Cell Voltage Equalizer Using a Selective Voltage Multiplier with a Reduced Selection Switch Count for Series-Connected Energy Storage Cells

Abstract

Cell voltage equalization is mandatory to eliminate voltage imbalance of series-connected energy storage cells, such as lithium-ion batteries (LIBs) and electric double-layer capacitors (EDLCs), to ensure years of safe operations. Although a variety of cell equalizers using selection switches have been proposed, conventional techniques require numerous switches in proportion to the cell count and are prone to complexity. This paper proposes a novel cell voltage equalizer using a selective voltage multiplier. By embedding selection switches into the voltage multiplier-based cell voltage equalizer, the number of selection switches can be reduced in comparison with that in conventional topologies, realizing the simplified circuit. A prototype for twelve cells was built, and an equalization test using LIBs was performed. The voltage imbalance decreased down to approximately 20 mV by the proposed equalizer, and the standard deviation of cell voltages at the end of the equalization test was as low as 10 mV, demonstrating its equalization performance.

Novel Bidirectional Voltage Equalizer Based on Double Output Dual Switch Flyback Circuit

“…It realized the control of a group of signals and simplify the control logic. The Multi-Stacked Current Doublers proposed in the literature used Half-Bridge Converter to achieve a DC-to-DC conversion, so that the energy was quickly transferred [14][15][16][17]. Because the energy can pass through the transformer in both directions, there is no need for a reset circuit and a degaussing circuit, and the secondary of the transformer can automatically select the monomer with the lowest power without complicated control, but the overall volume is still too large.…”

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

“…Cell inconsistencies in a LIB pack are a common issue; thus an appropriate BMS is also indispensable for the safe and reliable operation of the LIB pack as well as every single cell of the battery pack [9,10]. A BMS can be designed to serve many functions including but not limited to data acquisition, estimation of the state of charge (SOC) [11,12] and state of health (SOH) [13,14], temperature measurement/estimation [15], cell balancing [16,17], fault detection/diagnosis [18,19] and thermal management [16]. Very recently researchers have started paying attention to the detection/diagnosis of faults after the occurrence of several accidents in e-transportation due to the failure of the LIB system [20,21].…”

A Battery SOC Estimation Method Based on AFFRLS-EKF

“…The application of machine learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health, and remaining useful life [12]. Electromagnetic interference (EMI) of battery management systems (BMSs) will cause measurement errors of current and voltage signals, which will affect the performance of BSM [13][14][15][16][17][18][19]. For the situation that battery working conditions change rapidly, a recursive calculation method based on the Kalman filter is adopted [20][21][22].…”