Distributed state-of-charge estimation for lithium-ion batteries with random sensor failure under dynamic event-triggering protocol

• Published in: Information fusion Vol. 95; pp. 293 - 305
• Main Authors: Huang, Cong, Ding, Weiping, Gao, Ruifeng, Mei, Peng, Karimi, Hamid Reza
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2023
• Subjects: Distributed filtering; Dynamic event-triggering; Lithium-ion batteries; Random sensor failure; Sensor networks; State-of-charge estimation; Lithium-ion batteries; Sensor networks; State-of-charge estimation; Dynamic event-triggering; Random sensor failure; Distributed filtering
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2023.02.032

The state of charge (SOC) performs as an indicator of the remaining capacity of the Lithium-ion batteries (LBs). An accurate SOC estimation of LB is of great significance for its operation optimization and life extension of the battery. In this article, the issue of distributed SOC estimation is addressed for LBs. To design the distributed filter for SOC estimation, the equivalent circuit model comprised of the resistor–capacitor networks, Warburg element, ohmic resistance, battery current and voltage is established. In order to reflect the properties of the random sensor failure (RSF) well, a set of Bernoulli-distributed sequences with known probabilities is introduced. The communication resources over the wireless networks are usually limited, for the purpose of saving communication resources, the dynamic event-triggering mechanism (DETM) is adopted to regulate transmission of the signals. The main objective of this article is to design a distributed SOC estimation approach for LBs subject to RSF under DETM over the sensor networks. The upper bound of the estimation error covariance is first ensured and then such upper bound is minimized by parameterizing the estimator gain. In addition, by virtue of the matrix simplification technique, the issue of sensor network topology’s sparseness is effectively tackled. At last, experimental examples are employed to validate the feasibility of the developed distributed SOC estimation algorithm. •The dynamic event-triggered mechanism is considered in distributed SOC estimation.•Parameter uncertainty and sensor failure for lithium-ion batteries are considered.•The upper bound is derived for estimation error covariance and then minimized.•The developed distributed estimation algorithm is suitable for online applications.