Enhanced Physics-Based Models for State Estimation of Li-Ion Batteries

Luder, Daniel (15 October 2020). Enhanced Physics-Based Models for State Estimation of Li-Ion Batteries In: COMSOL Conference 2020. online. 14./15. Oktober 2020.

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Battery models and state estimation algorithms are a key components of todays advanced Battery Management Systems (BMS). Thereby, the battery models are used to estimate non-measurable states in the battery to ensure safety and availability while prolonging its life. This paper focuses on pseudo-2D physics-based battery models namely the Doyle-Fuller-Newman (DFN) model and Single Particle Model (SPM) that are capable to represent battery internal electrochemical states, that are vital for high precision simulation of the battery behavior. A three-step DFN model parameter identification procedure including QR decomposition with column pivoting, microstructure analysis and model optimization is proposed and applied on a commercial 18650 lithium-ion battery. The DFN model is validated with drive cycles as they occur in Electric Vehicles (EV) revealing a RMSE smaller than 18mV on average over the full SOC range. In the end, the DFN model is used to validate a state-space implementation of a SPM with electrolyte dynamics, which can be implemented on an embedded system to estimate battery states in real-time.

Item Type:

Conference or Workshop Item (Paper)


School of Engineering and Computer Science
BFH Centres and strategic thematic fields > BFH Energy Storage Research Centre


Luder, Daniel;
Caliandro, Priscilla and
Vezzini, Andrea0000-0002-5315-6135


T Technology > TK Electrical engineering. Electronics Nuclear engineering




Andrea Vezzini

Date Deposited:

27 Jan 2021 09:19

Last Modified:

27 Jan 2021 09:19





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