Setu Madhavi Namburu

Automotive battery management systems

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.

Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques

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.

Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems

Faulty automotive systems significantly degrade the performance and efficiency of vehicles, and oftentimes are the major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective fault diagnosis in automotive systems. Previously, we developed a data-driven approach using a data reduction technique, coupled with a variety of classifiers, for fault diagnosis in automotive systems. In this paper, we consider the problem of fusing classifier decisions to reduce diagnostic errors. Specifically, we develop three novel classifier fusion approaches: class-specific Bayesian fusion, joint optimization of fusion center and of individual classifiers, and dynamic fusion. We evaluate the efficacies of these fusion approaches on an automotive engine data. The results demonstrate that dynamic fusion and joint optimization, and class-specific Bayesian fusion outperform traditional fusion approaches. We also show that learning the parameters of individual classifiers as part of the fusion architecture can provide better classification performance.

Data Reduction Techniques for Intelligent Fault Diagnosis in Automotive Systems

Faults in automotive systems significantly degrade the performance and efficiency of vehicles, and often times are the major causes of vehicle break-down leading to large expenditure for repair and maintenance. An intelligent fault diagnosis system can ensure uninterrupted and reliable operation of vehicular systems, and aid in vehicle health monitoring. Due to cost constraints, the current electronic control units (ECUs) for control and diagnosis have 1-2 MB of memory and 24 -50 MHz of processor speed. Therefore, intelligent data reduction techniques and partitioning methodology are needed for effective fault diagnosis in automotive systems. In this paper, we propose a data- driven approach using a data reduction technique, coupled with a variety of classifiers, for an automotive engine system. Adaptive boosting (AdaBoost) is employed to improve the classifier performance. Our proposed techniques can be used for any vehicle systems without the need to tune the classification algorithms for a specific vehicle model. Our proposed fault diagnosis scheme results in significant reduction in data size (25.6 MBrarr12.8 KB) without loss of accuracy in classification.

An Optimization-Based Method for Dynamic Multiple Fault Diagnosis Problem

Imperfect test outcomes, due to factors such as unreliable sensors, electromagnetic interference, and environmental conditions, manifest themselves as missed detections and false alarms. The main objective of our research on on-board diagnostic inference is to develop near-optimal algorithms for dynamic multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. Our problem is to determine the most likely evolution of fault states, the one that best explains the observed test outcomes. Here, we develop a primal-dual algorithm for solving the DMFD problem by combining Lagrangian relaxation and the Viterbi decoding algorithm in an iterative way. A novel feature of our approach is that the approximate duality gap provides a measure of suboptimality of the DMFD solution.

Dynamic fusion of classifiers for fault diagnosis

This paper considers the problem of temporally fusing classifier outputs to improve the overall diagnostic classification accuracy in safety-critical systems. Here, we discuss dynamic fusion of classifiers which is a special case of the dynamic multiple fault diagnosis (DMFD) problem [1]-[3]. The DMFD problem is formulated as a maximum a posteriori (MAP) configuration problem in tri-partite graphical models, which is NP-hard. A primal-dual optimization framework is applied to solve the MAP problem. Our process for dynamic fusion consists of four key steps: (1) data preprocessing such as noise suppression, data reduction and feature selection using data-driven techniques, (2) error correcting codes to transform the multiclass data into binary classification, (3) fault detection using pattern recognition techniques (support vector machines in this paper), and (4) dynamic fusion of classifiers output labels over time using the DMFD algorithm. An automobile engine data set, simulated under various fault conditions [4], was used to illustrate the fusion process. The results demonstrate that an ensemble of classifiers, when fused over time, reduces the classification error as compared to a single classifier and static fusion of classifiers trained over the entire batch of data. The results for sliding window dynamic fusion are also provided.

Novel classifier fusion approahces for fault diagnosis in automotive systems

Faulty automotive systems significantly degrade the performance and efficiency of vehicles and are often major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective fault diagnosis in automotive systems. Previously, we developed a data-driven approach using a data-reduction technique, coupled with a variety of classifiers, for fault diagnosis in automotive systems. In this paper, we consider the problem of fusing classifier decisions to reduce diagnostic errors. Specifically, we develop three novel classifier fusion approaches: 1. class-specific Bayesian fusion; 2. joint optimization of the fusion center and individual classifiers; and 3. dynamic fusion. We evaluate the efficacies of these fusion approaches on an automotive engine data. The results demonstrate that the proposed fusion techniques outperform traditional fusion approaches. We also show that learning the parameters of individual classifiers as part of the fusion architecture can provide better classification performance.

Dynamic Set-Covering for Real-Time Multiple Fault Diagnosis

The set-covering problem has been widely used to model many real world applications. In this paper, we formulate a time-dependent set covering problem, viz., dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. We motivate the DSC problem from the viewpoint of a fault diagnosis problem, wherein multiple faults may evolve over time and the fault-test dependencies are deterministic. The objective of the DSC problem is to evaluate the most likely evolution of the minimal set of columns (component fault states) covering the rows (failed tests) of the DSC constraint matrix at a minimum cost or maximum reward. The DSC problem is an NP-hard and intractable due to the coupling among the rows and columns via the constraint matrix, and the temporal dependence of columns over time. By relaxing the constraints using Lagrange multipliers, the DSC problem can be decoupled into several subproblems; each corresponding to a column of the constraint matrix. Each subproblem is solved using the Viterbi decoding algorithm, and a primal feasible solution is constructed by modifying the Viterbi solutions via a heuristic. The Lagrange multipliers are updated using the subgradient method. The proposed primal-dual optimization framework provides a measure of suboptimality via approximate duality gap. Capabilities of the proposed algorithm are demonstrated by way of its application to the dynamic versions of the set-covering problems from the Beasleys OR-library and to real-world diagnostic models.

Diagnostic Ambiguity and Parameter Optimization in Classifier Fusion: Application to Gas Turbine Engine Data

Diagnostic ambiguity caused by limited observability of sensors is a significant challenge in real-world diagnostic applications, such as gas turbine engines. Traditional data-driven clustering, classification and fusion techniques based on single fault (class) assumption result in large diagnostic errors. Thus, we solve this problem by diagnosing the inherent ambiguity as multiple faults. The proposed primal-dual optimization framework for classifier fusion improves the correct fault isolation rate, while minimizing the false alarm rate. The key points of primal-dual optimization framework, viz. multiple fault diagnosis and classifier parameter optimization, are extended to the error correcting output code (ECOC)-based weighted voting method and were found to significantly increase correct fault isolation rate compared to the single class assumption at the cost of false alarms. The primal-dual optimization framework also performed better than any traditional fusion technique when it was forced to give a single fault decision; this is due to the fault clustering effect made possible by the dual solution of the multiple fault diagnosis problems.