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Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

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.

Lithium-ion batteries are widely used in the electric vehicle industry due to their recyclability and long life. However, a failure of lithium-ion batteries can cause some catastrophic accidents, such as electric car battery explosion fires and so on. To prevent such harm from occurring, it is essential to monitor the remaining useful life of lithium-ion batteries and give early warning. In this paper, an adaptive noise reduction approach is proposed to predict the RUL (Remaining Useful Life) of lithium-ion batteries, which uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) combined with wavelet decomposition to achieve adaptive noise reduction decomposition, and then inputs the obtained IMF (Intrinsic Mode Function) components into LS–RVM (Least Square Relevance Vector Machine) for training, prediction, and reconstruction, so as to achieve high-precision prediction of RUL. Moreover, in order to verify the validity of the model, the model in this paper is compared with other common models. The results demonstrate that the RMSE, MAPE, and MAE of the proposed model are 0.008678, 0.005002, and 0.006894, and that it has higher accuracy than the other common prediction models.

In the pursuit of more efficient vehicles on the world’s roads, the vehicle thermal management system has become a limiting factor when it comes to EV range and battery life. In extreme climates, if the thermal system cannot pull down or warm up the EV powertrain in a timely manner, the battery is at serious risk of capacity loss or accelerated degradation. As waste heat is inherently limited with EVs, the way in which we provide the heat for warm-up must be as efficient as possible to reduce the load on the battery. In this paper, a revolutionary waste heat recovery (WHR) thermal management system designed by Tesla, nicknamed the ‘Octovalve’, is described, modelled, and simulated. This paper contributes to collective knowledge by presenting an in-depth breakdown of the key operating modes and outlining the potential benefits. Modelled in the multidomain Simulink Simscape software, the octovalve’s performance is directly compared to a typical EV WHR thermal management system. The system under analysis is shown to significantly reduce EV energy consumption and battery load during warm-up but at the cost of overall warm-up time. Unlike any other WHR system found in literature, this system has a heat pump with can perform air conditioning and heat pump tasks simultaneously, which is shown to have a remarkable impact on energy efficiency and battery life.

This paper aims to analyse two energy storage methods—batteries and hydrogen storage technologies—that in some cases are treated as complementary technologies, but in other ones they are considered opposed technologies. A detailed technical description of each technology will allow to understand the evolution of batteries and hydrogen storage technologies: batteries looking for higher energy capacity and lower maintenance, while hydrogen storage technologies pursuing better volumetric and gravimetric densities. Additionally, as energy storage systems, a mathematical model is required to know the state of charge of the system. For this purpose, a mathematical model is proposed for conventional batteries, for compressed hydrogen tanks, for liquid hydrogen storage and for metal hydride tanks, which makes it possible to integrate energy storage systems into management strategies that aim to solve the energy balance in plants based on hybrid energy storage systems. From the technical point of view, most batteries are easier to operate and do not require special operating conditions, while hydrogen storage methods are currently functioning at the two extremes (high temperatures for metal and complex hydrides and low temperatures for liquid hydrogen or physisorption). Additionally, the technical comparison made in this paper also includes research trends and future possibilities in an attempt to help plan future policies.

Promoting the development of green technologies and replacing fossil fuel vehicles with electric ones can abate the environmental anxieties and issues associated with energy supply security. The increasing demand for electric vehicles requires an upgrade and expansion of the available charging infrastructure to accommodate the fast public adoption of this type of transportation. Ethiopia set a pro-electric cars policy and made them excise-free even before the first electric vehicle charging stations were launched by Marathon Motors Engineering in 2021. This paper presents the first ever technical, economic and environmental evaluation of electric vehicle charging stations powered by hybrid intermittent generation systems in three cities in Ethiopia. This paper tests this model using three different battery types: Lead-acid (LA), Flow-Zince-Bromine (ZnBr) and Lithium-ion (LI), used individually. Using these three battery technologies, the proposed hybrid systems are then compared in terms of system sizing, economy, technical performance and environmental stability. The results show that the feasible configuration of Solar Photovoltaic (PV)/Diesel Generator (DG)/ZnBr battery systems provide the lowest net present cost (NPC), with values of $2.97M, $2.72M and $2.85M, and cost of energy (COE), with values $0.196, $0.18 and $0.188, in Addis Ababa, Jijiga and Bahir Dar, respectively. Of all feasible systems, the Wind Turbine (WT)/PV/LI, PV/LI and WT/PV/LI configurations have the highest values of NPC and COE in Addis Ababa, Jijiga and Bahir Dar. Using this configuration, the results demonstrate that ZnBr battery is the most favorable choice because the economic parameters, including total NPC and COE, are found to be lowest.

This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of estimation methods. A reverse-time Kalman estimator is proposed for this task. Accurate SOC estimations are crucial for optimizing the utilization of multiple UAVs in a collaborative manner, ensuring the efficient use of energy resources and maximizing mission success rates. Experimental results for LiFePO4 batteries are provided, showing the capabilities of the proposal for the assessment of online SOC estimators.

This paper focuses on the statistical evaluation of various operating characteristics of electric buses. The data obtained for statistical evaluation come from practice. In this paper, we focus on electricity consumption—an important aspect of electric bus operation. The ambient temperature significantly affects electricity consumption. In this paper, we use applied mathematics—correlation analysis, we accurately identify the effect of temperature on the consumption of the electric bus. Our next goal was to define the relationship between the loss of energy from the battery and driving power. We used regression analysis to describe this relation. Our article also includes an example of the practical use of ANOVA analysis in identifying a statistically significant effect of a particular vehicle on average consumption. We also show results from previous research and compare two different types of electric buses in operation.

In this work, we consider a problem from the field of power-aware scheduling in which a fleet of electric vehicles have to be charged in a minimum time. Each vehicle is equipped with a lithium-ion battery of a given capacity. The initial power used for charging each battery is known, whereas it is assumed that the power drops to zero at the moment when the battery gets fully loaded. The power usage function is linear and decreasing. The charging jobs are nonpreemptable and independent, whereas the total available amount of power is limited. The objective is to minimize the schedule length. In this paper, we analyze the case of a problem with identical jobs that already cover a wide variety of practical situations. By employing inverses of natural numbers, similar to harmonic series, we prove two properties of this case, and we also discuss the phenomenon of the stabilization of the difference between the start times of two successive jobs in a schedule. We also take under examination a few special cases of the problem. Some conclusions and directions for future research are given.

Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disconnect the input power to load. To ensure optimal performance and avoid overloading issues, the EV system needs sophisticated control. This work introduces a machine-learning-based controller using an artificial neural network to solve these problems. This paper describes the detailed power management control methodology using multiple sources like solar PV, fuel cells, and batteries. Novel control with an instantaneous reference current scheme is used to manage the input power sources to satisfy the power demand of electric vehicles. The proposed work executes the power split-up operation with standard and actual drive cycles and maximum power point tracking for PV panels using MATLAB Simulink. Finally, power management with a machine learning technique is implemented in an experimental analysis with the LabVIEW software, and an FPGA controller is used to control a 48 V, 1 kW permanent-magnet synchronous machine.

Electrically powered rail transport is constantly increasing in order to meet the high demand for people and cargo transportation, whether with high-speed trains, subways, suburban trains, or electric tramways. In these types of applications, power electronics solutions such as integrated and efficient converters with multiple functionalities are highly desirable. Among these converters, one family stands out for its ability to generating multiple output terminals with reduced number of switches, namely, the nine-switch converter. Therefore, this paper proposes a multiport converter solution based on the nine-switch converter topology that integrates multiple functionalities with a reduced switch count. The converter, responsible for the power drive of the electric tramway, is exclusively powered by a battery. Moreover, it presents a strategically connected passive filter that provides a low-impedance path for high-frequency currents, avoiding leakage of the current circulation in the induction motor. Its phase-shift pulse-width modulation is capable of reducing the high-frequency components of the current delivered by the battery. The energy storage system is designed to optimize the system capacity based on the known real load profile of a public tramway, with a maximum power of 532.1 kW. The control system is designed and applied considering the battery as the energy source. Simulations were performed in the Matlab/Simulink environment to validate the proposed system, along with experiments using a reduced-scale prototype controlled by the dSPACE platform. The results present the converter’s proper operation with integration of the source and AC load, presenting improved features compared with conventional solutions in terms of reduced leakage of the current circulation from the AC load and reduced battery current ripple.

Beyond solar and wind energy, wave energy is gaining great interest due to its very high theoretical potential, although its stochastic nature causes intermittent and fluctuating power production. Energy storage system (ESS) integration to wave energy converter (WEC) plants represents a promising solution to mitigate this issue. To overcome the technological limits of the single storage devices, the hybridization of complementary ESSs represents an effective solution, extending the operating range over different timeframes. This paper analyzes the benefits of Li-ion battery–supercapacitor hybrid ESS integration into a grid-connected WEC, aiming at smoothing the produced power oscillations. The hybridization concept involves coupling a power-intensive technology, such as a supercapacitor devoted to managing fluctuations at higher frequency, with a battery technology exploited to manage power variations over longer timeframes to mitigate degradation issues. In this study, a multi-objective data-driven power management strategy, based on the simultaneous perturbation stochastic approximation (SPSA) algorithm, is implemented to minimize power fluctuations in terms of power ramp (representing the power variation between two consecutive values with a 1 s time step), both at the Point of Common Coupling (PCC) and the Li-ion battery terminals, thanks to the supercapacitor peak-shaving function. The SPSA management strategy, together with a suitable sizing procedure, allows a reduction of more than 70% in the power oscillations at the PCC with respect to those at the WEC terminals, while decreasing battery stress by more than 25% if compared to a non-hybrid ESS consisting of a Li-ion battery. This shows how supercapacitor features can extend battery lifespan when integrated in a hybrid ESS.

Batteries are one of the most crucial energy storage devices today, and battery-energy management technology has an extremely significant impact on the performance and lifespan of batteries. The traditional design approach for battery-energy management platforms often neglects considerations for charging and discharging scenarios. Additionally, functional modules are designed independently, leading to incompatibility issues between hardware and control units, thereby limiting the system’s performance. To address these challenges and enhance system coordination, this paper proposes a systematic design and implementation method for a battery-energy comprehensive management platform applied in charging and swapping scenarios. The method consists of four parts: hardware design, a dynamic load charging-balance control strategy, a composite micro-source hierarchical coordination control strategy, and a system emergency-response and protection strategy. The proposed method has been successfully applied to a design and has been used to build a battery-energy comprehensive management platform. Finally, through experiments, it has been demonstrated that this system can achieve energy scheduling, battery-energy balance, mode switching, and fault protection in a stable and reliable manner.

This paper proposes a methodological way to compensate for the imbalance between energy generation and consumption using a battery block from electric vehicles as an energy reservoir through the well-known vehicle-to-grid system (V2G). This method is based on a simulation process developed by the authors that takes into consideration the daily fluctuations in energy consumption as well as the power level generated by an energy source, either conventional, renewable, or hybrid. This study shows that for very large electric vehicle fleets, the system is rendered non-viable, since the remaining energy in the battery block that allows the electric vehicle to be usable during the daytime avoids having to compensate for the energy grid imbalance, only allowing it to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. This method allows for predicting the optimum size of an electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance, and size of the electric vehicle fleet.

The paper presents a grid-connected microgrid with a photovoltaic system and a battery as a storage element. The optimal design and control of storage elements and power quality improvement are enhanced using sigmoid-function-based variable step size (SFB-VSS) adaptive LMS control. The DC-link voltage and battery current are enhanced using an ILA-optimization-based PI controller. Comparative analysis shows that an ILA-optimized PI controller improves battery stress and DC-link voltage fluctuations, enhancing overall system stability. The relative percentage error of Vdc is only 0.5714% for ILA-optimized values as compared to GA, PSO, and manually tuned PI gains which are 0.857%, 1.14285%, and 0.86%, respectively. ILA-optimized parameters also enhance battery current, reducing stress on the battery. The system was studied under various dynamic conditions, achieving power balance in all conditions. The system has the capability of seamless transfer of control from GC mode to SA mode when the grid is disconnected. The proposed VSC control shows better performance in steady-state and dynamic conditions, maintaining a THD under 5%, which follows IEEE standard 519, and providing better DC offset rejection, fewer oscillations in the weight component of the load, and better convergence. The proposed control also enhances the frequency of the grid, ensuring a smooth transition between modes. The system is simulated in the MATLAB Simulink environment, and all the optimization techniques were carried out offline.

A Hybrid Electric Ship (HES) is investigated in this work to improve its dynamic response to sudden power demand changes. The HES system is based on a Variable-Speed Diesel Generator (VSDG) used for long-term energy supply, with Two Energy Storage Systems (TESSs) using Batteries and supercapacitors for transient power supply. The TESS mitigates the power demand fluctuations and reduces its impact on VSDG, which is linked to a DC-bus through a controlled rectifier. Batteries and Supercapacitors (SCs) are connected in a DC-bus using the bidirectional DC/DC converters to manage the transient and fluctuating components. Two thrusters (one in the front and the second in the back of the Ship) are considered for the propulsion system. The HES power demand includes the requirement of the thrusters and embedded power consumers (elevator, package lifting, air conditioning, onboard electronics devices, etc.). The highlight of this paper is based on the HES fast response improvement in sudden power demand situations via TESS-based batteries and supercapacitors. The other highlight concerns the SCs’ electrothermal modeling using an extension of the SCs’ current ripples’ frequency range (0 to 1 kHz), considering parameter evolution according to using the temperature and current waveform. This energy management-based dynamic power component separation method is tested via simulations using a variable operating temperature scenario.

Due to the reduction in pollutant emissions, the number of electric vehicles has experienced rapid growth in worldwide traffic. Vehicles equipped with batteries represent a greater danger of explosion and fire in the case of traffic accidents, which is why new protective systems and devices have been designed to improve impact safety. Through their design and construction, auxetic structures can ensure the efficient dissipation of impact energy, reducing the risk of battery damage and maintaining the safety of vehicle occupants. In this paper, we analyze the crashworthiness performance of a battery case equipped with an energy absorber with a particular shape based on a re-entrant auxetic model. Simulations were performed at a velocity of 10 m/s and applied to the battery case with a rigid impact pole, a configuration justified by most accidents occurring at a low velocity. The results highlight that by using auxetic structures in the construction of the battery case, the impact can be mitigated by the improved energy absorber placed around the battery case, which leads to a decrease in the number of damaged cells by up to 35.2%. In addition, the mass of the improved energy absorbers is lower than that of the base structure.

The modern era of green transportation based on Industry 4.0 is leading the automotive industry to focus on the electrification of all vehicles. This trend is affected by the massive advantages offered by electric vehicles (EV), such as pollution-free, economical and low-maintenance cost operation. The heart of this system is the electric motor powered by lithium-ion batteries; however, due to their many limitations, a hybrid energy storage system (HESS) consisting of batteries and ultracapacitors is currently gaining increased attention. This paper aims to review the distinct motor technologies such as brushless motors, synchronous reluctance and induction motors currently used in EVs. Additionally, through eleven selected criteria, such as regenerative braking efficiency and power density at different load ranges, the motors are classified in terms of their combined ability to operate with a HESS in order to maximize efficiency and sizing. The results show that permanent magnet and induction motors are the best options when all criteria are considered, while synchronous reluctance motor outperforms the induction motor regarding only the main factors affecting the performance of the hybrid storage system.

This paper proposes an energy management system (EMS) that aims at managing the modular direct current (DC) microgrids (MGs) of a hybrid DC/AC power system onboard cruise ships. Each shipboard microgrid is an electrically self-sufficient system supplied only by a fuel cell (FC) and a Lithium battery, and it powers the ship’s hotel services. However, continuously varying power demand profiles negatively affect the FC. Thus, the proposed EMS aims to minimize the FC operating point excursion on the source’s characteristic. This goal is pursued by exploiting the battery capability to manage load fluctuations and compensate for power demand forecasting errors. Furthermore, it is accomplished while satisfying all the operational constraints of the shipboard microgrids and ensuring daily battery charging/discharging cycles. The proposed EMS is based on two subsystems: (1) a rule-based microgrid supervisor, which makes the EMS capable of managing black start, normal operating conditions, and transient or faulty conditions; (2) an energy management (EM) algorithm, which allows achieving the desired goal without oversizing the battery, thus granting the cost-effectiveness of the solution and a reduced impact on technical volumes/weights on board. The EMS was tested with specific reference to a real-world case study, i.e., a 48,000 gross tonnage cruise ship under different operating scenarios, including black start and multi-day period operation of shipboard MGs. Test results showed that the operating points of the FC were always in the neighborhood of the point chosen by the MG designer, that the voltage variations were always well below 5%, guaranteeing stable operation, and that the black start operation was suitably handled by the EMS. According to the obtained results, the effectiveness of the proposed approach was assessed.

The enormous number of automobiles across the world has caused a significant increase in emissions of greenhouse gases, which pose a grave and mounting threat to modern life by escalating global warming and polluting air quality. These adverse effects of climate change have motivated the automotive sector to reform and have pushed the drive towards the transformation to fully electric. Charging time has been identified as one of the key barriers in large-scale applications of Electric Vehicles (EVs). In addition, various challenges are associated with the formulation of a safe charging scheme, which is concerned with appropriate charging converter architecture, with the aim of ensuring a safe charging protocol within a range of 5–10 min. This paper provides a systematic review of thharging technologies and their impacts on battery systems, including charger converter design and associated limitations. Furthermore, the knowledge gap and research directions are provided with regard to the challenges associated with the charger converter architecture design at the systems level.

This paper presents the implementation of a single-controllable microgrid in the engineering school of the Federal University of Minas Gerais using commercial devices. Such a microgrid exchanges controllable active and reactive power terms with the upstream grid, proportionally shares active/reactive power among the battery-based DERs and endows the microgrid with the capability of operating in both grid-connected and islanded modes. The energy storage system is composed of three different battery technologies: lead-acid, lithium-ion and sodium–nickel, which are coordinately controlled according to their inherent features. A usable average energy control is proposed to avoid mismatches between the batteries’ states of charge. The single-controllable microgrid performs the following services: self-consumption, energy time shift, peak-shaving and reactive power support to the upstream grid. The coordinated secondary control and the operating modes of the microgrid were validated by means of full-scale experimental results using commercial devices. The lithium-ion battery showed the best performance in terms of round-trip efficiency, 93% over 85% (lead-acid) and 81% (sodium–nickel). The results demonstrated the microgrid’s capability of delivering ancillary services at the connection with the upstream grid, and proportionally exploiting the dispersed battery banks. In addition, the challenges of practical implementation were analyzed.

The development of fully electric and hybrid electric aircraft using fuel cells and batteries has lately received much attention since the technology can contribute to achieving an emission-free world. For hybridisation of fuel cells and batteries, the challenge of integrating different voltage sources to supply power to different loads at different voltage levels has to be met. Presently, this is achieved by using a combination of complex passive circuitry and DC–DC converters. In this paper, a new galvanically isolated DC–DC converter topology is proposed; it integrates a battery and fuel cells as input sources and supplies power to a high voltage (HV) and a low voltage (LV) load simultaneously. The converter enables power flow from the sources to the load, as well as between the sources and power flow back towards the battery during recuperation for recharging the battery during flight. The functionality of the converter design is verified using a real-time (RT) hardware-in-the-loop (HiL) test. The proposed concept is easily scalable and can be adopted in any fuel cell and battery drive train of all-electric flights of different sizes.

Solar PV and battery energy storage (BES) costs for domestic consumers are constantly diminishing. On top of this, the end of the Feed-in-Tariff programme has significantly increased interest in combined PV + BES systems. In this paper, we explore the economics of domestic PV + BES systems, extending the current literature on the topic via the use of a large smart meter dataset and a demographic comparison. Predictably, time-varying tariffs and higher electricity prices generally strengthen the economic arguments for PV + BES systems, however our consumption data yields a wide range of Net Present Values for different consumers. In terms of demographics, we find that batteries are more favourable for more affluent households due to their larger consumption levels, though profitability becomes more uniform if the batteries are tailored to individual households. This is an important point for policy, since it indicates that if PV + BES systems become widely profitable this is unlikely to help the financial situation of lower-income households.

A battery–supercapacitor hybrid energy storage system is investigated as a solution to reduce the high-power delivery stress on the battery. An optimally-sized system can further enhance the storage and cost efficiency. This paper discusses several possible problems in the sizing of a battery–supercapacitor hybrid energy storage system for practical applications. A sizing method that utilises data collected from a fully active embedded control hybrid energy system is proposed. The feasibility of the method is then tested on three load profiles that represent the load demand of inter- and intra-applications with a battery–supercapacitor hybrid energy storage system. The result is compared to a battery-only single energy storage system. The results verified that the number of batteries required in the hybrid energy storage system is reduced by at least 50% compared to the battery-only single energy storage system.

Lithium batteries for energy storage systems are a prominent solution for both stationary and mobile applications. Electro-thermal modelling of the cell is a useful tool for monitoring voltage and temperature in order to predict battery behaviour especially in cases of critical operative conditions. This paper provides a modelling approach focusing on the calibration of parameters of an electro-thermal model for large prismatic LFP lithium cells. The designed model is tuned by means of experimental tests that identify a set of parameters that are function of a cell’s state-of-charge and temperature. The model outputs are voltage, cell surface, and internal temperature profiles, which are validated against experimental data referring to realistic working conditions, even providing an intense level of thermal stress. The model accuracy is marked by a voltage mean average error lower than 1% and a mean cell surface temperature deviation lower than 1 K.

In this paper, a hybrid reformed methanol fuel cell–battery system for telecom backup applications was modeled in MATLAB Simulink. The modeling was performed to optimize the operating strategy of the hybrid system using an energy management system with a focus on a longer lifetime and higher fuel efficiency for the fuel cell, while also keeping the state-of-charge (SOC) of the battery within a reasonable range. A 5 kW reformed methanol fuel cell stack and a 6.5 kWh Li-ion battery were considered for the hybrid model. Moreover, to account for the effects of degradation, the model evaluated the performance of the fuel cell both in the beginning of life (BOL) and after 1000 h and 250 start–stop cycling tests (EOT). The energy management system (EMS) was characterized by 12 operating conditions that used the battery SOC, load requirements and the presence or absence of grid power as the inputs to optimize the operating strategy for the system. Additionally, the integration of a 400 W photovoltaic (PV) system was investigated and was able to supplement the battery SOC, thereby increasing the stability and reliability of the system. However, extensive power outages during the night could lead to low battery SOC and, therefore, critical operating conditions and the extended use of the fuel cell. The model also predicted the methanol consumption for different scenarios.

Automated guided vehicles (AGV) or mobile robots (MR) are being used more and more in modern factories, logistics, etc. To extend the work-time of the robot, kinetic energy recovery systems are implemented to store the braking or lifting energy. In most applications, the energy storage system is a Li-ion battery, which is therefore subjected to increased stress and is also oversized. Super-Capacitors can be used in combination to solve this issue. In this paper, different power distribution systems are analysed and compared, using both single or hybrid storage systems (battery and super-capacitor combined). The comparison is both qualitative, using general system characteristics, and quantitative, using an efficiency/power density Pareto front analysis.

Batteries have been the predominant energy storage system used in electric vehicles. Battery packs have a large number of cells that develop charge, thermal, and capacity imbalances over time, limiting the power, range, and lifetime. Electronic battery management and state of charge (SoC) equalization methods are necessary to mitigate such imbalances. Today, it is possible to find a wide range of battery equalization methods in the literature, but how to decide which of these methods should be applied in practice? This paper compares 24 SoC equalization circuits that are typically found in automotive applications. We employ an analytic hierarchy process (AHP) approach to rank these equalization circuits according to multiple decision criteria (energy efficiency, equalization speed, implementation and control simplicity, hardware size, and total price). We also prepared a survey to collect design preferences from multiple battery balancing experts from around the world in order to better understand the relative importance of different criteria. The obtained results confirm that automotive engineers continue to favor passive balancing methods because of their low price, small PCB size, and implementation simplicity—despite the energy efficiency benefits of active balancing.

This paper deals with a photovoltaic (PV) system containing a quasi-Z-source inverter (qZSI) and batteries connected in parallel with the qZSI’s lower-voltage capacitor. The control system design is based on knowledge of three transfer functions which are obtained from the novel small-signal model of the considered system. The transfer function from the d-axis grid current to the battery current has been identified for the first time in this study for the considered system configuration and has been utilized for the design of the battery current control loop for the grid-tied operation. The transfer function from the duty cycle to the PV source voltage has been utilized for the design of the PV source voltage control loop. The PV source voltage is controlled so as to ensure the desired power production of the PV source. For the maximum power point tracking, a perturb-and-observe algorithm is utilized that does not require the measurement of the PV source current, but it instead utilizes the battery current during the stand-alone operation and the d-axis reference current during the grid-tied operation. The corresponding tracking period was determined by using the transfer function from the duty cycle to the battery current and in accordance with the longest settling time noted in the corresponding step response. The proposed control algorithm also has integrated protection against battery overcharging during the stand-alone operation. The considered system has been experimentally tested over wide ranges of irradiance and PV panel temperature.