Electrochemical model-based estimation techniques have attracted increasing attention in the past decade due to their inherent insight about the internal battery operating conditions and limits while being able to monitor important li-ion battery states. The applicability of these methods is, however, limited due to the implementation complexity of their underlying models. In order to facilitate online implementation while maintaining the physical insight, a reduced-order electrochemical model is used in this work. This model, which is based on the commonly-used single particle model, is further improved by incorporating the electrolyte-phase potential. Furthermore, an output-injection observer, suitable for online implementation, is proposed to estimate SOC. The observer convergence is proved analytically using Lyapunov theory. Although the proposed observer shows great performance at low C rates, its accuracy deteriorates at high C-rates. To overcome this issue and achieve accurate SOC estimates for such charge/discharge rates, an adaptation algorithm is augmented to the observer. The adaptation algorithm, which can be implemented online, is used to compensate for model uncertainties, especially at higher C rates. Finally, simulation results based on a full-order electrochemical model are used to validate the observer performance and effectiveness.
- Dynamic Systems and Control Division
Electrochemical Model-Based Adaptive Estimation of Li-Ion Battery State of Charge
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Lotfi, N, Landers, RG, Li, J, & Park, J. "Electrochemical Model-Based Adaptive Estimation of Li-Ion Battery State of Charge." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems. Columbus, Ohio, USA. October 28–30, 2015. V001T13A008. ASME. https://doi.org/10.1115/DSCC2015-9918
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