Optimized estimation of Li-ion battery parameters to improve the Enhanced Self-Correcting model with nonlinear least-squares data fitting and SoC estimation using Invariant EKF
Year: 2025
Type: article
Abstract: In recent years, batteries have become increasingly important, especially lithium batteries. Battery modeling is essential for many applications, such as state-of-charge (SoC) estimation. To obtain a high-performance model, the estimation of battery parameters must be precise. The Enhanced SelfCorrecting model describes several battery dynamics, such as hysteresis cycling. This model can be further improved by making these parameters variable as a function of the SoC and temperature, making the model more efficient. In this study, we estimate the circuit parameters using a single RC branch during charging and discharging. This enables us to accurately describe battery dynamics and improve the Enhanced Self-Correcting model. MATLAB/Simulink Toolboxes can simplify many tasks, and nonlinear leastsquares data fitting using the Trust-Region-Reflective algorithm produces remarkable results. We then validate the model using the SoC estimation with the invariant extended Kalman filter (IEKF), which is reliable for nonlinear systems. The correction of this filter is independent of the output error, leading to greater accuracy and performance.
Authors: Yassine Derouech, Abdelouahed Mesbahi
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,400
OpenAlex ID: W7083587180