Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Support vector machine in machine condition monitoring and fault diagnosis

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

A review of data-driven building energy consumption prediction studies

Text Mining has become an important research area. Text Mining is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. In this paper, a Survey of Text Mining techniques and applications have been s presented.

/pdf/a-survey-of-text-mining-techniques-and-applications-3dtpeqf8jg.pdf

A Survey of Text Mining Techniques and Applications

A method for managing a vehicle is disclosed wherein the method includes receiving an identification (ID) code associated with at least one user. The method further includes corresponding the ID code to at least one user profile following verification of the ID code and executing a vehicle setting associated with the at least one user profile. An apparatus for remotely managing a vehicle setting is further disclosed. The apparatus includes memory for storing at least one user profile. The apparatus further includes a processor coupled to the memory, the processor configured to receive an identification (ID) code corresponding to the at least one user profile. Furthermore, the processor is in communication with a vehicle processing system operable to execute a vehicle setting associated with the at least one user profile.

Methods and Systems for Remotely Managing A Vehicle

Chillers constitute a significant portion of energy consumption equipment in heating, ventilating and air-conditioning (HVAC) systems. The growing complexity of building systems has become a major challenge for field technicians to troubleshoot the problems manually; this calls for automated ldquosmart-service systemsrdquo for performing fault detection and diagnosis (FDD). The focus of this paper is to develop a generic FDD scheme for centrifugal chillers and also to develop a nominal data-driven (ldquoblack-boxrdquo) model of the chiller that can predict the system response under new loading conditions. In this vein, support vector machines, principal component analysis, and partial least squares are the candidate fault classification techniques in our approach. We present a genetic algorithm-based approach to select a sensor suite for maximum diagnosabilty and also evaluated the performance of selected classification procedures with the optimized sensor suite. The responses of these selected sensors are predicted under new loading conditions using the nominal model developed via the black-box modeling approach. We used the benchmark data on a 90-t real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers, to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables of the chiller under 27 different modes of operation during nominal and eight faulty conditions with different severities.

Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers

Battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, controlling the charge, determining the state of charge (SOC), the state of health (SOH), and the remaining useful life (RUL) of the battery, cell balancing, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates three critical characteristics of the battery (SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on an equivalent circuit battery model consisting of resistors, capacitor, and Warburg impedance. The resistors usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. We investigate the use of support vector machines to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. The circuit parameters are estimated from electrochemical impedance spectroscopy (EIS) test data using nonlinear least squares estimation techniques. Predictions of remaining useful life (RUL) of the battery are obtained by support vector regression of the power fade and capacity fade estimates.

Automotive battery management systems

A method of remotely managing vehicle settings wherein the method includes receiving an identification (ID) code associated with at least one user. The method further includes corresponding the ID code to at least one user profile following verification of the ID code and executing a vehicle setting associated with the at least one user profile. An apparatus for remotely managing vehicle settings is further disclosed. The apparatus includes memory for storing at least one user profile. The apparatus further includes a processor coupled to the memory, the processor configured to receive an identification (ID) code corresponding to the at least one user profile. Furthermore, the processor is in communication with a vehicle processing system operable to execute a vehicle setting associated with the at least one user profile.

Remote management of vehicle settings

Imperfect test outcomes, due to factors such as unreliable sensors, electromagnetic interference, and environmental conditions, manifest themselves as missed detections and false alarms. This paper develops near-optimal algorithms for dynamic multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. The DMFD problem is to determine the most likely evolution of component states, the one that best explains the observed test outcomes. Here, we discuss four formulations of the DMFD problem. These include the deterministic situation corresponding to perfectly observed coupled Markov decision processes to several partially observed factorial hidden Markov models ranging from the case where the imperfect test outcomes are functions of tests only to the case where the test outcomes are functions of faults and tests, as well as the case where the false alarms are associated with the nominal (fault free) case only. All these formulations are intractable NP-hard combinatorial optimization problems. Our solution scheme can be viewed as a two-level coordinated solution framework for the DMFD problem. At the top (coordination) level, we update the Lagrange multipliers (coordination variables, dual variables) using the subgradient method. At the bottom level, we use a dynamic programming technique (specifically, the Viterbi decoding or Max-sum algorithm) to solve each of the subproblems, one for each component state sequence. The key advantage of our approach is that it provides an approximate duality gap, which is a measure of the suboptimality of the DMFD solution. Computational results on real-world problems are presented. A detailed performance analysis of the proposed algorithm is also discussed.

/pdf/dynamic-multiple-fault-diagnosis-mathematical-formulations-2gr5a984mr.pdf

S.M. Namburu

Papers

Methods and Systems for Remotely Managing A Vehicle

Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers

Automotive battery management systems

Remote management of vehicle settings

Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques