Stable Configuration of a Li-Ion Series Battery Pack Based on a Screening Process for Improved Voltage/SOC Balancing

By Jonghoon Kim, Jongwon Shin, C. Chun, Bo-Hyung Cho. IEEE transactions on power electronics, 2012

266 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1f81975fcf20fe3e1ed9003efcfb6c0f8235a9e9

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In Automotive Engineering

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Citations in the corpus

State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (2017, background): “rately monitor battery’s SOC as it is a measure of the charge present with respect to the amount it can hold at maximum rated condition [8].”
State-of-Charge Balance Using Adaptive Droop Control for Distributed Energy Storage Systems in DC Microgrid Applications (2014, background): “In [14], a screening process is involved to improve the performance of output voltage and SoC balancing.”
State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries (2020, background): “It can be achieved by conducting battery clustering [39] to select batteries for ageing tests.”
A comprehensive review of the key technologies for pure electric vehicles (2019, background): “The state of charge (SOC) of the battery, namely SOCbat, shows the remaining amount of electric energy stored in the battery and can be defined as [22,23]”
Double-Quadrant State-of-Charge-Based Droop Control Method for Distributed Energy Storage Systems in Autonomous DC Microgrids (2015, methodology): “In [24], the screening process is employed to improve the performance of SoC balancing in a string of battery cells.”
Practical Capacity Fading Model for Li-Ion Battery Cells in Electric Vehicles (2013, background): “improved SoC controllers [25], [26], charge equalizers [27], [28], smarter chargers [29], or a hybrid battery/fuel [30] or”
Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016, methodology): “The extended Kalman filter (EKF), which linearizes the state-space equation of nonlinear systems by Taylor’s formula, has received better results on SOC estimation [14]–[17].”
Support Vector Machines Used to Estimate the Battery State of Charge (2013, methodology): “The most common methods include current integration techniques, artificial neural networks (ANNs) and fuzzy logic-based estimations [2], Kalman filterbased estimators [3]–[5], and others [6]–[9].”