Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries

By S. Schwunk, Nils Armbruster, Sebastian Straub, Johannes Kehl, M. Vetter. 2013

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “One common way is through the use of models simulating the behaviours of cells [8,9], followed by various optimization algorithms and observers, such as the Kalman filter family [10] and particle filter [11] to identify the parameters and SOH states [4].”
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, unknown): “For instance, dual extended Kalman filtering (EKF) in [11]–[13], particle filtering (PF) in [14], dual slide mode observer in [15], and Lyapunovbased adaptive observer in [6] and [7].”
The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles (2018, methodology): “Combined estimation of the SOC and SOH was implemented in [27]–[29].”
Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks (2017, methodology): “The filtering problem presented here differs from most of the Bayesian filtering problems analyzed in the prognostic literature, where the objective was the estimation of physical quantities collected in the system state vector [25].”