Electrochemical Model-Based Adaptive Estimation of Li-Ion Battery State of Charge
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.
N. Lotfi et al., "Electrochemical Model-Based Adaptive Estimation of Li-Ion Battery State of Charge," Proceedings of the ASME 2015 Dynamic Systems and Control Conference (2015, Columbus, OH), vol. 1, American Society of Mechanical Engineers (ASME), Oct 2015.
The definitive version is available at https://doi.org/10.1115/DSCC2015-9918
ASME 2015 Dynamic Systems and Control Conference, DSCC 2015 (2015: Oct. 28-30, Columbus, OH)
Mechanical and Aerospace Engineering
Keywords and Phrases
Adaptive control systems; Aerospace applications; Automobile engines; Charging (batteries); Electric batteries; Electric machine control; Electric power system control; Electrolytes; Engines; Hybrid vehicles; Intelligent robots; Intelligent systems; Internal combustion engines; Lithium-ion batteries; Optimization; Robotics; Secondary batteries; Uncertainty analysis; Wind power, Adaptation algorithms; Adaptive estimation; Electrochemical modeling; Implementation complexity; Model uncertainties; Observer performance; Online implementation; Single-particle model, Battery management systems
International Standard Book Number (ISBN)
Article - Conference proceedings
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