Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries

By M. Roscher, Dirk Uwe Sauer. 2011

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Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells (2014, methodology): “While Chen and Rincon-Mora [17] recommended two RC groups as the optimal model, there are several references [11], [18], [19] stating that one RC group structure can provide results that are accurate enough for applications, such as PHEVs and PEVs.”
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries (2018, methodology): “Traditionally, two SOC estimation techniques have been used: open circuit voltage based techniques and coulomb counting [6], [9].”
A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries (2013, methodology): “In recent years, many methods have been proposed in the literature to improve the performance of the SOC estimation, including the current integral method [1], open-circuit voltage method [3–5], neural network method [6,7], Kalman filter method [8,9], extended Kalman filter method [10–15], unscented”
An Online SOC and SOH Estimation Model for Lithium-Ion Batteries (2017, methodology): “The OCV method is relatively accurate but needs a sufficient rest time, and it becomes less accurate if a battery has a flat OCV-SOC curve [15].”