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This paper deals with the real-time simulation of a power tool battery pack on a mechatronic powertrain test bench. The ability of an easy-to-use model for quick and iterative test runs mainly depends on the effort of parameterization. For this purpose, an easily parameterizable battery model is required. The battery model used is based on the current state of research and simulates the battery’s behavior with an adequate precision. The suggested parameterization allows building the model without the necessity of experimental investigation. Three different procedures for model parameterization were used and compared with the real battery behavior. In conclusion, this paper shows a good tradeoff between precision and an easy way to handle a battery model for testing mechatronic powertrains.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A modular battery-based energy storage system is composed by several battery packs distributed among different modules or parts of a power conversion system (PCS). The design of such PCS can be diverse attending to different criteria such as reliability, efficiency, fault tolerance, compactness and flexibility. The present paper proposes a quantitative and qualitative comparison among the most widely proposed PCSs for modular battery-based energy storage systems in literature. The obtained results confirm the high performance of those PCSs based on the parallel connection of different modules to a single point of common coupling, also identifying those based on modular multilevel cascaded converters as promising concepts according to the assumptions of the present paper.

One of the important tasks for increasing the efficiency of photovoltaic (PV) system is the development and improvement of the maximum power point tracking algorithms (MPPT). These MPPT algorithms lead to the ability to catch efficiently the global maximum power point of the partially shaded PV array. One of these trackers is the particle swarm optimization (PSO) algorithm which is one of the Soft computing techniques. The conventional PSO based trackers have many advantages such as the simplicity of hardware implementation and independence from the installed system. The actual problem of the practical application of PSO is the determination of its parameters to ensure high effectiveness of extracting the global MPP. Analysis of scientific papers devoted to the PSO algorithm has shown that there is currently no methodology for the optimal parameters' selection of PSO algorithm based maximum power trackers for the PV system. This paper aims to create a convenient and reasonable method for choosing the optimal parameters of the PSO algorithm, taking into account the topology and parameters of the DC-DC converter and the configuration of solar panels. A new method for selecting the parameters of a buck converter connected to a battery has been presented. The optimal value of the sampling time for the digital MPP controllers, providing their maximum performance; has been determined based on a new methodology. Matlab/Simulink software package is used as the main research tool. The prominent outcomes identify that the modified PSO and its designed parameters best meet the requirements of the MPPT controller for the PV system.

Energy storage systems are expected to play a fundamental part in the integration of increasing renewable energy sources into the electric system. They are already used in power plants for different purposes, such as absorbing the effect of intermittent energy sources or providing ancillary services. For this reason, it is imperative to research managing and sizing methods that make power plants with storage viable and profitable projects. In this paper, a managing method is presented, where particle swarm optimisation is used to reach maximum profits. This method is compared to expert systems, proving that the former achieves better results, while respecting similar rules. The paper further presents a sizing method which uses the previous one to make the power plant as profitable as possible. Finally, both methods are tested through simulations to show their potential.

As decentralized electricity generation is supporting grid development into the prosumer era, this paper investigates the economic viability of adding batteries to residential microgrids powered by photovoltaic units, under various electricity pricing schemes. Batteries bring the benefits of grid-stabilization and congestion relief, and they are also becoming cheaper. The problem identified is that the main grid effectively acts as a lossless storage system, especially under the net-metering scheme, whereas using a battery involves investment costs and energy losses. This mismatch is addressed by analysing residential microgrid projects under seven tariff designs, each in seven countries of the European Union, and compare the economic viability of photovoltaic systems with and without batteries. The findings show that the conditions most favourable to batteries are given by a capacity tariff scheme allowing price arbitrage. Based on these findings, the paper discusses possibilities for further support in order to bring the economic viability of microgrids with batteries on par with that of microgrids without batteries. This research has been performed in part using the ERIGrid Research Infrastructure and is part of a project that has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 654113. The support of the European Research Infrastructure ERIGrid and its partners Centre for Renewable Energy and Saving, Ricerca sul Sistema Energetico, Danish Technical University and Technical Research Centre of Finland is very much appreciated.

The need to reduce the use of fossil fuels and greenhouse gas (GHG) emissions produced by the transport sector has generated a clear increasing trend in transportation electrification and the future of energy and mobility. This paper reviews the current research trends and future work for power electronics-based solutions that support the integration of photovoltaic (PV) energy sources and smart grid with charging systems for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV). A compressive overview of isolated and non-isolated DC–DC converters and AC–DC converter topologies used to interface the PV-grid charging facilities is presented. Furthermore, this paper reviews the modes of operation of the system currently used. Finally, this paper explores the future roadmap of research for power electronics solutions related to photovoltaic (PV) systems, smart grid, and transportation electrification.

It is predicted there will be a rapid increase in the number of lithium ion batteries reaching end of life. However, recently only 5% of lithium ion batteries (LIBs) were recycled in the European Union. This paper explores why and how this can be improved by controlled dismantling, characterization and recycling. Currently, the favored disposal route for batteries is shredding of complete systems and then separation of individual fractions. This can be effective for the partial recovery of some materials, producing impure, mixed or contaminated waste streams. For an effective circular economy it would be beneficial to produce greater purity waste streams and be able to re-use (as well as recycle) some components, thus, a dismantling system could have advantages over shredding. This paper presents an alternative complete system disassembly process route for lithium ion batteries and examines the various processes required to enable material or component recovery. A schematic is presented of the entire process for all material components along with a materials recovery assay. Health and safety considerations and options for each stage of the process are also reported. This is with an aim of encouraging future battery dismantling operations.

Inertia plays a vital role in maintaining the frequency stability of power systems. However, the increase of power electronics-based renewable generation can dramatically reduce the inertia levels of modern power systems. This issue has already challenged the control and stability of small-scale power systems. It will soon be faced by larger power systems as the trend of large-scale renewable integration continues. In view of the urgent demand for addressing the inertia concern, this paper presents a comprehensive review of inertia enhancement methods covering both proven techniques and emerging ones and also studies the effect of inertia on frequency control. Among those proven techniques, the inertia emulation by wind turbines has successfully demonstrated its effectiveness and will receive widespread adoptions. For the emerging techniques, the virtual inertia generated by the dc-link capacitors of power converters has a great potential due to its low cost. The same concept of inertia emulation can also be applied to ultracapacitors. In addition, batteries will serve as an alternative inertia supplier, and the relevant technical challenges as well as the solutions are discussed in this paper. In future power systems where most of the generators and loads are connected via power electronics, virtual synchronous machines will gradually take over the responsibility of inertia support. In general, it is concluded that advances in semiconductors and control promise to make power electronics an enabling technology for inertia control in future power systems.

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

Ships entering designated emission control areas are required to reduce emission gases drastically, in accordance with international maritime organization regulations or local policies. In this regard, a hybrid power system, which is a combination of conventional generators and lithium-ion batteries as a type of energy storage system (ESS), has been applied to ships. However, this hybrid concept has been focused on small-sized coastal ships, because they have a short voyage time or require high peak power for a short time. And by extension, this paper demonstrates the potential of a hybrid power system for mediumsized oceangoing ships. First, a medium-sized container ship is selected as a target ship. The electric load profile near ports is defined to select the optimum capacity of the ESS and generators. Then, to verify the advantage of the proposed system, this paper uses MATLAB/Simulink software for simulation. Finally, carbon dioxide (CO2) emissions are compared between the proposed power system and the conventional one which uses only generators. The results show that overall, 8.6% ~ 20.7% CO2 emissions can be reduced, depending on the electric load conditions of a ship. Hence, a hybrid power system can be an eco-friendly solution for medium-sized oceangoing ships, as well as small-sized coastal ships.

This article belongs to the Section Electric Vehicles. Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required. The authors thank the support from the Gipuzkoa Provincial Council (project Etorkizuna Eraikiz 2019 DGE19/03), the Basque Government (GISEL research group IT1083-16), as well as from the University of the Basque Country UPV/EHU (PES16/31 and PES17/08).

Advances in battery system design, integration and management help to meet the highly demanding safety requirements for Li-ion batteries in the electric mobility field. The aim of this paper is to describe the implementation of a new smart battery, based on the concept of smart cells combined with an FPGA-based main unit and an embedded DC/DC converter. An extended test campaign has been carried out to verify the correct and effective operation of all the battery components. The paper shows that innovative battery functions can be achieved by the integration of up-to-date electronics technology.

Still today the dynamic identification of mechatronic systems - in particular electrical machines - is often based on stimulating them with deterministic signals, being single sinusoidal tones, single steps or periodical pulse trains of fixed frequency the most common ones. Such methodologies were the most, when not the only, practicable ones when the instrumentation was limited to low frequency generators and analog oscilloscopes, at most. The modern metrological scenario allows to concretize way more sophisticated approaches nowadays, which can bring to Mechatronics more holistic and easily executable identification methods; like those based on flat noise sources traditionally used in Radio Frequency (RF). More in general, the adoption of SiC and GaN fully controllable switches demands that also Power Electronics become eventually permeated with the very different way of thinking peculiar of RF. Within the framework of such cultural and methodological evolution that Power Electronics must embrace, the paper describes the unconventional use of converters based on GaN switches as powerful high-flatness noise sources for broadband dynamic identification, here tailored for modern electrical machines and battery technologies. Indeed, owing to the much shorter raise and fall times, as well as much higher switching frequencies enabled by GaN, it is possible to greatly extend the upper boundaries of the frequency bands where desired spectra can be synthesized by commutating such fully controllable switches according to proper time-based patterns. The authors illustrate the use of a randomly PWM-modulated H-bridge employing GaN E-HEMTs as high-flatness noise source up to at least 200 kHz thanks to 1 MHz PWM carrier. Various selected experimental results from identifications of BLDC Motors and Lithium-Iron-Phosphate $(LiFePo_{4})$ cells are shown. The details in the figures are fully visible by magnifying the electronic version of the paper, which represents the same approach when Microfilms were widely used.

This paper is an Editorial for the special issue (SI) of Renewable and Sustainable Energy Reviews (RSER) devoted to the 12th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), held from the 4th to 8th October 2017 in Dubrovnik, Croatia. The key focus of RSER is to publish critical thinking across the wide field of renewable and sustainable energy. The journal publishes either pure review papers or research papers with a precise review element. Overall, the guest editors invited 36 papers presented on 12th SDEWES conference, of which 17 were accepted after rigorous peer review process. The SDEWES conferences present a continuous effort in gathering high level scientists and research papers across the field of sustainability and by now the amount of accumulated knowledge, through conference papers and special journal issues, is significant. Each SDEWES journal editorial presents a wider overview of special issue journal papers in order to maintain the continuation of knowledge generation on the main sustainability topics. The main topics covered in this RSER editorial include renewable energies with special focus on integration, markets and roadmaps, energy efficiency in buildings, industrial process and transport, bio based economy focused on bio based chemicals and biofuels, energy storage focused on batteries, thermal and geological storages and various sustainability assessments in several sectors and technologies. All were considered in light of the Circular Economy.

This paper focuses on evaluating the energy and power requirements of a specific aircraft on-board electric taxiing (ET) system. The developed model of the investigated system is used to determine the requisites for a typical taxiing profile mission of a Boeing 737–400. Besides the derivation of the specifications, the comparison of batteries and electrochemical capacitors is outlined in the light of viable candidates for a local energy storage system (LESS). It is estimated that LESS should be sized for capacity of 19kWh and peak power of 81kW. The paper is concluded with a comparison and discussion on LESS topologies.

The integration of energy storage systems (ESS) in PV plants is a very topical subject. In recent years, many research activities have been addressed to PV plants combined with batteries. Most of the papers in literature refer to plants for residential or industrial users with significant self-consumptions. Only a few papers investigate on ESS for utility-scale PV plants and on the optimal energy management strategies, some examples are in [1], [2]. As a contribution to fill this gap, this paper presents a study on a specific power layout in which the ESS is distributed into the PV field and no longer centralized close to the inverter cabin, as for the majority of the existent PV plants with ESS. Focus is on the design and implementation of suitable models for both the PV field and the ESS with the purpose to establish optimal energy management strategies. The validation process of such models has been carried out through laboratory tests as well as exploiting data coming from the monitoring system of a real PV plant in Central Italy.

International audience; In the current context of high integration of renewable energies, maximizing infrastructures capabilities for electricity transmission is a general need for Transmission System Operators (TSO). The French TSO, RTE, is developing levers to control power flows in real-time: renewable production curtailment is already employed and large battery storage systems are planned to be installed for congestion management in early 2020. The combination of these levers with the use of Dynamic Line Rating (DLR) helps exploiting the lines at the closest of their limit by managing their temperature in real-time. Unnecessary margins can be reduced, avoiding congestion and excessive generation curtailment. In particular, there is a possible interesting correlation between the transits increase due to high wind farms generation and the cooling effect of wind on power lines in the same area. In order to optimize the electrical transmission network capacities, the present paper advocates the use of a temperature management model, mixing production curtailment and large batteries as control variables. A robust Model Predictive Control framework for local control on electrical lines temperature is presented based on the regulation within tubes of trajectories. Simulations on the French electrical network are conducted to show the effectiveness of the optimization-based control design.

This paper discusses the themes of optimal design and management strategies of hybrid energy storage system (HESS) for marine applications. This design and related strategy are aimed to improve battery pack durability, ensuring a smooth profile of the required current, through the complementary action of super-capacitors. A DC/DC bidirectional power converter allows for the integration of these devices and control of on-board power fluxes. The proposed methodology is based upon the solution of a constrained optimization problem. In order to overcome computational issues, proper formulation of the mathematical problem is based upon the Ritz method, which is one of the direct methods for solving problems related to calculus of variations. The solution of the optimization procedure, which is the key finding in this paper, permits to obtain battery voltage and current profile, which enables the designer to determine battery and super-capacitor sizes and also provides battery/super-capacitor reference current profiles to be tracked by the DC/DC bidirectional power converter. The procedure is evaluated with reference to the case study of a water-bus operating in the Venetian lagoon, whose operative cycle has been acquired during proper measurement campaigns. Then, the performance of energy management strategies are verified by means of laboratory experimentations on the hybrid energy storage system, which have been carried out by scaling operative cycles at single storage cell level. The obtained results show the potential advantages of proper design and energy management obtained through this new methodology, in terms of investment and maintenance costs for sustainable maritime transport.

Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time-varying and non-linear properties, which are extremely complex. Therefore, accurately estimating the state of charge is a challenging task. This paper reviews various representative patents and papers related to the state of charge estimation methods for an electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional methods based on the battery experiments, the modern methods based on control theory, and other methods based on the innovative ideas, especially focusing on the algorithms based on control theory. The results imply that the algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish a rich database, improve hardware technology, come up with a much better battery model, and give full play to the advantages of each algorithm.

Battery energy storage systems are widely recognized as viable means for providing frequency regulation in electrical systems characterized by the massive deployment of renewable resources. In this case, the estimation of the battery lifetime is very complex since its response depends strongly on the stochastic nature of various contingencies. By starting from the characterization of the power system frequency in terms of the stochastic process, in this paper, tools needed for the battery lifetime estimation are detailed. On the basis of a realistic dynamic model, the state of charge profile requested to a battery providing primary frequency regulation is derived, with the aim of characterizing statistically its degradation. In this way, it is expected to obtain an efficient battery lifetime estimation compared to the heuristic methods. Numerical simulations are carried out in the last part of the paper, illustrating a simulation tool for the estimation of the battery working cycle. © 2018 IEEE.

ICIT2018, 19th IEEE International Conference on Industrial Technology, Lyon, FRANCE, 20-/02/2018 - 22/02/2018; In order to study the aging of batteries in automotive applications, it is important to understand how and under what conditions these batteries are operating in electric vehicles (EVs). However, because of the specificities of EVs, these uses may be very different from those known from internal combustion engine cars (ICE). In this paper, we present how, from real-life data, we determine the different driving modes of electric vehicles. The paper presents the different techniques adopted to analyze and classify the data and the different EV running modes, which are obtained.

International audience; This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB.

A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle., The authors wish to acknowledge financial support from the Generalitat de Catalunya (GRC MCIA, Grant n SGR 2014-101). This work was supported by the catalan Government: la Generalitat de Catalunya.

International audience; This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model (SPM) with electrolyte that achieves higher predictive accuracy than the SPM. Next, we propose an estimation scheme and prove estimation error system stability, assuming that the total amount of lithium in the cell is known. The state estimation scheme exploits the dynamical properties, such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm’s performance and limitations.

A novel optimal power and energy management (OPEM) for centralized hybrid energy storage systems (HESS) in microgrids is presented in this paper. The proposed OPEM aims at providing multiple grid services by suitably exploiting the different power/energy features of electrochemical batteries (B) and supercapacitors (S). The first part of the paper focuses on the design and analysis of the proposed OPEM, by highlighting the advantages of employing hand-designed solutions based on Pontryagin’s minimum principle rather than resorting to pre-defined optimization tools. Particularly, the B power profile is synthesized optimally over a given time horizon in order to provide both peak shaving and reduced grid energy buffering, while S is employed in order to compensate for short-term forecasting errors and to prevent B from handling sudden and high-frequency power fluctuations. Both the B and S power profiles are computed in real-time in order to benefit from more accurate forecasting, as well as to support each other. Then, the effectiveness of the proposed OPEM is tested through numerical simulations, which have been carried out based on real data from the German island of Borkum. Particularly, an extensive and detailed performance analysis is performed by comparing OPEM with a frequency-based management strategy (FBM) in order to highlight the superior performance achievable by the proposed OPEM in terms of both power and energy management and HESS exploitation.

This paper presents a method to optimally use an energy storage system (such as a battery) on a microgrid with load and photovoltaic generation. The purpose of the method is to employ the photovoltaic generation and energy storage systems to reduce the main grid bill, which includes an energy cost and a power peak cost. The method predicts the loads and generation power of each day, and then searches for an optimal storage behavior plan for the energy storage system according to these predictions. However, this plan is not followed in an open-loop control structure as in previous publications, but provided to a real-time decision algorithm, which also considers real power measures. This algorithm considers a series of device priorities in addition to the storage plan, which makes it robust enough to comply with unpredicted situations. The whole proposed method is implemented on a real-hardware test bench, with its different steps being distributed between a personal computer and a programmable logic controller according to their time scale. When compared to a different state-of-the-art method, the proposed method is concluded to better adjust the energy storage system usage to the photovoltaic generation and general consumption. - Paper originally published in ENERGIES. Abstract obtained fromthe original publication.

International audience; In this paper, the authors investigate four original methodologies for sizing a battery bank inside a passive wind turbine system. This device interacts with wind and load cycles, especially for a stand-alone application. Generally, actual wind speed measurements are of long duration which leads to extensive processing time in a global optimization context requiring a wide number of system simulations. The first part of this article outlines two sizing methodologies based on a statistical approach for the sizing of the electrochemical storage device of a stand-alone passive wind turbine system. Two other efficient methodologies based on the synthesis of compact wind speed profiles by means of evolutionary algorithms are described in the second part of this paper. The results are finally discussed with regard to the relevance of the battery bank sizing and in terms of computation cost, this later issue being crucial to an Integrated Optimal Design (IOD) process.

This paper presents an analysis of the main issues related to the design of battery pack supplying full electric vessels, such as water busses. After an assessment of the energy storage systems technologies available to power marine applications, battery design aspects are investigated. Hence, a probabilistic battery design method is discussed, taking properly into account the battery lifetime and the inherent economical evaluation. The method is based upon accelerated life tests and allows to face with the randomness of the power load in a suitable way. The optimal size of the battery is determined by tailoring an optimization procedure which ensures the desired robustness against the stochastic load parameters. In the last part of the paper, this probabilistic methodology is applied to a case study represented by a water bus for the public transportation service in the canals of the Venetian lagoon. The numerical results allow to confirm the feasibility and the goodness of the probabilistic approach. © 2016 AEIT.

5 pages; International audience; This paper presents a technical and economical study of a hybrid—fuel cell, photovoltaic,and battery—electric generation system. The system is installed on five different remote sites. These sites are family houses located in different regions in France beyond the reach of electrical power lines. The optimum system configuration is not often easily found and a lot of calculations and/or simulations are required in order to decide which combination gives the best technical and financial benefit. The aim of this study is to design each component of the remote generation system in order to get the best technical and financial combination. In this paper, a method is suggested to find the optimum combination for a hybrid power system dedicated to off-grid electricity users. Indeed, this project permits to save up to 25% of the initial cost.

International audience; Electric vehicles are currently a held of research with many challenges that raise the interest of many researchers. The major challenge is about the autonomy of electric vehicles, which is limited as compared to that of conventional vehicles, and thus the drivers' anxiety about reaching or not the desired destination is very important. In this paper, we propose an experimental study to understand the energy consumption of electric vehicles, and we investigate some factors that have an important impact on their autonomy, such as the route type, the driving style and the ambient temperature. This study can be very useful in a further step to conceive a driving assistance system that indicates in real time the remaining energy and gives online instructions to reach the next charging station or the final destination. The analysis reported in this paper is based on real-world data collected using a full electric car with different driving conditions.

[EN] Air-conditioning (AC) is an important sub-system in electric vehicles (EVs). AC is responsible for the highest energy consumption among all the auxiliary systems. As the energy is delivered by the batteries, the power consumption for airconditioning can imply a significant reduction of the vehicle autonomy. Given the actual state of the art and the temperature and power requirements, electrically driven compressors are the most feasible solution. However, vapour-compression systems are reaching their maximum efficiency. Using innovative technologies can improve the performance of standard systems and hereby increase the vehicle autonomy. This paper presents the first steps in the design of a magnetocaloric air-conditioner for an electric minibus. The system will include two reversible magnetocaloric heat pumps, one in the front part of a minibus and one on the rear. The heat rejection system of the power electronics will be coupled to the air-conditioning system. In order to assist the design of the system, a dynamic model has been developed for the cabin, the hydraulic loops and heat exchangers, and the magnetocaloric units. An integrated design of the complete system is necessary, as it will work under dynamic conditions which depend on the thermal load in the cabin. In this paper, the operation conditions of the magnetocaloric units are presented and the design of the magnetocaloric air-conditioner is discussed. This work has been developed under the frame of the European Project ICE which aims to develop an innovative mobile air-conditioning system for EVs based on a magnetocaloric heat pump., This work has been supported by the European Commission under the 7th European Community framework program as part of the ICE project “MagnetoCaloric Refrigeration for Efficient Electric Air-Conditioning”, Grant Agreement no. 265434. B. Torregrosa-Jaime acknowledges the Spanish Science and Innovation Ministry (Ministerio de Ciencia e Innovación) for receiving the Research Fellowship FPU ref. AP2010-2160.

International audience; This paper presents an embedded energy share method between the high energy storage system (battery) and the auxiliary energy storage system such as supercapacitors (SC). Using the SC and battery with a good strategy for energy management improves the performance of hybrid electric vehicles (HEVs). The SC modules are dimensioned for peak power requirement, and the battery's module ensures the average power of HEVs. The battery module is connected to dc-bus through a dc/dc converter for the first topology and without a converter for the second configuration. Buck-boost converters are used between the SC and the dc-bus to manage the available energy for all topologies. The originality of this paper stems from its focus on the control methods of the dc-bus voltage and currents, which use adjustable polynomial controllers (correctors based on polynomial approach). These methods are implemented in the PIC18F4431 microcontroller which ensures analog to digital conversion, and the pulsewidth modulation signals generation for dc/dc converters. Due to cost and available components, such as the power semiconductors (IGBT) and the battery, the experimental tests benches are carried out in reduced scale. Through some simulations and experimental results, the performance of the proposed control is shown and analyzed.

Ship hybridization has received some interests recently in order to achieve the emission target by 2050. However, designing and optimizing a hybrid propulsion system is a complicated problem. Sizing components and optimizing energy management control are coupled with each other. This paper applies a nested double-layer optimization architecture to optimize the sizing and energy management of a hybrid offshore support vessel. Three different power sources, namely diesel engines, batteries and fuel cells, are considered which increases the complexity of the optimization problem. The optimal sizing of the components and their corresponding energy management strategies are illustrated. The effects of the operational profiles and the emission reduction targets on the hybridization design are studied for this particular type of vessel. The results prove that a small emission reduction target of about 10% can be achieved by improving the diesel engine efficiency using the batteries only while the achievement of a larger emission reduction target mainly depends on the amount of the hydrogen and/or on-shore charging electricity consumed. Some design guidelines for hybridization are derived for this particular ship which could be also valid for other vessels with similar operational profiles.

This article presents a non-conventional design approach of high order passive filters incorporated with distributed energy grid integration systems based on particle swarm optimization (PSO) as one of multi-objective evolutionary search algorithms. Two topologies of passive grid filters (third order passive damped LCL-filter and trap filter) are chosen as case studies. The presented grid filter design is based searching the optimum values of filter passive elements that can optimize an objective function composed of several terms such as harmonic attenuation factor and size (value) of passive elements (inductors and capacitors). The employed multi-objective design approach has three main advantages: (1) The PSO algorithm offers several groups of solutions to the same optimization problem. Accordingly, the most convenient solution can be chosen based on several factors such as cost of realization, availability in the market and the corresponding THD of grid current. (2) Multi-objective design approach is flexible enough to include other factors in the customized objective function to achieve different design criteria in accordance with new (or updated) versions of grid codes. (3) The PSO algorithm converges to the optimum solution(s) regardless the initial search values (initial guess). Consequently, the algorithm does not need any prior knowledge about filter numerical values The PSO algorithm has been developed in Matlab®, while the overall hardware grid-integration system has been modeled and studied using PSIM® software package. The obtained results demonstrate the effectiveness of the proposed approach to get practical and applicable values of filter components that result in good harmonic attenuation and satisfy the related codes of grid integration such as the IEEE standard 519. The main contribution of this paper is the utilization of evolutionary optimization technique to achieve an optimum design of passive grid filters that can optimize simultaneously several contradictory goals such as achieving the maximum possible harmonic attenuation at the lowest possible filter size. Compared with conventional design approach, the PSO-based filter design approach results in lower numerical values of filter components, which leads to considerable reduction in the size and cost of the passive grid filter. Moreover, grid filter design based on evolutionary search approach permits accommodation of several design criteria in the customized objective function with arbitrary weighting factors upon system design requests and new grid codes constrains.

In the pursuit of batteries with higher energy density and lower cost, central to advancement of the technology is the ability to prolong cycle life. Techniques are sought which can elucidate information on battery degradation without significantly disrupting the performance of cells. Electrochemical impedance spectroscopy (EIS) offers a non-destructive route to in-situ analysis of the dynamic processes occurring inside a battery. The technique is relatively easy to use, but meaningful data analysis requires assignment of spectroscopic features to battery impedance components. Three-electrode cell configurations afford a way to potentially disentangle the impedance components. This paper examines a number of three-electrode cell designs reported in the literature, and compares their advantages and limitations. EIS results obtained using a novel in-house, three-electrode pouch cell are reported and the results compared with those obtained from conventional two-terminal impedance complex plane plots. In this way, the separate contributions of anodic and cathodic impedances can be assessed.

International audience; This paper presents an optimization model for Home Energy Management Systems from an aggregator’s standpoint. The aggregator manages a set of resources such as PV, electrochemical batteries and thermal energy storage by means of electric water heaters. Resources are managed in order to participate in the day-ahead energy and local flexibility markets, also considering grid constraint support at the Point of Common Coupling. The resulting model is a Mixed-Integer Linear Programming problem in which the objective is to minimize day-ahead operation costs for the aggregator while complying with energy commitments in the day-ahead market and local flexibility requests. Three sources of uncertainty are considered: energy prices, PV production and load. Adjustable Robust Optimization is used to find a robust counterpart of the problem for including uncertainty. The results obtained show that using robust optimization allows strategic bidding to capture uncertainties while complying with obligations in the wholesale and local market. Data from a real-life energy community with 25 households is used to validate the proposed robust bidding methodology.

The future use of electric vehicles in the southern regions of the world could face several problematics related to high temperature, mostly when charging at high power. This paper proposes a new external cooling solution for cooling EV batteries pack at higher temperature conditions especially for those without effective cooling system embedded. A 3D-thermal model has been developed in order to investigate and to analyse the temperature distribution over the battery pack, the ANSYS FLUENT software has been used to solve the model development. Furthermore, different external cooling solutions have been proposed by using the engineering process design to study their impact on the internal temperature of the battery pack. Keywords: Thermal model, Batteries, Electric vehicle, Finite volume method, Response surface method, ANSYS FLUENT

This paper presents the development and operation on 13.8kV distribution systems of a peak-shaving equipment with battery energy storage. This equipment injects active power to grid during peak times (when the cost of energy is higher) and charges its battery banks from the grid at the off-peak times (when the energy has a low producing cost). The equipment is based on a multilevel converter coupled to the grid through a 2.4kV:13.8kV transformer. In addition to the peak-shaving functionality, a feature of compensation of harmonics on the distribution voltage is included, without the need for any extra sensor nor hardware (apart from the ones already in use for the peak-shaving). The compensation of voltage harmonics is performed through the emulation of a harmonic resistance in order to damp resonances between system impedances that are excited by non-linear loads. This approach is very appealing to distribution systems, where the non-linear loads are not accessible and are scattered. Experimental results obtained on a 13.8kV test substation are presented.

Existing energy systems face problems such as depleting fossil fuels, rising energy prices and greenhouse gas (GHG) emissions which seriously affect the comfort and affordability of energy for large-sized commercial customers. These problems may be mitigated by the optimal scheduling of distributed generators (DGs) and demand response (DR) policies in the distribution system. The focus of this paper is to propose an energy management system (EMS) strategy for an institutional microgrid (μG) to reduce its operational cost and increase its self-consumption from green DGs. For this purpose, a real-time university campus has been considered that is currently feeding its load from the national grid only. However, under the proposed scenario, it contains building owned solar photovoltaic (PV) panels as non-dispatchable DG and diesel generator as dispatchable DG along with the energy storage system (ESS) to cope up with the intermittency of solar irradiance and high operational cost of grid energy. The resulting linear mathematical problem has been mapped in mixed-integer linear programming (MILP) and simulated in MATLAB. Simulations show that the proposed EMS model reduces the cost of grid electricity by 35% and 29% for summer and winter seasons respectively, while per day reductions in GHG emissions are 750.46 kg and 730.68 kg for the respective seasons. The effect of a half-sized PV installation on energy consumption cost and carbon emissions is also observed. Significant economic and environmental benefits as compared to the existing case are enticing to the campus owners to invest in DG and large-scale energy storage installation.

For safety and longevity reasons, in subzero temperatures, lithium-ion batteries (LIBs) can only be charged after precommissioning their temperature. Therefore, in such conditions, fast charging depends on fast heating. Recently, the injection of ac currents into LIBs has been reported as a technique with the potential to decrease heating time. This paper proposes a method based on a multi-objective algorithm for dc–dc converter design using transformerless resonant filters. The method enables the dc–dc converters to produce magnified ac current in addition to the dc current. Using the proposed design method, a topological survey of dc–dc converters with magnified ac current capability composed of either half-or full-bridge switch arrangements is carried out. In the presented experimental setup, it is demonstrated that by using an $LCL$ circuit with specific component values and a full-bridge switch arrangement, magnifications of up to 15.7 may be reached. Furthermore, by matching the switching frequency with the frequency where the $LCL$ and the battery resonate, for the same injected ac current, the current flowing in the semiconductors and the switching frequency could be reduced. This allowed a loss reduction in the semiconductors of up to 75%, when compared with an equivalent dc–dc converter enabled to produce a nonmagnified ac current.

This paper proposes a multi-objective hybrid energy management system that minimizes both the electricity expenses and the household greenhouse gas emissions released due to consumption, considering the entire life cycle of the generation assets used to provide energy. The global warming potential indicator is used to decide if it is more sustainable to purchase electricity from the grid or use the household’s flexible generation sources like photovoltaic panels and the energy storage system. Results prove that it is possible to reduce greenhouse gas emissions without incurring expensive electricity bill costs thanks to the hybrid-based home energy management system approach. This method gives the end-users a more influential role in the climate change solution, allowing them to give more or less importance to the economic or environmental component, according to their preferences.

17 USC 105 interim-entered record; under review. The article of record as published may be found at http://dx.doi.org/10.3390/en14102773 This paper presents a user-friendly design method for accurately sizing the distributed energy resources of a stand-alone microgrid to meet the critical load demands of a military, commer cial, industrial, or residential facility when utility power is not available. The microgrid combines renewable resources such as photovoltaics (PV) with an energy-storage system to increase energy security for facilities with critical loads. The design method’s novelty complies with IEEE Standards 1562 and 1013, and addresses resilience, which is not taken into account in existing design methods. Several case studies simulated with a physics-based model validate the proposed design method and demonstrate how resilience can be included in the design process. Additionally, the design and the simulations were validated by 24 h laboratory experiments conducted on a microgrid assembled using commercial off-the-shelf components. NAVFAC as part of the Naval Shore Energy Technology Transition Program (NSETTI)

This paper presents an electrical infrastructure planning method for transit systems that operate with partially grid-connected vehicles incorporating on-board batteries. First, the state-of-the-art of electric transit systems that combine grid-connected and battery-based operation is briefly described. Second, the benefits of combining a grid connection and battery supply in Bus Rapid Transit (BRT) systems are introduced. Finally, the planning method is explained and tested in a BRT route in Medellin, Colombia, using computational simulations in combination with real operational data from electric buses that are currently operating in this transit line. Unlike other methods and approaches for Battery Electric Bus (BEB) infrastructure planning, the proposed technique is system-focused, rather than solely limited to the vehicles. The objective of the technique, from the vehicle’s side, is to assist the planner in the correct sizing of batteries and power train capacity, whereas from the system side the goal is to locate and size the route sections to be electrified. These decision variables are calculated with the objective of minimizing the installed battery and achieve minimum Medium Voltage (MV) network requirements, while meeting all technical and reliability conditions. The method proved to be useful to find a minimum feasible cost solution for partially electrifying a BRT line with In-motion Charging (IMC) technology.

Made available in DSpace on 2018-12-11T16:55:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-08-30 Penetration rate of grid-connected photovoltaic (PV) generation to the existing utility grid is rapidly increasing over the years. Since the power generated from PV systems fluctuate according to the weather condition, e.g., cloud passing, this can significantly disturb the stability of a weak utility grid. The integration of energy storage devices and its ramp-rate control technique are required to reduce the impact of PV systems output fluctuations and augment the stability of the utility grid. In this paper, ramp-rate control is applied to the direct connection of energy storage devices in PV generation system configuration. The direct connection of supercapacitors string and battery combination scheme is proposed to reduce the number of power converters so that the efficiency of the system is increased. In this work, the PV system output is controlled by directly control the energy storage system (ESS) to limit the changing rate of PV output to desired ramp-rate value. Hence, reducing the battery charge/discharge cycles and extending the expected lifetime of the ESS. The performance of the proposed direct connection scheme of ESS and its ramp-rate control strategy is verified using a 1-kW PV system prototype. Electrical Engineering, Khalifa University of Science Technology and Research, 105955 Abu Dhabi, Abu Dhabi United Arab Emirates (e-mail: yoga.sandi@yahoo.com) Department of Electrical and Computer Engineering, Curtin University, 1649 Perth, Western Australia Australia 6845 (e-mail: s.m.muyeen@ieee.org) Electrical Engineering, Khalifa University of Science and Technology, Abu Dhabi United Arab Emirates (e-mail: ahmed.aldurra@ku.ac.ae) Electrical Engineering and Computer Science, Colorado School of Mines, Golden, Colorado United States 80401-1887 (e-mail: msimoes@mines.edu) Sao Paulo State University, Sao Paulo, Sorocaba Brazil (e-mail: helmo.paredes@unesp.br)

This paper proposes an approximate dynamic programming (ADP) based algorithm for the real-time operation of the microgrid under uncertainties. First, the optimal operation of the microgrid is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem, combining the ac power flow and the detailed operational character of the battery. For this NP-hard problem, the proposed ADP based energy management algorithm decomposes the original multitime periods MINLP problem into single-time period nonlinear programming problems. Thus, the sequential decisions can be made by solving Bellman's equation. Historical data is utilized offline to improve the optimality of the real-time decision, and the dependency on the forecast information is reduced. Comparative numerical simulations with several existing methods demonstrate the effectiveness and efficiency of the proposed algorithm.

High integration of rooftop photovoltaic (PV) plants in distribution systems leads to new technical challenges: reverse-active power and voltage rise in low-voltage (LV) and medium-voltage (MV) grids. These challenges limit the maximum amount of power can be produced by PVs in LV and MV grids, called the hosting capacity (HC). Battery storage systems (BSSs) have been used in many studies to decrease the reverse power and improve the HC by controlling the active power. However, the influence of a central BSS on the HC can be greatly improved by using a quadratic power control, simultaneous active and reactive power control, and by selecting of the optimal battery size, the converter size, and the place of the central BSS. The effectiveness of the quadratic power control was not seen in previous works due to the fact that grids with one level of voltage without modeling of MV/LV transformers were simulated. This paper develops a method to select the optimal size of the battery and converter unit as well as the optimal place of an LV-central BSS having an optimal quadratic power control. The simulation results show considerable effects of the optimal selection of an LV-central BSS on the HC improvement. QC 20180327

In this paper, the financial sense of used batteries providing energy storage (ES) for grid applications is investigated. An investment strategy to determine the optimal site and size of used battery ES is proposed for profit-oriented merchant entities and is formulated as a bilevel model. On the upper level, the decisions on ES investment and bidding and offering in the market are optimized, subject to maximizing the social welfare, which is achieved in the market clearing process on the lower level. Computational tractability is achieved by implementing a solution approach based on Benders decomposition. To speed up the convergence of this algorithm, we propose two acceleration techniques, namely, the valid inequalities and a multi-cut framework. We test the proposed algorithm on the IEEE Reliability Test System. The optimal siting and sizing decisions of the used battery ES are analyzed, followed by the analyses of the impacts of the lifespan, unit cost, and capacity loss of the used batteries on the investment decisions. Finally, we demonstrate that the proposed accelerating Benders decomposition outperforms the classical decomposition methods on computational performance.

The electrolyte filling process constitutes the interface between cell assembly and formation of lithium ion batteries. Electrolyte filling is known as a quality critical and also time consuming process step. To avoid limitations in battery quality a homogeneous electrolyte distribution is necessary. Therefore, especially large sized cells are stored for hours. To accelerate filling and wetting processes the effect of materials- and process parameters on electrolyte distribution needs to be investigated. Unfortunately, in situ methods to characterize the filling and wetting state are still rare, limited in availability or even time-consuming in preparation. To overcome these drawbacks this paper introduces X-ray as an innovative method to visualize the electrolyte filling process in large scaled lithium ion batteries. Therefore, an experimental setup was developed to enable in situ X-ray measurements during the filling process of large scaled cells. Additionally, an evaluation process for the optical data was proposed. Based on these images the suitability of X-ray as visualization method is shown considering three exemplary filling parameters.

This paper presents a passivity-based control (PBC) scheme for the switched reluctance generator (SRG) in small-scale wind energy conversion systems for dc microgrid applications. The main objective is to stabilize the output voltage in case the system supplies constant power loads (CPLs) and operates with maximum power point tracking (MPPT). Stability improvement and dc-link ripple reduction in the presence of CPLs is achieved using system-level modeling of SRG-based dc microgrid through the Euler-Lagrange system (ELS) from the viewpoint of the machine physical structure. Compared with other control methods, the proposed MPPT method based on passivity-based speed controller employs the back electromotive force (EMF) in the generation process as a position-dependent voltage source to overcome the major challenge of SRG complicated uncertain dynamic model. To deal with the time-varying inductance and back EMF of SRG, an adaptation mechanism is incorporated in the proposed adaptive PBC and the control design is constructed by using the Lyapunov theorem where the closed-loop stability is ensured. The effectiveness of the proposed method in avoiding instability effects of SRG and CPL with voltage ripple reduction and precise wind turbine speed tracking is investigated with simulation results and validated with experimental by using a four-phase, 8/6 SRG drive system.

The aim of this study is to assess the environmental impact of storage systems integrated with energy plants powered by renewable sources. Stationary storage systems proved to be a valid solution for regulating networks, supporting frequency, and managing peaks in electricity supply and demand. Recently, their coupling with renewable energy sources has been considered a strategic means of exploiting their high potential since it permits them to overcome their intrinsic uncertainty. Therefore, the storage systems integration with distributed generation can improve the performance of the networks and decrease the costs associated with energy production. However, a question remains regarding the overall environmental sustainability of the final energy production. Focusing on electrochemical accumulators, the problems mainly concern the use of heavy metals and/or impacting chemical components of storage at the center of environmental hazard debates. In this paper, an environmental assessment from a life-cycle perspective of the hybrid energy systems powered by fossil and renewable sources located on two non-interconnected minor islands is presented. Existing configurations are compared with new ones obtained with the addition of batteries for the exploitation of renewable energy. The results show that, for batteries, the assembly phase, including raw material extraction, transport, and assembly, accounts for about 40% of the total, while the remaining part is related to end-of-life processes. The reuse and recycling of the materials have a positive effect on overall impacts. The results also show that the overall impact is strongly related to the actual energy mix of the place where batteries are installed, even if it is usually lower than that of the solution without the batteries. The importance of a proper definition of the functional unit in the analysis is also emphasized in this work.