State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering

By Zheng Chen, Yuhong Fu, Chunting Chris Mi. IEEE Transactions on Vehicular Technology, 2013

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Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “[68], experimental data were used to develop the battery model to reduce the effect of measurement and process noise signals.”
A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature (2018, background): “The open circuit voltage of the battery as a function of SOC can be represented as a polynomial equation [37].”
State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization (2015, methodology): “Using the method given in [25], eight LIBs connected in series are tested.”
SoC Estimation for Lithium-ion Batteries: Review and Future Challenges (2017, methodology): “Some good examples of how to apply and run an EKF algorithm (as depicted in Figure 9) for SoC estimation are presented in [7,21,91,102].”
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (2017, methodology): “Lastly, this SOC estimate is further corrected using a KF like in [15].”
Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016, background): “The absolute error ea in the literature [10], [17] is confined to ±2% error band and ±3% in [11] and [16].”