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State of health (SoH) estimation plays a key role in smart battery health prognostic and management. However, poor generalization, lack of labeled data, and unused measurements during aging are still major challenges to accurate SoH estimation. Toward this end, this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation. Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells, the proposed method achieves accurate and robust estimations using limited labeled data. A filter-based data preprocessing technique, which enables the extraction of partial capacity-voltage curves under dynamic charging profiles, is applied at first. Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder. The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data, which boosts the performance of the estimation framework. The proposed method has been validated under different battery chemistries, formats, operating conditions, and ambient. The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles, with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%, and robustness increases with aging. Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method. This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

Dear Editor, Lithium-ion (Li-ion) battery has become a promising source to supply and absorb energy/power for many energy-transportation applications. However, Li-ion battery capacity would inevitably degrade over time, making its related ageing prediction necessary. This letter presents effective battery calendar ageing trajectory prediction by deriving a knowledge-guided data-driven model with transfer concept. More specifically, this data-driven model is based on the support vector regression (SVR) technology. To ensure highly-accurate prognostics of battery calendar ageing trajectory under wit-nessed conditions, a knowledge-guided kernel is first developed by coupling the mechanism and empirical knowledge elements of battery storage temperature, state-of-charge (SoC), and time. To im-prove model's generalization ability under unwitnessed conditions, the knowledge-guided data-driven model is then equipped with trans-fer concept by adding a classical Gaussian kernel for all inputs. A well-rounded real battery ageing dataset under eight different storage conditions is collected to evaluate the performance of developed model. Results illustrate that this knowledge-guided battery ageing trajectory prediction model presents satisfactory accuracy for wit-nessed conditions with R2 over 0.98. After using only 20% starting capacity point to tune its transfer part, it can also generalize well for unwitnessed conditions with R2 over 0.97, further heavily reducing the required ageing experimental time and cost.

Artificial neural networks are widely studied for the state of health (SOH) estimation of Lithium-ion batteries because they can recognize global features from the raw data and are able to cope with multi-dimensional data. But the performance of the model depends to some extent on the selection of the hyperparameters, which remain constant during model training. To improve the generalization performance as well as accuracy, an ensemble learning framework is proposed for battery SOH estimation, where multiple extreme learning machines are trained combined with bagging technology. The numbers of bags and neurons of the base model are then tuned by five commonly used hyperparameter optimization methods. Moreover, the SOH value with maximum probability density is selected as the output estimate to further improve the estimation accuracy. Finally, experimental results on both NMC and LPF batteries demonstrate that the proposed method with hyperparameter optimization can achieve stable and accurate battery SOH estimation. Regardless of which optimization method is used, the average percentage error for SOH estimation of NMC and LFP batteries can keep below 1% and 1.2%, respectively.

Power synchronization control (PSC) is one of the popular control schemes in grid-forming control-based converters because it simulates the grid support capability of conventional synchronous generators. However, prior research is based on two-level converters which do not have complex internal circuits, and whether PSC can be directly applied to the modular multilevel converter (MMC) topology since MMC has sub-module capacitor voltage ripples and inherent second harmonic circulating current algorithm, has not been analyzed. This paper establishes the small signal model of MMC with PSC considering the MMC internal dynamic and circulating current suppression control (CCSC). The power oscillation phenomenon when grid short-circuit ratio (SCR) increases is also demonstrated with the closed-loop system eigenvalues calculation and verified with the experimental results.

Studying and analyzing battery aging behavior is crucial in battery health prognostic and management. This paper conducts novel and comprehensive experiments to evaluate battery aging under variable external stresses, including different dynamic load profiles and variable environmental temperatures. Respond analysis in the time and frequency domain is performed to account for the different aging rates under different current loadings, where the statistic calculation and fast Fourier transform are used for the analysis. The empirical model is used to fit the fade curve for the comparisons between constant and variable temperatures. The capacity decrease and internal resistance increase are extracted to evaluate capacity and power fade, respectively. The experimental results show that the urban dynamic operating conditions help to prolong the service life compared to the constant current aging case. In contrast, the aging under the highway profile accelerates the aging process. Although the average temperature is the same as under constant temperature conditions, variable temperature conditions accelerate battery aging.

This paper presents the setting and coordination of over-current relays in an offshore wind power plant (WPP) grid connected only to the power electronic converters with limited fault current capability. The limited fault current injection capability of the converters has been considered as a hindrance for the relay setting. This characteristic can be utilized to develop a deterministic picture of the radial grid network and then the relays settings could be applied. The relay coordination has been demonstrated through an implementation in the Real Time Digital Simulator (RTDS) platform. The setting of relay parameters based upon maximum nominal currents is explained and the consequences are investigated.

During unbalanced grid voltage faults the Power injected to the grid experiences 100Hz oscillations as a result of interactions between positive and negative sequence components of three-phase voltages and currents. These oscillations can become as high as %50 percent of the rated power. In this article an improved controller is proposed which present different behavior during normal operation and faults to keep track of non-sinusoidal current reference signals. The reference signals are calculated to obtain zero power oscillations. Reconfigurable resonant controllers are used for this purpose applied to 3-level Neutral-point-clamped converter in the megawatt range.

High penetration of wind energy into the grid may introduce stability and power quality problems due to the fluctuating nature of the wind and the increasing complexity of the power system. By implementing advanced functionalities in power converters, it is possible to improve the performance of the wind farm and also to provide grid support, as it is required by the grid codes. One of the main compliance difficulties that can be found in such power plants are related to reactive power compensation and to keep the harmonics content between the allowed limits, even if the power of the WPP converters is increasing. This paper deals with an advanced control strategy design of a three-level converter performing STATCOM and Active Filter functionalities. The proposed system is called Smart-STATCOM since it has the capability of self-controlling reactive power and harmonic voltages at the same time. Therefore, deciding the amount of non-active currents (fundamental, 5th and 7th harmonics) to be injected depending on the PCC voltage quality. Experimental results of the proposed control strategy are analyzed in order to validate the performance of the entire system. Keywords- STATCOM, Active Filter, Power Quality

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is developing rapidly. To ensure the battery safe usage and to reduce its average lifecycle cost, an accurate State of Charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper reviews recently proposed online SOC estimation methods and classifies them into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.

With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories,that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.