Chin-Sien Moo

Bio: Chin-Sien Moo is an academic researcher from National Sun Yat-sen University. The author has contributed to research in topics: Battery (electricity) & Power module. The author has an hindex of 17, co-authored 48 publications receiving 2006 citations.

Charge equalization for series-connected batteries

Abstract: A novel nondissipative charge equalization circuit is proposed for charge equalization control of series-connected batteries. Each battery associates with a subcircuit, which is essentially a buck-boost converter acting as a current diverter to redistribute the excessive energy from more affluent batteries to the hungry ones. By dynamically redistributing the charging current, charge equalization can be achieved more quickly and efficiently. The applicability of this approach is confirmed by experiments.

An Efficient Driver for Dimmable LED Lighting

Abstract: An efficient driver circuit is proposed for light-emitting-diode (LED) lamps with a dimming feature. The driver consists of a flyback converter in series with the dc-link. By processing partial power of the driver circuit for current regulation, the loss produced by power conversion can be diminished. The dimming feature is accomplished by means of current amplitude modulation (AM) or double pulse-width modulation (DPWM). The detailed circuit operation stages and analysis are provided. A laboratory circuit is designed for a 45 W LED lamp. Experimental results demonstrate that a high efficiency can be achieved, even at dimmed lamp power.

Parallel Operation of Battery Power Modules

Abstract: Operating batteries in parallel improves the battery power system management and resolves the problems of conventional battery banks that arrange batteries in series. The discharging currents of the batteries are independently controlled, but coordinated to provide a full amount of the load current. Batteries connected in parallel do not suffer from charge imbalance, and thus, can avoid being overcharged or overdischarged. Sophisticated discharging profiles can be realized to efficiently utilize the available stored energy in batteries. Some of the batteries may take rest or be isolated from the system for the open-circuit measurement during the operation period, thus, facilitating the estimation of the state-of-charge and the evaluation of state-of-health.

An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries

Abstract: This paper proposes an enhanced coulomb counting method based on the depth-of-discharge (DOD) to estimate the state-of-charge (SOC) and state-of-health (SOH) for valve regulated lead-acid (VRLA) batteries. The losses at different discharging currents are accounted for compensation to the releasable capacities. Furthermore, the SOH is revaluated at the depletion and fully charged states by the maximum releasable capacity and the charged capacity, consequently leading to more accurate SOC estimation. Through the experiments that emulate practical operations, the experimental results reveal that the maximum error is less than 6 %.

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

Abstract: 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.

Energy Storage Systems for Automotive Applications

Abstract: The fuel efficiency and performance of novel vehicles with electric propulsion capability are largely limited by the performance of the energy storage system (ESS). This paper reviews state-of-the-art ESSs in automotive applications. Battery technology options are considered in detail, with emphasis on methods of battery monitoring, managing, protecting, and balancing. Furthermore, other ESS candidates such as ultracapacitors, flywheels and fuel cells are also discussed. Finally, hybrid power sources are considered as a method of combining two or more energy storage devices to create a superior power source.

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

Abstract: 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.