Accurate and robust state of charge (SOC) estimation is vital for reliable and safe battery operations. However, the nonlinear and time-varying dependence of the model parameters on the battery states makes the problem challenging. To address this task, we propose a moving-horizon estimation (MHE)-based robust approach for joint state and parameter reconstruction. Due to its optimization-based nature, the computational burden of MHE is a crucial challenge. To overcome this, we introduce a real-time adaptive sampling method based on wavelet analysis. The resulting adaptive multirate MHE dynamically selects the best measurements for solving the estimation problem. Such an approach reduces the computational burden by focusing on the most informative data in the measurement buffer. Last, we implement a parallelized observer structure, combining both multirate and reduced-order MHEs to further lower the computational burden while maintaining estimation accuracy. The proposed methods are validated using a first-order equivalent circuit model on real battery datasets, under moderate operating conditions. The adaptive sampling framework extends naturally to higher-order models with appropriate recalibration for more demanding scenarios.

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation is vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Dut to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model.