Inflammation is important in the development of bronchopulmonary dysplasia (BPD). Polymorphonuclear cells and macrophages and proinflammatory cytokines/chemokines denote early inflammation in clinical scenarios such as in utero inflammation with chorioamnionitis or initial lung injury associated with respiratory distress syndrome or ventilator-induced lung injury. The persistence and non-resolution of lung inflammation contributes greatly to BPD, including altering the lung's ability to repair, contributing to fibrosis, and inhibiting secondary septation, alveolarization, and normal vascular development. Further understanding of the role of inflammation in the pathogenesis of BPD, in particular, during the chronic inflammatory period, offers us the opportunity to develop inflammation-related prevention and treatment strategies of this disease that has long-standing consequences for very premature infants.

The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early stages of diagnostic design. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests.With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented. Keywords-Lithium-ion battery; Fault detection and isolation; Structural analysis; Statistical inference residualevaluation. IntroductionWith the development of Electric Vehicles (EVs) in recent years, the lithium-ion batteries, as the energy storage device, are gaining more and more attentions due to its inherent benefits of high energy and power density, low self-discharge rate and long lifespan [1]. To guarantee the battery safety, performance, reliability and life, a welldesigned battery management system (BMS) is required to perform the functions such as thermal management to ensure the batteries work at optimal average temperature and reduced gradient, State-of-Charge (SoC) and State-of-Health (SoH) estimations, as well as over-current, over-/under-voltage protections [2]- [6]. These critical functions are mainly dependent on the embedded current, voltage and temperature sensor measurements.

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This paper presents a systematic methodology based on structural analysis and sequential residual generators to design a Fault Detection and Isolation (FDI) scheme for nonlinear battery systems. The faults to be diagnosed are highlighted using a detailed hazard analysis conducted for battery systems. The developed methodology includes four steps: candidate residual generators generation, residual generators selection, diagnostic test construction and fault isolation. State transformation is employed to make the residuals realizable. The simulation results show that the proposed FDI scheme successfully detects and isolates the faults injected in the battery cell with cooling system at different times. In addition, there are no false or missed detections of the faults.

Diesel engines are widely used for the propulsion of marine vehicles and are exposed to uncertain working environment. In this paper, a robust nonlinear controller is proposed for a marine diesel engine that employees sliding mode control theory. The robust controller aims to maintain the desired diesel engine speed performance under harsh sea environment. The sliding surface has been carefully chosen that minimises the error in both angular velocity and acceleration. Robust control algorithm development and its tuning are also discussed. The performance of the proposed nonlinear robust controller is investigated thoroughly and is compared with a classical Proportional-Integral-Differentiation controller with integral windup scheme. Simulation results show that the proposed super-twisting-algorithm-based sliding mode controller can effectively improve the speed performance of the marine diesel engine in transient and steady operating conditions.