The modular multilevel converter (MMC) has been a subject of increasing importance for medium/high-power energy conversion systems. Over the past few years, significant research has been done to address the technical challenges associated with the operation and control of the MMC. In this paper, a general overview of the basics of operation of the MMC along with its control challenges are discussed, and a review of state-of-the-art control strategies and trends is presented. Finally, the applications of the MMC and their challenges are highlighted.

Operation, Control, and Applications of the Modular Multilevel Converter: A Review

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

This paper investigates the characteristics of the active and reactive power sharing in a parallel inverters system under different system impedance conditions. The analyses conclude that the conventional droop method cannot achieve efficient power sharing for the case of a system with complex impedance condition. To achieve the proper power balance and minimize the circulating current in the different impedance situations, a novel droop controller that considers the impact of complex impedance is proposed in this paper. This controller can simplify the coupled active and reactive power relationships, which are caused by the complex impedance in the parallel system. In addition, a virtual complex impedance loop is included in the proposed controller to minimize the fundamental and harmonic circulating current that flows in the parallel system. Compared to the other methods, the proposed controller can achieve accurate power sharing, offers efficient dynamic performance, and is more adaptive to different line impedance situations. Simulation and experimental results are presented to prove the validity and the improvements achieved by the proposed controller.

Design and Analysis of the Droop Control Method for Parallel Inverters Considering the Impact of the Complex Impedance on the Power Sharing

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures

Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I-V performance accurately. The model can also be easily extended to other battery and power sourcing technologies.

Accurate electrical battery model capable of predicting runtime and I-V performance

A novel, accurate, compact, and power-efficient lithium-ion (Li-Ion) battery charger designed to yield maximum capacity, cycle life, and therefore runtime is presented and experimentally verified. The proposed charger uses a diode to smoothly (i.e., continuously) transition between two high-gain linear feedback loops and control a single power MOS device, automatically charging the battery with constant current and then constant voltage. An adaptive power-efficient charging scheme in the form of a cascaded switching regulator supply ensures the voltage across the charging power-intensive pMOS remains low, thereby reducing its power losses and yielding up to 27% better overall power efficiency. An 83% power-efficient printed circuit board prototype was built and used to charge several Li-Ion batteries to within plusmn0.43% of their optimum full-charge voltage and therefore within a negligibly small fraction of their full capacity

Accurate, Compact, and Power-Efficient Li-Ion Battery Charger Circuit

Flyback inverter has the advantages such as compact conformation, simple control loop, electric isolation, high step-up ratio, high efficiency, etc., therefore is an attractive solution for photovoltaic ac module applications. In this topology, BCM is more preferred compared to DCM and CCM, because of its higher power level, higher efficiency and wider switching frequency bandwidth. However, the control of BCM is more complicated due to its variable switching frequency. This also leads to the difficulty to get the accurate mathematical model between the output current iout and the reference current iref, which has a great influence on the THD of iout. This paper analyzes and proposes a mathematical model between iout and iref in BCM through theoretical derivation, and proposes a novel control strategy to generate the reference current that can decrease THD of output current. Meanwhile the realization of MPPT based on the mathematical model is also investigated. Finally, simulation and experiment results based on an improved flyback-inverter prototype are presented, which validates the proposed mathematical model and the control strategy.

Analysis and Implementation of an Improved Flyback Inverter for Photovoltaic AC Module Applications

Although SiC- mosfet has significant advantages on switching performance over traditional Si-IGBT, the switching loss of SiC- mosfet devices at hard switching rises quickly with the increment in the switching frequency. This has narrowed down further possibilities of improving efficiency and power density of the grid inverter. Zero-voltage-switching (ZVS) space-vector-modulation (SVM) technique is introduced to further push the power density of SiC- mosfet inverter. This paper focuses on the impact of applying the ZVS-SVM to three-phase two-level SiC- mosfet inverter. With the same efficiency requirement the ZVS-SVM SiC inverter can operate at a much higher switching frequency, which gives the opportunity to further reduce the size of passive components. The loss distributions, conversion efficiencies, and volumes of passive components of both a 20-kW SiC- mosfet hard-switching inverter and a 20-kW SiC- mosfet ZVS-SVM inverter have been compared under switching-frequency range from 50 to 300 kHz. Meanwhile, a new metric called “efficiency stiffness” is proposed to compare different inverters with respect to the efficiency performance against switching-frequency characteristics. In addition, high voltage overshoot of SiC- mosfet and high thermal stress of resonant inductor are the two critical issues in the SiC- mosfet ZVS-SVM inverter with high switching frequency. A power module including seven SiC- mosfet bare dies with low stray inductance is designed for ZVS-SVM inverter instead of the existing seven discrete TO-247 package SiC- mosfet s to reduce the voltage overshoots on the switches. Besides, to reduce the power loss of the resonant inductor caused by large amplitude of current at hundreds of kHz excitation frequency, design of the inductor with distributed air gap and optimal winding thickness are studied. A 20-kW SiC- mosfet ZVS-SVM grid inverter prototype is built to verify the proposed design.

20-kW Zero-Voltage-Switching SiC- mosfet Grid Inverter With 300 kHz Switching Frequency

Due to the long cold start time and slow dynamics of proton exchange membrane (PEM) fuel cell (FC) stack, operating modes transfer control strategy for fuel cell uninterruptible power supply (FC-UPS) is different from the traditional uninterruptible power supply (UPS) system. In this paper, a seamless transfer control strategy, which is suitable for FC-UPS, is proposed. The power conversion architecture of FC-UPS is presented with the characteristic analysis of PEMFC and the requirements of UPS. Then, the scheme of the seamless transfer control strategy is investigated. The proposed seamless transfer control strategy is not only capable of guaranteeing the uninterruptible load voltage, but also protecting FC against the power demands beyond its allowable bandwidth during the transition for long lifespan and safety. Finally, the control scheme has been verified on a 10-kW FC-UPS prototype.

Min Chen

Papers

Accurate electrical battery model capable of predicting runtime and I-V performance

Accurate, Compact, and Power-Efficient Li-Ion Battery Charger Circuit

Analysis and Implementation of an Improved Flyback Inverter for Photovoltaic AC Module Applications

20-kW Zero-Voltage-Switching SiC- mosfet Grid Inverter With 300 kHz Switching Frequency

Seamless Transfer Control Strategy for Fuel Cell Uninterruptible Power Supply System