Reduced-Order Electrochemical Model-Based SOC Observer with Output Model Uncertainty Estimation
As an integral part of energy storage systems, Li-ion batteries require extensive management to guarantee their safe and efficient operation. Estimation of the remaining energy capability of the battery, usually expressed in terms of state of charge (SOC), plays an important role in any battery-powered application. Electrochemical model-based estimation techniques have proven very attractive for this purpose due to the additional information they provide regarding the internal battery operating conditions. A modified reduced-order model based on the single particle approximation of the electrochemical model, suitable for the real-time implementation of SOC estimation, is employed in this paper. This model, while maintaining some of the physical insights about the battery operation, provides a basis for an output-injection observer design to estimate the SOC. Output model uncertainties, originating primarily from the electrolyte-phase potential difference approximation and encountered mainly at higher discharge rates, are handled by incorporating an adaptation algorithm in the observer. Therefore, the proposed method, while being suitable for online implementation, provides an electrochemical model-based solution for battery SOC estimation over a wide range of operations. System stability and the robustness of the estimates given measurement noise are proved analytically using Lyapunov stability. Finally, accurate performance of the proposed SOC estimation technique is illustrated using simulation data obtained from a full-order electrochemical model of a lithium manganese oxide battery.
N. Lotfi et al., "Reduced-Order Electrochemical Model-Based SOC Observer with Output Model Uncertainty Estimation," IEEE Transactions on Control Systems Technology, vol. 25, no. 4, pp. 1217-1230, Institute of Electrical and Electronics Engineers (IEEE), Sep 2016.
The definitive version is available at https://doi.org/10.1109/TCST.2016.2598764
Mechanical and Aerospace Engineering
Center for High Performance Computing Research
Article - Journal
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