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.

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 classifies the recently proposed online SOC estimation methods 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.

The performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM). Index Terms-State-of-charge estimation, partial least squares regression, Kalman filter, Lithium-ion battery. I. INTRODUCTION  ith the significant progress of the battery technology, Lithium-ion (Li-ion) batteries have become a promising choice for Electrical Vehicle (EV) [1] and Battery Energy Storage System (BESS) [2], [3]. The extensive usage of the Li-ion batteries is mainly because of their superior properties including long lifespan, high energy density, low self-discharge 

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An off-board dc fast battery charger for electric vehicles (EVs) with an original control strategy aimed to provide ripple-free output current in the typical EV batteries voltage range is presented in this paper. The proposed configuration is based on modular three-phase interleaved converters and supplied by the low-voltage ac grid. The ac/dc interleaved three-phase active rectifier is composed of three standard twolevel three-phase converter modules with a possibility to slightly adjust the dc-link voltage level in order to null the output current ripple. A modular interleaved dc/dc converter, formed by the same three-phase converter modules connected in parallel, is used as an interface between the dc-link and the battery. The use of low-cost, standard and industry-recognized three-phase power modules for high-power fast EV charging stations enables the reduction of capital and maintenance costs of the charging facilities. The effect of coupling on the individual input/output inductors and total input/output current ripples has been investigated as well, considering both possible coupling implementations, i.e. inverse and direct coupling. Numerical simulations are reported to confirm the feasibility and the effectiveness of the whole EV fast charging configuration, including the proposed control strategy aimed to null the ripple of the output current. Experimental results are provided by a reduced scale prototype of the output stage to verify the ripplefree output current operation capability.

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In this paper, a control method is proposed that allows the extraction of maximum power from each individual photovoltaic string connected to the Modular Multilevel Converter (MMC) and inject balanced power to the AC grid. The MMC solution used does not need additional DC–DC converters for the maximum power point tracking. In the MMC, the photovoltaic strings are connected directly to the sub-modules. It is shown that the proposed inverter solution can provide balanced three-phase output power despite an unbalanced power generation. The maximum power of the photovoltaic string is effectively harnessed due to the increased granularity of the maximum power point tracking. An algorithm that tracks the sub-module capacitor voltages to their respective voltage references is proposed. A detailed modeling and control method for balanced operation of the proposed topology is discussed. The operation of the MMC under unbalanced power generation is discussed. Simulation results are provided that show the effectiveness of the proposed control under unequal irradiance.

In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.