Big data applications such as Artificial Intelligence (AI) and Internet of Things (IoT) have in recent years been leading to many technological breakthroughs in system modeling. However, these applications are typically data intensive, thus requiring an increasing cost of resources. In this paper, a first-of-its-kind comprehensive review of data selection methods across different engineering disciplines is given in order to analyze the effectiveness of these methods in improving the data efficiency of mathematical modeling algorithms. Eight distinct selection methods have been identified and subsequently analyzed and discussed on the basis of the relevant literature. In addition, the selection methods have been classified according to three dichotomies established by the survey. A comparative analysis of these methods was conducted along with a discussion of potentials, challenges, and future research directions for the research area. Data selection was found to be widely used in many engineering applications and has the potential to play an important role in making more sustainable Big Data applications, especially those in which transmission of data across large distances is required. Furthermore, making resource-aware decisions about the use of data has been shown to be highly effective in reducing energy costs while ensuring high performance of the model.

Lithium-ion (Li-ion) batteries are the preferred choice for energy storage applications. Li-ion performances degrade with time and usage, leading to a decreased total charge capacity and to an increased internal resistance. In this article, the wavelet analysis is used to filter the voltage and current signals of the battery to estimate the internal complex impedance as a function of state of charge (SoC) and state of health (SoH). The collected data are then used to synthesize a battery digital twin (BDT). This BDT outputs a realistic voltage signal as a function of SoC and SoH inputs. The BDT is based on feedforward neural networks trained to simulate the complex internal impedance and the open-circuit voltage generator. The effectiveness of the proposed method is verified on the dataset from the prognostics data repository of NASA.

Battery packs in electric vehicles are managed by battery management systems that influence the state of charge among the cells in the pack, where such systems have received much attention in research. More recently, balancing the temperature among the cells has become a research topic. In our work, we consider a dual-balancing problem where we aim to balance both the parameters of the state of charge and temperature. We consider a Smart Battery Pack, where individual cells can be bypassed, meaning that no current is going to or from the cell, which allows the cell to cool off while the cell does not charge or discharge. Moreover, a smart battery pack can estimate each cell's characteristics, which, in turn, can be used to define a model of cell and battery pack behavior. We conduct experiments using the model of a battery pack where each cell differs in its configuration as an effect of aging. For such a pack with heterogeneous cells, we use Q- Learning in U ppaal Stratego to synthesize a controller that maximizes the time spent in a balanced state, meaning that all cells' states are within a specific range of each other. We show significant improvements in two aspects compared with two threshold-based controllers that balance either state of charge or temperature. The synthesized controllers are only unbalanced with the state of charge between 1-4% of the time and for temperature between 15-20% of the time. The threshold-based controllers are either unbalanced for the state of charge for as much as 37 % of the time or for temperature for as much as 44 % of the time. Finally, the maximum variations of state of charge and temperature among the cells are decreased.

Artificial intelligence (AI) has been widely studied for batteries remaining useful lifetime prediction. However, the requirement of big datasets to train a robust AI model limits its practical application, particularly when batteries exhibit diverse degradation behaviors under different working conditions. Collecting sufficient data through laboratory testing can take several years. To tackle these challenges, a few-shot learning-based method for battery early lifetime prediction is proposed where only 6 cycles of charging data are required. The proposed method models batteries with different lengths of cycle life separately, considering that aging features recognized from early cycles might be different for long-life and short-life batteries. First, an auto encoder is trained to group batteries into long-life and short-life classes. The prototypical networks algorithm is employed to learn a metric space where samples from the same class are brought closer together than samples from different classes. Then based on the classification result, different lifetime models are selected, resulting in the final prediction. Few-shot learning technique is utilized to enable accurate and early health assessment of lithium-ion batteries. Compared to building a single model for all batteries throughout their lifetimes, the proposed method reduces the required data size, simplifies AI modeling, and improves prediction accuracy. Finally, the effectiveness of the proposed framework is verified using the accelerated aging dataset from 124 batteries.

Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This article proposes a novel semi-supervised self-learning method for battery lifetime prediction. First, three health indicators (HIs) are extracted from the partial capacity-voltage curve. Second, the capacity estimation model and lifetime prediction model are built using data from three randomly selected batteries in the source domain. Then, the HIs are used to reconstruct the historical capacities to provide pseudo values for self-training of the lifetime model. Finally, the self-trained lifetime model is used to predict future degradation. The uncertainty expression is also included to provide the probabilistic prediction of future capacities. Different application scenarios are considered in the verification. The mean lifetime prediction error is less than 23 cycles with only three known checkpoints for batteries aging under different profiles. Predictions for different battery types show that the errors are less than 50 cycles with relative errors less than 4.1% for long lifespan batteries, and less than 20 cycles with relative errors less than 5.21% for short lifespan batteries. This article guides proper solutions for lifetime prediction when the labeled capacities in the real world are limited.

Aiming at the instability problem of traditional power synchronization control (PSC) in a stiff grid, an advanced PSC based on modular multilevel converter (MMC) is proposed in this paper, which can effectively improve converter stability in high short circuit ratio (SCR) scenarios. The key point of the proposed PSC is to eliminate the impact of the arm inductor voltage and capacitor voltage ripple on the converter output voltage, which means a generalized decoupling process in the PSC controller. Based on the decoupling correction, MMC can be treated as a controlled voltage source regardless of the internal impedance circuit and the power oscillation between MMC and the stiff grid can be suppressed effectively. The control strategy is verified and compared with traditional PSC in time simulations.

The large-scale retirement of electric vehicle traction batteries poses a huge challenge to environmental protection and resource recovery since the batteries are usually replaced well before their end of life. Direct disposal or material recycling of retired batteries does not achieve their maximum economic value. Thus, the second-life use of EV batteries has become the most economical and environmentally friendly solution. However, there are still many issues facing second-life batteries (SLBs). To better understand the current research status, this article reviews the research progress of second-life lithium-ion batteries for stationary energy storage applications, including battery aging mechanisms, repurposing, modeling, battery management, and optimal sizing. Energy management strategies are reviewed to maximize the economic benefits for SLBs, and the less-demanding applications of SLBs are presented. The technical challenges and future development trends of battery reusing technologies are also discussed. Finally, the conclusions and relevant recommendations for future studies are summarized.

Lithium-ion batteries have high energy density, lightweight and long life cycle, thus they are the choice for powering electric vehicles. The needed high voltage battery pack is achieved using series-connected cells, that ideally should be identical. However, parameter variations of cells in EVs, along with different working conditions can cause State of Charge (SoC) and temperature imbalances that shorten battery lifetime. Moreover, a series connection can potentially be exposed to single-cell failure. This paper proposes a redundant smart battery topology based on the series connection of individual cell modules. Each module is formed by a cell with an insertion/bypass circuit and a wireless processor that monitors cell states and communicates with a Master controller. To synthesize the nominal voltage, a battery pack with a small redundant number of cells is considered. In this way, it can be made both fault-tolerant and reach higher safety. The problem is then shifted to the control algorithm that at each sampling time has to select "n" cells out of the total cells, according to some goals. A Machine Learning-based control algorithm was developed and tested in Matlab to insert/bypass the cells and reach simultaneous balancing of both SoC and temperature. The new method based on the K-nearest algorithm has been compared in simulation with a conventional sorting balancing method and showed superior performance, especially in temperature balancing.

To ensure the reliable operation of the batteries and maximize their service lifetime, it is important to have accurate knowledge of their state of health (SOH). Using data-driven methods to estimate the SOH is extensively studied and the feature data plays an important role in such methods. As fuzzy entropy (FE) can capture the variation of the voltage during the battery aging process, it can be used as a feature. In this paper, in order to reduce the noise from raw feature data, six smoothing methods are introduced to pre-process the FE. Furthermore, the relationship between the smoothed feature and SOH is established by support vector machine and Gaussian process regression. The comparison results show that adding a simply feature smoothing step before the model training can improve the SOH estimation performance. Finally, the effectiveness of the proposed method is verified by experimental results.

Battery management systems (BMSs) play a critical role to ensure the safety and extend the lifetime of the Lithium-ion batteries. In the conventional BMSs, there are some disadvantages such as low scalability and insufficient flexibility. In order to enhance these performances and further increase their reliability, the smart batteries are proposed. The smart batteries are the cell-level BMSs, which are built by the integrated cell modules connected in series. Each integrated cell module can monitor its own states, control the bypass circuit, and can communicate with the master controller, thus they are also called smart cells. This paper presents the state-of-the-art of management architectures, communication implementations, bypass circuits, and bypass decision strategies in the smart batteries. Four different existing smart batteries are compared. According to the comparison, future trends of the smart batteries are provided.

This paper proposes a novel fault-tolerant control method for modular multilevel converter (MMC) with an improved level-shifted carrier PWM, based on the concept of virtual voltage. First, an improved phase disposition level-shifted (PD-LS) PWM with only one carrier in each arm is adopted. Then, based on the virtual voltage idea, a simplified control method is proposed to realize fault-tolerant operation without carriers reconfiguration. Compared with traditional fault-tolerant control methods, due to the reduced carriers generation and decreased comparisons between carriers and modulation waves, the modulation process of the proposed scheme can be dramatically simplified during both the pre-fault and the post-fault process. The effectiveness of the proposed method is verified by simulation and experiment results.

During the last 4-5 years the plug in electrical vehicle market has known a tremendous growth, only in 2017 307400 units were sold, representing an increase of 39%. The battery management system and the battery charger are two of the most demanding systems in terms of required computational power added to this type of vehicles. Also, features related to driving automation, safety and comfort are becoming more complex, therefore a higher level of integration is necessary. The objective of this paper is to integrate an intelligent wireless battery management system into the same system on chip as the battery charger and to prove that a single IC can parallelize and coordinate all tasks conventionally carried out by multiple dedicated processors. The proposed solution exhibits higher integration, improved balancing, increased reliability and extended range.

A hybrid modular multilevel converter (MMC), consisting of full-bridge sub-modules (FBSMs) and half-bridge sub-modules (HBSMs), is an efficient and DC-fault tolerant topology, which can be used in overhead-line HVDC transmission application. Additionally, it can work under over-modulation condition (i.e., arm submodule clusters may generate negative voltage during a fundamental cycle) to improve the utilization of submodules (SMs). Regarding the over-modulation issue, this paper proposes a reduced-switching-frequency modulation method, considering the different dynamics of FBSMs and HBSMs, for hybrid MMCs. Moreover, an improved wave-distributing approach which can reduce losses and make the operating time and losses of the two types of SMs more even than the conventional method is presented. Finally, the effectiveness of the proposed method is verified by an over-modulation hybrid MMC simulation model built based on PSCAD/EMTDC platform.

This paper analyzes a 10MW medium voltage Back-to-Back (BTB) Modular Multilevel Converter (MMC) without a DC-Link capacitor with half-bridge submodules. It focusses on the system behavior under single-line-to-ground (SLG) fault when there is no capacitor on the DC-Link. The fault current is computed numerically and compared with the simulated results. The converter control behaviour with regards to the injection of the positive sequence currents in the presence of unbalanced terminal voltages has been investigated. Finally, the converter protection using cell level chopper control strategy is presented to prevent DC overvoltages in the sub-modules during faults.

Modular multilevel converters (MMC) are penetrating due to their superior performances. Due to the modular structure of the converter, communication platform has to be established between the submodules (SMs) and a central controller unit (CCU). When the communication platform is designed for such an application, several key parameters have to be considered such as high-speed data transfer with low propagation delay, data integrity, and ability for accurate synchronization. In order to minimize the data flow in the MMC, a hierarchical control is proposed where a CCU calculates an identical reference for all the SMs, while the modulation and capacitor voltage balancing is performed in the controller from the SM. Thus, at each sampling instance, only four bytes references are sent by the central controller to the controllers from the SM, while one or two bytes are received from SMs. Furthermore, the control algorithm is validated through experiments.

This paper develops a complete control scheme for the Modular Multilevel Converter (MMC) in a STATCOM application. The focus is laid on the internal balancing of the converter and its reliable operation under internal mismatches as well as under external unbalanced conditions. The leg energy controller ensures that the sum of the capacitor voltages of each phase are equal. An intuitive arm energy controller is also implemented which allows the decoupled control of the energy levels of every arm. The control performance of the STATCOM was evaluated under asymmetrical grid faults and the simulation results verified the capability to provide maximum reactive power to the AC grid while maintaining internal balance through the individual control of each arm energy level.

To cope with power from large-scale renewable systems, more and more power electronic converters are used in parallel. Typically, carrier interleaving is used, thus leading to a decrease in filtering requirements. However, this causes additional circulating current to flow between the modules of the converter. The modules of the parallel converters are Voltage Source Converters (VSC). Usually, a coupled inductor (CI) is used to suppress the circulating current. However, the CI can be sensitive to the low order harmonic content of the circulating current which can appear owing to parameter mismatches between the converters. If the value of the low frequency circulating current is too high, this can saturate the CI. In order to ensure stable operation of the converter, this article presents a circulating current control based on the Proportional Resonant (PR) controller, which reduces the value of the low frequency circulating current. Furthermore, a specially designed sampling method is also presented, which ensures that only the fundamental component of the circulating current is fed to the circulating current controller.

Transformer-less string PV inverter is getting more and more widely utilized due to its higher efficiency, smaller volume and weight. However, without the galvanic isolation, the leakage current limitation and operation safety became the key issues of transformer-less inverters. This paper simplifies the leakage current generation circuit model and presents a leakage current estimation method both in real time and frequency domain. It shows that the leakage current is related to the circuit stray parameters, output filter and common mode voltage. Furthermore, with the proposed analysis method, the leakage current generation of H-bridge with different modulation methods and HERIC inverter are discussed individually. At last, the presented method has been verified via simulation.

Lithium-Sulfur (Li-S) batteries are an emerging energy storage technology, which draw interest due to its high theoretical specific capacity (approx. 1675 Ah/kg) and theoretical energy density of almost 2600 Wh/kg. In order to analyse their dynamic behaviour and to determine their suitability for various commercial applications, battery performance models are needed. The development of such models represents a challenging task especially for Li-S batteries because this technology during their operation undergo several different chemical reactions, known as polysulfide shuttle. This paper focuses on the comparison of different parametrization methods of electrical circuit models (ECMs) for Li-S batteries. These methods are used to parametrize an ECM based on laboratory measurements performed on a Li-S pouch cell. Simulation results of ECMs are presented and compared against measurement values and the accuracy of parametrization methods are evaluated and compared.

The line current ripple and the size of the dc-link capacitor can be reduced by interleaving the carriers of the parallel connected Voltage Source Converters (VSCs). However, the interleaving of the carriers gives rise to the circulating current between the VSCs, and it should be suppressed. To limit the circulating current, magnetic coupling between the interleaved legs of the corresponding phase is provided by means of a Coupled Inductor (CI). The design of the CI is strongly influenced by the Pulsewidth Modulation (PWM) scheme used. The analytical model to evaluate the flux-linkage in the CI is presented in this paper. The maximum flux density and the core losses, being the most important parameters for the CI design, are evaluated for continuous PWM and discontinuous pulsewidth modulation (DPWM) schemes. The effect of these PWM schemes on the design of the CI is discussed. The simulation and the experimental results are finally presented to validate the analysis.

The power handling capability of the state-of-the-art semiconductor devices is limited. Therefore, the Voltage Source Converters (VSCs) are often connected in parallel to realize high power converter. The switching frequency semiconductor devices, used in the high power VSCs, is also limited. Therefore, large filter components are often required in order to meet the stringent grid code requirements imposed by the utility. As a result, the size, weight and cost of the overall system increase. The use of interleaved carriers of the parallel connected VSCs, along with the high order line filter, is proposed to reduce the value of the filter components. The theoretical harmonic spectrum of the average pole voltage of two interleaved VSCs is derived and the reduction in the magnitude of some of the harmonic components due to the carrier interleaving is demonstrated. A shunt LC trap branch is used to sink the dominant harmonic frequency components. The design procedure of the line filter is illustrated and the filter performance is also verified by performing the simulation and the experimental study.

Modular multilevel converter (MMC) is a promising multilevel topology for high-voltage applications that has been developed in recent years. The control of MMCs has been analyzed in detail in many papers, showing that the converter capacitors can be kept charged and balanced by controlling the circulating current of each leg. The set-point signal of the circulating current is probably the most difficult variable to choose. Typically, it is chosen in such a way to control the total energy and the energy unbalance of each leg. Although several theories are available, the control of the circulating current is still a complex task and cannot be fully tackled with traditional linear control techniques. In this paper a multiple time-scale analysis is proposed to determine an approximated model of the MMC that can be used to solve the control problem of the capacitor voltages. In addition, it is shown that the reference signal of the circulating current can be built by combining orthogonal functions of the measured voltages and currents. Numerical simulations are used to test the feasibility of the developed approach.

The installation of photovoltaic (PV) plants has been expanding rapidly across the world during the last years. In this paper, a methodology for the design optimization of PV plants is presented, which, in contrast to the conventional PV plant design approaches, is suitable to be executed using high time-resolution (i.e., 1-min-average) values of the meteorological input data. Due to the nonlinear operation of the devices comprising a PV plant, this allows for the accurate estimation of the PV plant performance during its operational lifetime period. A parallel processing-based implementation of genetic algorithms has been employed, which, compared to the serial execution, provides the ability to accomplish the proposed optimal design procedure in a considerably shorter time interval. The design optimization results confirm that the proposed method successfully accounts for both the meteorological conditions and the operational characteristics of the PV plant components and incorporates their impact on the PV plant energy production and cost in the design process. Thus, the proposed optimization method allows for optimum design of PV systems, which will provide maximum economic profit during their lifetime period.

Thermal models are needed when designing power converters for Wind Turbines (WTs) in order to carry out thermal and reliability assessment of certain designs. Usually the thermal models of Insulated Gate Bipolar Transistors (IGBTs) are given in the datasheet in various forms at component-level, not taking into account the thermal distribution among the chips. This is especially relevant in the case of Press-Pack (PP) IGBTs because any non-uniformity of the clamping pressure can affect the chip-level thermal impedances. This happens because the contact thermal resistances in the thermal impedance chains are clamping pressure dependent. In this paper both component-level and chip-level dynamic thermal models for the PP IGBT under investigation are developed. Both models are developed using geometric parameters and material properties of the device. Using the thermal models, the thermal impedance curves under various mechanical clamping conditions are derived. Moreover, the deformation of the internal components of the PP IGBT under operating-like conditions is investigated with the help of the thermal models and the coefficient of thermal expansion (CTE) information.

Photovoltaic (PV) power among all renewable energies had the most accelerated growth rate in terms of installed capacity in recent years. Transmission System Operators (TSOs) changed their perspective about PV power and started to include it into their planning and operation, imposing PV systems to be more active in grid support. Therefore, a better understanding and detailed analysis of the PV systems interaction with the grid is needed; hence power hardware in the loop (PHIL) testing involving PV power can be a solution to address the testing challenges. To test PV systems for grid code (GC) compliance and supply of ancillary services, first the grid has to be simulated using PHIL, but in order to achieve it, different interface algorithms (IA) had to be evaluated in terms of system stability and signal accuracy.

Two existing methods to implement resampling modulation technique for modular multilevel converter (MMC) (the sampling frequency is a multiple of the carrier frequency) are: the software solution (using a microcontroller) and the hardware solution (using FPGA). The former has a certain level of inaccuracy in terms of switching instant, while the latter is inflexible with higher cost. In order to overcome these drawbacks, this paper proposes an alternative solution, which uses high frequency saw-tooth carrier and two independent comparators, usually available in microcontrollers, to create the proper switching instances needed for the resampling modulation technique. The software implementation of the proposed phase shifted PWM (PS-PWM) method, and its application in a distributed control system for MMC, are fully discussed in this paper. Simulation and experiment results show that the proposed solution can realize the resampled uniform PWM and provide high effective sampling frequency and low time delay, which is critical for the distributed control of MMC.

Lithium-ion batteries are increasingly considered for a wide area of applications because of their superior characteristics in comparisons to other energy storage technologies. However, at present, Lithium-ion batteries are expensive storage devices and consequently their ageing behavior must be known in order to estimate their economic viability in different application. The ageing behavior of Lithium-ion batteries is described by the fade of their discharge capacity and by the decrease of their power capability. The capability of a Lithium-ion battery to deliver or to absorb a certain power is directly related to its internal resistance. This work aims to investigate the dependency of the internal resistance of lithium-ion batteries on the storage temperature and on the storage time. For this purpose, accelerated ageing calendar lifetime tests were carried out over a period of one year. Based on the obtained laboratory results, an empirical ageing model was developed; the model is able to predict with accurately the increase of the internal resistance of Lithium-ion batteries during calendar (storage) ageing. Based on the proposed ageing model, it was found out that the internal resistance of the studied Lithium-ion battery cell will double after approximately eleven years if stored at 25°C.

Fulfilling ambitious goals of the full transition from the centralized, fossil fuel-based conventional generation units into distributed and eco-friendly renewables can be difficult to achieve without energy storage systems due to technical and economical challenges. Energy storage system addition to wind turbines/wind farms is one of the most promising solutions to problems related with the further integration of wind power into the grids with already high wind penetration. Lithium ion (Li-ion) batteries are constantly improving their performance and are becoming attractive for stationary energy storage applications due to their characteristics such as fast response, high power capability, high efficiency, and long lifetime. This paper deals with the lifetime of the LiFePO4/C battery system when it provides wind power output smoothing service. A semi-empirical lifetime model for these battery cells is developed based on accelerated lifetime testing of LiFePO4/C battery cells. The developed Li-ion battery lifetime model is later used for the investigation on the lifetime of Li-ion battery energy storage system (BESS) under different energy management strategies for the wind power variations smoothing service.

Modular multilevel converter (MMC) is the latest converter topology suitable for the transformerless applications in HVDC transmission. HVDC systems are required to remain connected during grid faults, provide grid support and completely decouple the healthy side from the faulty one. The MMC converter weak points are challenged by this particular condition and by these demands. This paper demonstrates the effect of negative and zero sequence current control in MMC-HVDC during asymmetric grid faults. A current limitation strategy for MMC is derived and verified through simulations.