Design of an augmented unknown input estimator for the lithium-ion battery state of charge and sensor fault estimation

This research proposes a novel method to enhance the accuracy of state of charge (SoC) estimation in lithium-ion packs by utilising nonlinear battery models in combination with an estimator for the unknown input. It is crucial in optimising performance, ensuring safety, and increasing operational life in situations involving electric vehicles and renewable power systems. A key enhancement of this approach is the inclusion of sensing faults as state variables, allowing simultaneous estimation of both SoC and sensor faults. This improves system reliability by detecting and correcting sensor inaccuracies, ensuring stable battery management. The fault-tolerant design reduces errors and enhances real-world applicability. The methodology presented was validated through experimental tests, demonstrating a significant improvement in battery state estimation. The results verify the advancement in battery management systems and aim to develop efficient and reliable energy storage for diverse uses.