Online Parameterization of Lumped Thermal Dynamics in Cylindrical Lithium Ion Batteries for Core Temperature Estimation and Health Monitoring
By Xinfan Lin, Hector Eduardo Perez, Jason B. Siegel, Anna G. Stefanopoulou, Yonghua Li, R. D. Anderson, Yi Ding, M. Castanier. IEEE Transactions on Control Systems Technology, 2013
340 citations recorded by the source corpus.
Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/d65aa448ce5af83ac7bf2ba39a12c78fa7056d8c
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In Automotive Engineering
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Citations in the corpus
- State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries (2020, background): “3) Thermal models: Another way to alleviate the influence of ambient temperature on SOH estimation is to use thermal models, which theoretically model battery heath generation and temperature rise [23].”
- Model-based real-time thermal fault diagnosis of Lithium-ion batteries (2016, methodology): “…of the battery is modelled as a linearly parameterised function of SOC and core temperature Tc. β β β= + + ( )R SOC T 6c0 1 2 where β0, β1 and β2 are constant parameters that can be determined a priori by offline identification techniques or via online estimation schemes (see Lin et al., 2013).”
- Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures (2020, methodology): “Various filters and observers have been applied to fault diagnosis for LIBSs, such as the Kalman filter (KF) [59], extended KF (EKF) [60], unscented KF [61], particle filter (PF) [62], Lunberger observer [63], and adaptive observer [64].”