Nonlinear Stochastic Filtering for Online State of Charge and Remaining Useful Life Estimation of Lithium-ion Battery

Battery state monitoring is one of the key techniques in Battery Management System (BMS). Accurate estimation can help to improve the system performance and to prolong the battery lifetime. The main challenges for the state online estimation of Li-ion batteries are the flat characteristic of open circuit voltage (OCV) with the function of the state of charge. Hence, the focus of this thesis study is to estimation of the state of charge (SOC) of Li-ion with high accuracy, more robustness. A 2nd order RC equivalent circuit model is adapted to battery model for simulation, mathematical model analysis, and dynamics characteristic of battery study. Model parameters are identified with MATLAB battery model simulation. Although with more lumped RC loaders, the model is more accurate, high computation with a higher nonlinear function of output will be. So, a discrete state space model for the battery is developed. For a complex battery model with strong nonlinearity, Sequential Monte Carlo (SMC) method can be utilized to perform the on-line SOC estimation. An SMC integrates the Bayesian learning methods with sequential importance sampling. SMC approximate the posterior density function by a set of particles with associated weights, which is developed in MATLAB environment to estimate on-line SOC. A comparison is presented with Kalman Filtering and Extended Kalman Filtering to validated estimation results with SMC. Finally, the comparison results provide that SMC method is more accurate and robust then KF and EKF. Accurately prediction of Remaining Useful Life of Li-ion batteries is necessary to reliable system operation and monitoring the BMS. An empirical model for capacity degradation has been developed based on experimentally obtained capacity fade data. A nonlinear, non-Gaussian state space model is developed for empirical model. The obtained empirical model used in Sequential Monte Carlo (SMC) framework is to update the on-line state and model parameters to make a prediction of remaining useful life of a Li-ion battery at various lifecycle

Health Monitoring of Nonlinear Systems with Application to Gas Turbine Engines

Health monitoring and prognosis of nonlinear systems is mainly concerned with system health tracking and its evolution prediction to future time horizons. Estimation and prediction schemes constitute as principal components of any health monitoring framework. In this thesis, the main focus is on development of novel health monitoring techniques for nonlinear dynamical systems by utilizing model-based and hybrid prognosis and health monitoring approaches. First, given the fact that particle filters (PF) are known as a powerful tool for performing state and parameter estimation of nonlinear dynamical systems, a novel dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the prediction error (PE) concept and the particle filtering scheme. Estimation of system parameters along with the states generate an updated model that can be used for a long-term prediction problem. Next, an improved particle filtering-based methodology is developed to address the prediction step within the developed health monitoring framework. In this method, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizons. Particles are then propagated to future time instants according to a resampling algorithm in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models (DLM) for development of an observation forecasting scheme. A novel hybrid architecture is then proposed to develop prognosis and health monitoring methodologies for nonlinear systems by integration of model-based and computationally intelligent-based techniques. Our proposed hybrid health monitoring methodology is constructed based on a framework that is not dependent on the structure of the neural network model utilized in the implementation of the observation forecasting scheme. Moreover, changing the neural network model structure in this framework does not significantly affect the prediction accuracy of the entire health prediction algorithm. Finally, a method for formulation of health monitoring problems of dynamical systems through a two-time scale decomposition is introduced. For this methodology the system dynamical equations as well as the affected damage model, are investigated in the two-time scale system health estimation and prediction steps. A two-time scale filtering approach is developed based on the ensemble Kalman filtering (EnKF) methodology by taking advantage of the model reduction concept. The performance of the proposed two-time scale ensemble Kalman filters is shown to be more accurate and less computationally intensive as compared to the well-known particle filtering approach for this class of nonlinear systems. All of our developed methods have been applied for health monitoring and prognosis of a gas turbine engine when it is affected by various degradation damages. Extensive comparative studies are also conducted to validate and demonstrate the advantages and capabilities of our proposed frameworks and methodologies

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Energy: A special bibliography with indexes, April 1974

This literature survey of special energy and energy related documents lists 1708 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1968, and December 31, 1973. Citations from International Aerospace Abstracts (IAA) and Scientific and Technical Aerospace Reports (STAR) are grouped according to the following subject categories: energy systems; solar energy; primary energy sources; secondary energy sources; energy conversion; energy transport, transmission, and distribution; and energy storage. The index section includes the subject, personal author, corporate source, contract, report, and accession indexes

Energy: A continuing bibliography with indexes, supplement 16, January 1978

This bibliography lists 1287 reports, articles, and other documents introduced into the NASA scientific and technical information system from October 1, 1977 through December 31, 1977