Accurate state of charge (SOC) estimation is essential for the battery management system (BMS). In engineering, inappropriate selection of equivalent circuit model (ECM) and model parameters is common for lithium-ion batteries. It can result in systematic errors (i.e., modeling errors) in the state-space equation, thus affecting the SOC estimation accuracy. Aiming at that, this paper proposes a self-calibration method to enhance SOC estimation. In the method, a novel state-space equation containing an unknown systematic error term is developed based on the Thevenin model. A self-calibration unscented Kalman filter (SC-UKF) algorithm is then introduced for recursive SOC estimation. The algorithm can automatically recognize and calibrate the unknown systematic error in the state equation, while also reducing the random noise effect through data fusion with the measurement equation. Test results demonstrate that the method can effectively correct the Thevenin modeling error and improve SOC estimation accuracy. Furthermore, the proposed method is computationally simple and convenient for engineering applications without increasing model complexity.INDEX TERMS lithium-ion battery, state of charge, equivalent circuit model, systematic error, selfcalibration, unscented Kalman filter.

Rolling bearings are critical rotating components in aerospace and high-speed trains. The service loads of rolling bearings are generally different from each other, causing significant differences in their damage degrees and reliable life potential. To ensure bearing reliability during service, a reliable life consumption assessment and individual life monitoring method is proposed. Firstly, a small-sample reliable life evaluation method is established based on an accelerated life test conducted at a constant load or load block spectrum before delivery, by which the lower confidence limit of the reliable life of the rolling bearing under any service load can be calculated with the required confidence and reliability. Then, the confidence limits of the reliable life consumption percentage and remaining reliable life percentage for each rolling bearing can be evaluated in real time according to their online monitoring loads and Miner’s law. By this means, individual life monitoring and online life management can be conveniently realized. Compared with traditional bearing life management methods, which do not consider service load differences, this method can more effectively ensure service safety and simultaneously maximize the life potential of bearings.

Safe life extension work is demanded on an aircraft’s main landing gear (MLG) when the outfield MLG reaches the predetermined safe life. Traditional methods generally require costly and time-consuming fatigue tests, whereas they ignore the outfield data containing abundant life information. Thus, this paper proposes a novel life extension method based on statistical inference of test and outfield life data. In this method, the MLG’s fatigue life is assumed to follow a right-skewed lognormal distribution with an asymmetric probability density function. In addition, the MLG’s new safe life can be inferred through the Bayesian approach in which the test life data and outfield life data are used for prior information acquisition and Bayesian update, respectively. The results indicated that the MLG’s safe life was significantly extended, illustrating the effectiveness of the proposed method. Numerous simulations also demonstrated that the extended safe life can meet the requirements of reliability and confidence and thus is applicable in engineering practice.