the methods currently in use. [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]

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]

Hydraulic hybrid technology can further improve the economy of electric vehicles (EV). In this article, a novel electric‐hydraulic power coupling vehicle (EHPCV), which is endowed with multiple drive modes and energy regenerative braking modes, is investigated. The electric‐hydraulic power coupler (EHPC) is an innovative device for realizing power coupling and conversion. The design and optimization of the energy management strategy (EMS) are key to ensuring the efficient and stable operation of the hybrid electric vehicle. Based on the analysis of energy flow, a rule‐based EMS (RB‐EMS) is built. To achieve more reasonable energy management of the EHPCV in random driving environments, the article proposes an optimization framework for the EMS based on multiple driving cycles. More precisely, a multiobjective optimization mathematical model is established with the goal of maximizing the battery state of charge and minimizing velocity error. Moreover, the optimal Latin hypercube design is used to select the design variables that have a significant impact on the optimization objective. Definitively, the nondominated sorting genetic algorithm II algorithm combines with RB‐EMS to optimize the control parameters. The verification results show that the optimized EMS enables the EHPCV to have superior economic advantages. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) utilizing hybrid energy sources is a significant step toward a sustainable future in the transportation industry. The electric three‐wheeler (3W) considered in the proposed work includes three sources—battery, supercapacitor (SC), and photo‐voltaic (PV) panels. In battery electric vehicles (BEV), battery life cycle, energy efficiency, and performance are affected by variations in driving conditions that inhibit their wider adoption. The main focus of the proposed intelligent hybrid energy management strategy (IHEMS) is to enable the vehicle to adaptively manage and diminish the effects of load fluctuations due to varying conditions. IHEMS diverts the load fluctuations to the SC bank by ensuring an effective absolute energy sharing among the sources with a fuzzy logic control algorithm. PV energy is utilized to assist the battery during sunny days. Performance of the EMS in hybrid source EV is analyzed in MATLAB/SIMULINK environment with a combination of three different standard real‐time driving profiles (NYCC, Artemis Urban, WLTP class‐1). Proposed EMS reduces peak battery power by 20% and 14.35% and improves battery life by 16.4% and 11.4% compared to BEV and conventional EMS, respectively. This proves that the proposed EMS exhibits adaptive energy management irrespective of the driving conditions and ensures improved battery performance and longevity. [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]

To compensate for the slow dynamic response of fuel cell system (FCS) in fuel cell hybrid electric vehicles (FCHEVs), battery and supercapacitor are widely used as auxiliary supply energy sources in FCHEVs. How to reasonably allocate the required power among the three energy sources, reduce the fuel consumption and the SOC fluctuations of auxiliary energy sources becomes a tough problem to be solved. In this paper, we propose an energy management strategy (EMS) based on fuzzy control, considering the multiple constraints of hydrogen consumption and auxiliary energy SOC fluctuations of FCHEV. In this paper, the ant colony algorithm (ACA) is used for the first time to optimize the membership functions parameters of the dual fuzzy controller. The results obtained are verified in Matlab/Simulink. The proposed ACA optimized EMS can effectively reduce the equivalent hydrogen consumption and the SOC fluctuation of battery and supercapacitor by proving in four typical driving conditions. [ABSTRACT FROM AUTHOR]

The high global demand for fossil fuels and resulting climate‐change effects due to air pollution have led to the rapid development of renewable energy sources. Battery and proton exchange membrane fuel cells have high power densities and high energy, and supercapacitors can provide peak power. These devices have the advantages of high thermal efficiencies and no carbon emissions during use, which have far‐reaching applications in the fields of hybrid vehicles and ships. To compensate for the slow dynamic response of fuel cells, batteries or supercapacitors are often used as auxiliary power sources to form hybrid powertrains in practice. Therefore, to take full advantage of the hybrid system, energy coordination between multiple energy sources using an energy management strategy is important. The equivalent consumption minimization strategy has been widely investigated due to its simplicity, small operation, good control, and real‐time optimization. In this paper, the topologies of fuel cell hybrid power systems are first systematically classified and characterized. Then, the calculation of equivalent hydrogen consumption for a battery, fuel cell, and supercapacitor is reviewed. To reduce overall hydrogen consumption and extend the life of fuel cells and batteries, an equivalent hydrogen consumption minimization strategy is applied to the energy management of hydrogen fuel power systems. [ABSTRACT FROM AUTHOR]

Microgrid is an important part of smart grid, and the system management method of microgrid has been widely concerned. In this paper, a system modeling method based on energy circuit is proposed to model the microgrid system structure. The branches are represented by logic variables and state variables. The model of microgrid can be easily generated on the computer by inputting the parameters of nodes and branches. Then, a method of demand side response (DSR) based on probability is proposed to solve the problem of uncertain load estimation. On this basis, the DSR method is applied to energy management strategy (EMS), in which the return coefficients of loads are changed. The simulation results show that the modeling method is effective and feasible for the energy management system of microgrid. The EMS including demand side response can significantly reduce the start‐up time of diesel generator (DSL) and the operation cost of microgrid. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A hierarchical energy management strategy (EMS) integrating self‐adaptive adjustment and Pontryagin's minimum principle‐based optimization is proposed for a fuel cell hybrid electrical vehicle. First, the proposed EMS estimate the future power requirement by using Markov chain Metropolis–Hastings sampling, second the parameters of the low‐pass filter is adjusted according to the state of charge estimation of the super capacitor, finally the best fuel economy is realized through Pontryagins minimum principle‐based optimization. The evaluation of the EMS is verified via simulation and real driving cycle, the results are compared with another global optimization EMS. The results show that the proposed EMS can fully exert the function of the super capacitor and improve the fuel economy 0.3% and 8.8% for simulation and real driving cycle, respectively. [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]

A fuel‐cell vehicle (FCV) mainly depends on the fuel‐cell system (FCS) to provide the power to drive the vehicle. A battery is also integrated as the second power source, which has the advantage of high density and quick dynamic response. Since two power sources co‐exist in the vehicle, the control balance of how to distribute the transient power requirement between the FCS and the battery has a great impact on the performance of the FCV. In this paper, the dynamic model of FCS, the battery, the electric motor (EM), and its control unit are first established. Then the parameters in these models are determined by using the least squares method to achieve an analytical description of the power‐train system. According to the FCV model, the power balance control based on the best state feedback is realized. This optimized control is verified over three different typical driving cycles through simulation and a bench experiment. The results show that the balance control has a practical application for FCV in improving the fuel economy and the dynamic performance. [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]

This study introduces a type of solid‐state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic‐assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real‐time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real‐time power allocation which improves the efficiency of the photovoltaic‐based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power. [ABSTRACT FROM AUTHOR]

This study proposes an energy management strategy (EMS) for a dual‐mode hybrid locomotive equipped with a fuel cell, supercapacitors, and batteries, and intermittent access to an electrified overhead catenary. It is inspired by the Ragone plot and does not consider information or predictions of future load consumption. It aims to reduce a cost function that considers the cost of hydrogen, the electricity consumed from the network, and the energy sources' degradation. The EMS focuses on maximising the energy recovered during braking. The study introduces a methodology to tune the EMS parameters. Two study cases are used to evaluate the EMS. In the evaluation driving profile, typical for a French freight train, the braking energy is around 12.8% of the total energy. With the proposed EMS, the energy recovered is around 99.8% of the total braking energy. A second EMS not oriented to reduce the energy in the braking resistor is also evaluated. The energy recovered with this strategy is around 91.5% of the total braking energy. The global energy reduction is around 1.1% compared with the second EMS and 12.8% without energy recovering. These results show a real opportunity to increase the energy recovered during braking. [ABSTRACT FROM AUTHOR]

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

In this study, a novel energy management strategy (EMS) with two degrees of freedom is proposed for hybrid energy storage systems consisting of supercapacitor (SC) and battery in islanded microgrids. The proposal introduces two degrees of freedom including an adaptive high‐pass filter cut‐off frequency fc and a charge/discharge coefficient kb, according to the SC and battery state of charge (SOC), respectively. The fc regulates SC current in the transient process and the kb controls battery current in the steady‐state. Therefore, appropriate power distribution that the SC suppresses the transient power fluctuations while the battery supports average power demand is realised, over‐charging, and over‐discharging are avoided and the current stress of the battery during the shut‐down process is reduced. Moreover, three operation modes for isolated DC microgrids, comprising power‐sharing mode, battery‐only mode, and extreme mode are demonstrated. Hence, the DC microgrid can remain stable for the whole time, achieve seamless transitions from the power‐sharing mode to the two others, and provide faster SOC recovery of the SC. The effectiveness of the proposed EMS is validated by experimental studies with a prototype of the simplified DC microgrid. [ABSTRACT FROM AUTHOR]

In this paper, in order to extend battery lifespan and lift power performance in hybrid batteries/supercapacitors electrical vehicles, a new energy management strategy is proposed based on model prediction control and adaptive method. Firstly, models of batteries and supercapacitors are built, which are then adapted and simplified to develop state space expression. Secondly, a series of reference values of battery power and supercapacitor state of charge for model prediction methods are properly calculated by the adaptive method. Thereafter, an energy management strategy based on the model prediction control method is designed to allocate the output power of batteries and supercapacitors within constraints, which guarantees batteries lifespan and the power performance of vehicle. Finally, simulation and experiment results are provided to evaluate battery lifespan and power performance of vehicles under HWFET and UDDS road conditions. The results obtained show that the proposed strategy, compared with the former methods, reduce average power and power variation of batteries, and effectively utilize supercapacitors depending on the power demand, which can extend battery lifespan and lift the power performance of vehicle. [ABSTRACT FROM AUTHOR]

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

The propulsion system of a medium‐voltage dc (MVDC) ship is subject to large thrust/torque variations due to interactions of the ship and the propeller with sea waves. These variations induce steep power fluctuations on the MVDC bus, adversely impacting the stability, efficiency and power quality. Hybrid energy storage system (HESS) is a promising solution for mitigating these power fluctuations. Dictating the energy that the HESS components must deliver/absorb, the energy management strategy (EMS) impacts the size/capacity of the energy storage system (ESS). Based on this consideration, sizing and EMS of a battery/supercapacitor (SC) HESS are jointly optimised by using a deep reinforcement learning‐based method. The proposed method splits the power between the HESS components such that while the operational constraints are satisfied, energy storage size and losses are minimised. Its features are adaptability to varying sea states, real‐time implementation feasibility, while obviating the requirement for knowledge of the ship propulsion power profile. The efficacy of the joint sizing/EMS on reducing the ESS size is validated by comparing the sizes when battery‐only, SC‐only and HESS are employed in the MVDC shipboard power system. Real‐time implementation feasibility and adaptability to various ship propulsion power profiles is confirmed through real‐time simulations. [ABSTRACT FROM AUTHOR]

Aiming to achieve the flexible operation of distribution network (ADN), an energy management strategy of ADN with integrated distributed wind power and smart buildings is proposed in this study. First, based on the thermal storage characteristics of buildings, the resistor–capacitor model is used to develop an energy consumption prediction model of smart buildings. Second, the smart buildings are modelled as flexible resources of ADN. A multi‐objective ADN model is further formulated based on analytic hierarchy process (AHP) by comprehensively considering constraints of power grid and smart buildings. Then, the unified model of ADN is transformed by second‐order conic relaxation to guarantee the global optimal solution. Finally, the scheduling results for the unified model of ADN under different control methods for heating ventilation and air‐conditioning (HVAC) systems are compared in the winter heating scenario. In addition, the impact of the demand response capability for smart buildings on the economic and secure operation of the ADN is further analysed. Numerical studies demonstrate that the proposed method can increase the penetration of local wind power, reduce peak‐valley load difference and network loss of the grid while ensuring the comfort temperature of customers, and further achieve the flexible operation of ADN. [ABSTRACT FROM AUTHOR]

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]

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

This paper proposes an efficient hybrid approach–based energy management strategy (EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasi‐oppositional‐chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS‐RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid‐connected MG system is continuously tracked by the RF technique. The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two‐strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques. [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]

This study presents a novel energy management strategy (EMS) which outperforms the published strategies developed for an international technology challenge, IEEE Vehicular Technology Society (VTS) Motor Vehicles Challenge 2017. The objective of the strategy is to minimise the cost of ownership of a low‐power (15 kW) fuel cell (FC)‐battery electric vehicle. Both the fuel consumption cost and power sources degradation costs are combined to represent the total cost of ownership. The simple adaptive rule‐based strategy optimises the FC operation during low‐traction power operation and switches to battery charge‐sustaining operation for high traction power operation. This minimises fuel consumption and increases the lifetimes of the FC and of the battery. The strategy is then compared with the EMS of the 2015 Toyota Mirai, and the challenge vehicle model is modified to capture the learnings from the Mirai. Finally, a cost‐benefit analysis for a plug‐in FC vehicle is considered in order to improve FC lifetimes and to reduce costs for short drive cycles. [ABSTRACT FROM AUTHOR]

A layered management and hybrid control strategy based on hybrid automata and random forest for the microgrid is proposed in this study. It is used to realise energy management and multi‐modal coordinated switching of microgrid system to ensure high security, stability and high efficiency. Management and control strategies are implemented through a three‐layer microgrid architecture based on multi‐agent system theory. An energy management strategy is designed for the microgrid system in the upper layer agent structure. A coordinated control strategy is designed in the middle layer agent structure. A local control strategy is designed into the bottom layer agent structure. In the middle layer coordinated control strategy, the hybrid automata is used to characterise the complex logical features of each underlying unit and trigger the switching of working modes according to the condition change. The random forest algorithm is used to analyse the running state and reduce the number of switching. Finally, the simulation results verify the effectiveness of the proposed control strategy in different scenarios. [ABSTRACT FROM AUTHOR]

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

Energy metabolism has received much attention as a potential driver of repeatable among‐individual differences in behaviour (animal personality). Several factors have been hypothesized to mediate this relationship. We performed a systematic review with a meta‐analysis of >70 studies comprised of >8000 individuals reporting relationships between measures of maintenance metabolic rates (i.e. basal metabolic rate, resting metabolic rate, and standard metabolic rate) and behaviour. We evaluated support for three hypothesized mediators: (i) type of behaviour, (ii) opportunities for energy re‐allocation, and (iii) magnitude of energetic constraints. Relationships between measures of maintenance metabolic rate (MR) and behaviour are predicted to be strongest for behaviours with strong consequences for energy turnover (acquisition or expenditure). Consistent with this, we found that behaviours with known consequences for energy gain (e.g. foraging, dominance, boldness) or expenditure (e.g. maximum sprint speed, sustained running speed, maximum distance travelled, etc.) had strong positive correlations with MR, while behaviours with putatively weak and/or inconsistent associations with net energy gain or loss (e.g. exploration, activity, sociability) were not correlated with MR. Greater opportunities for energy reallocation are predicted to weaken relationships between MR and behaviour by creating alternative pathways to balance energy budgets. We tested this by contrasting relationships between MR and behaviour in ectotherms versus endotherms, as thermoregulation in endotherms creates additional opportunities for energy reallocation compared with ectotherms. As predicted, the relationship between behaviour and MR was stronger in ectotherms compared with endotherms. However, statistical analyses of heterogeneity among effect sizes from different species did not support energy re‐allocation as the main driver of these differences. Finally, we tested whether conditions where animals face greater constraints in meeting their energy budgets (e.g. field versus laboratory, breeding versus non‐breeding) increased the strength of the relationship between MR and behaviour. We found that the relationship between MR and behaviour was unaffected by either of these modifiers. This meta‐analysis provides two key insights. First, we observed positive relationships of similar magnitude between MR and behaviours that bring in net energy, and behaviours that cost net energy. This result is only consistent with a performance energy‐management model. Given that the studies included in our meta‐analysis represent a wide range of taxa, this suggests that the performance model may be the most common model in general. Second, we found that behaviours with putatively weak or inconsistent consequences for net energy gain or expenditure (exploration, activity, sociability) show no relationship with MR. The lack of relationship between MR and behavioural traits with weak and/or inconsistent consequences for energy turnover provides the first systematic demonstration of the central importance of the ecological function of traits in mediating relationships between MR and behaviour. [ABSTRACT FROM AUTHOR]

In a microgrid, a rational network is necessary to improve the economy, reliability, adaptability, security, and coordination of the supply with the load demand. A flexible network optimal planning model of autonomy microgrid is proposed. Several uncertainties including the output power of distributed generation and load power are considered and each uncertainty factor is divided into several typical scenarios. The probability of each scenario is calculated by asymptotically optimal scenario analysis. The annual investment cost, the fault outage cost expectation, the annual cost regret value expectation and penalty function, the violating electrical constraints degree, and the source‐network‐load equilibrium degree are applied to represent the flexibility of the planning model. Allocation of energy storage system capacity is considered in the network planning model, which can reduce the negative effects on the microgrid in extreme scenarios. An energy management strategy is constructed to coordinate the distribution of source and load. The tree structure encoding is employed to ensure the connectivity of the network. Dynamic Pareto set‐based particle swarm optimisation is adopted for the solution of the network planning model. Finally, the calculation results of planning schemes are demonstrated by radar chart, and the optimal one is selected by its comprehensive evaluation value. [ABSTRACT FROM AUTHOR]

In this study, a double fuzzy control strategy for the parallel hybrid electric vehicle (HEV) is proposed, then based on the genetic algorithm (GA) to get better simulation results, and the results are verified by dynamic programming (DP) optimisation. First, the energy management strategy is established by fuzzy control theory. On this basis, considering braking energy recovery, this study designs a double fuzzy vehicle energy control strategy. A simulation analysis of the above two control strategies is carried out in urban dynamometer driving schedule, and the comparison with the work efficiency of the engine and fuel economy performance, respectively, is made; the simulation results show that the double fuzzy control strategy can effectively improve the HEV performance. In order to make rule base more accurate, this study also uses a GA to optimise the fuzzy control rules of the fuzzy controller. Then the DP is used to optimise the energy control strategy and obtain optimal results. The results verified that the design of fuzzy controllers is correct, and the optimised fuzzy control strategy by GA can improve the work efficiency of the engine and fuel consumption. [ABSTRACT FROM AUTHOR]

One of the key strategies to reduce fuel consumption and emissions is given by engine downsizing, together with turbocharging. Under this aspect, the possibility to couple an electric drive to the turbocharger to recover the residual energy of the exhaust gases is becoming more and more attractive. However, additional energy coming from an electric turbo compound (ETC) application has to be managed by the vehicle powertrain through a proper energy management strategy. This study shows the numerical results of a research programme under way focused on the comparison of the benefits resulting from the application of two ETC configurations to a medium size vehicle equipped with a small four cylinders turbocharged diesel engine (1561 cm3). Starting from the experimental maps of the turbine and compressor, the complete engine model was created using a commercial one‐dimension code. The numerical activity then moved to the whole vehicle modelling. Engine results have been used to properly define a powertrain architecture and energy management strategy in order to maximise the benefits coming from the single‐ETC or the dual‐ETC solution. Finally, vehicle performance and energy flows have been analysed in different real driving conditions and compared to the original vehicle without ETC. [ABSTRACT FROM AUTHOR]

In view of the tremendous benefits induced by cooperative operation of microgrids, such as reduced power loss, lower operational cost and load peak reduction, this study presents a new energy management strategy for coalition forming microgrids under an electricity market environment. The proposed framework models the time‐variant and intermittent attribute of renewable energy sources using a worst‐case transaction mechanism. The energy management strategy minimises the net operating cost of the microgrids forming the coalition, which not only includes the cost of distributed generation but a worst‐case net transaction cost to account for the intermittency in renewable energy‐based sources. Extensive numerical results are shown to corroborate the efficacy of the proposed framework. [ABSTRACT FROM AUTHOR]

Summary: A worldwide shift headed for a greener and low emissions will necessitate remarkable advancement in the way in which the energy is being produced and used. The factors such as climate changes induced by pollution, progressively more strict emissions norms for vehicles, depletion of petrol/diesel along with instability in their prices for transportation systems, play a vital role in the improvisation of technology involved in conventional vehicles. The hybrid electric vehicles (HEVs) are on the peak of the list of choices available for clean vehicle technologies. The various architectures of HEV, different methodologies of hybrid vehicle, are focused in this paper. The design criteria and optimization techniques with reference to the driving cycle is also elucidated. The various electric drives used for HEV are discussed in this paper. Also, the different electric propulsion systems are explained. To improve the fuel economy and emission of hybrid power system, control strategies are very significant. Researchers concentrate in optimizing the performance of HEV. [ABSTRACT FROM AUTHOR]

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

This paper deals with the modeling of electric vehicles using hybrid power source: fuel cell/supercapacitor. The fuel cell is connected to the direct current (DC) Bus via a boost converter and satisfies the load average power requirements. The supercapacitor is linked to the DC-Bus through a buck-boost converter and ensures the transient power requirements. A permanent-magnet synchronous motor is used as traction motor and connected to the DC-Bus through a 3-phase inverter. We propose an energy management technique, based on the urban driving cycle (ECE-15 driving cycle) and the extraurban driving cycle, with the use of conventional proportional-integral (PI) controllers. The main goals are as follows: first, to avoid fast operation of the fuel cell current so as to ensure optimal operation of the fuel cell system, and second, the supercapacitor allows a satisfactory dynamic of the DC-Bus so as to provide the required energy for the car transients so as to meet both urban and extraurban driving cycles. The performances of the proposed strategy are evaluated through some simulations dedicated to electric vehicle applications. [ABSTRACT FROM AUTHOR]

This study presents an approach of the voltage regulation of DC bus for the photovoltaic energy storage by using a combination of batteries and supercapacitors (SCs). The batteries are used to meet the energy requirements for a relatively long duration, whereas the SCs are used to meet the instantaneous power demand. The energy management strategy is developed to manage the power flows between the storage devices by choosing the optimal operating mode, thereby to ensuring the continuous supply of the load by maintaining the state‐of‐charge (SoC) of SCs (SoCsc) and the SoC of the batteries (SoCbat) at acceptable levels. This energy management strategy is performed by using the fuzzy logic supervisor. The validation results prove the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper addresses the energy management strategy (EMS) for a fuel cell hybrid electric vehicle (FC‐HEV). The fuel cell system (FCS) is a multi‐physics system, and consequently, its energetic performances depend on the degradation and on the operating conditions. The maximum power (MP) and the maximum efficiency (ME) points of the FCS are unique but they move with operating condition variations. Thus, developing an extremum seeking process (ESP) for both MP and ME tracking is a challenging task. In the ESP, models are identified online by using an adaptive recursive least square (ARLS) method to seek a variation in the FCS performances. Then an optimisation algorithm is used on the updated model to find the MP and the ME points. The ESP is incorporated into a hysteresis power splitting control (HPSC). A MP mode or a ME mode can be set based on the energy storage level (battery pack). The effectiveness of the proposed MP‐ and ME‐ESP EMS is demonstrated by conducting experimental studies on two FCSs with different levels of degradation. It was demonstrated that the classical EMS based on maps are not valid when the operating parameters vary because of the level of degradation change. [ABSTRACT FROM AUTHOR]

This study presents the multiple energy storage elements usability for ships using a passive hybrid topology. The considered hybridisation is based on a passive parallel topology connecting NiMH batteries and SuperCapacitors to a DC power distribution by a bidirectional DC/DC converter. The overall propulsion architecture is a hybrid series system where an engine–generator group is the main energy source and the multiple energy storage system (ESS) answer the intermittent power demanded by the on‐board loads. First, the multiple ESS is sized using voltages, storage elements' characteristics, and typical power demand profile. Thereafter, a control scheme of this hybridisation is deduced through a cascade of current and voltage linear controllers. The energy management strategy based on the stability of the DC power distribution voltage and the inherent storage elements' characteristics is fully addressed in order to reduce weight and space on‐board, fuel consumption, pollution, and optimise the global efficiency. Experimental results show that the engine–generator fulfil a constant power, meanwhile the multiple ESS stabilises the DC‐link voltage with unknown power demand profile. The effectiveness of the proposed passive hybrid topology with reduced control layer complexity based on the DC‐link voltage stabilisation as an energy management strategy is validated. [ABSTRACT FROM AUTHOR]

As microgrid system is still in the stage of demonstration application, microgrid system has to satisfy more stringent requirements for grid connection: 1. In order to ensure the security of grid, the output power of microgrid system is less than the factory load, not sending power to the grid. 2. The power fluctuations of microgrid system must meet the demands of National Standard. In order to improve the output characteristics of microgrid system applied in industrial park to meet the requirement of grid connection, an energy management strategy based on the ultra short term power prediction and feed‐forward control is proposed. Through the ultra short term power prediction, the output power of microgrid system is effective controlled to track the factory load, not sending power to the grid. Through feed‐forward control of battery energy storage systems (BESS), the power fluctuations of microgrid system are smoothed to meet the demands of National Standard. On the basis of real time digital simulator (RTDS) and energy management controller, the hardware in the loop (HIL) experimental platform is built to verify the effectiveness of algorithm. The security and stability operation of MW microgrid demonstration project verified the validity of the energy management strategy. [ABSTRACT FROM AUTHOR]

This study presents a control scheme for hybridised electric vehicles with an online power follower management strategy. The considered hybridisation is based on an active parallel topology composed of two bidirectional DC–DC converters, connecting NiMH batteries and SuperCapacitors. First, a control scheme of this hybridisation is deduced through a cascade of current and voltage linear controllers, complemented with the decoupling method. Thereafter, an energy management strategy based on a power follower is developed using an efficiency map of the hybridised feeding system. A reduced‐scale prototype has been built to analyse the performance of the control scheme with an online power follower strategy under realistic driving cycles. The experimental results show the effectiveness of the proposed control layer under an online rule‐based management strategy. [ABSTRACT FROM AUTHOR]

A hybrid power system based on a fuel cell (FC) and an energy storage system appears to be very promising for satisfying the high energy and high power requirements of automotive applications in which the power demand is impulsive rather than constant. This paper deals with the use of a hybrid energy storage system with the battery (BAT)/ultracapacitor (UC) as ancillary power source in FC electric vehicles. The energy management strategy (EMS) is one of the most important issues for the efficiency and performance of such systems. The designed EMS uses a splitting method, allowing a natural frequency decomposition of the power demands. It takes into account the slow dynamics of FC and the state of charge of the UC and BAT. A simulation is conducted using MATLAB/SIMULINK software in order to verify the effectiveness of the proposed control strategy. It confirms the performance of the control method and also demonstrates the robustness and stability of the control strategy with good tracking response (transient performance), low overshoot, zero steady-state error, and control flexibility during a power demand cycle. [ABSTRACT FROM AUTHOR]