Chaitanya Sankavaram

System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics

The 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, thermal management, controlling the charge-discharge, determining the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of the battery, cell balancing, data acquisition, communication with on-board and off-board modules, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on a modified Randles circuit model consisting of resistors, a capacitor, the Warburg impedance for electrochemical impedance spectroscopy test data, and a lumped parameter model for hybrid pulse power characterization test data. The resistors in a Randles circuit model 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. The Randles circuit parameters are estimated using a frequency-selective nonlinear least squares estimation technique, while the lumped parameter model parameters are estimated by the prediction error minimization method. We investigate the use of support vector machines (SVMs) to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. An alternate procedure for estimating the power fade and energy fade from low-current Hybrid Pulse Power characterization (L-HPPC) test data using the lumped parameter battery model has been proposed. Predictions of RUL of the battery are obtained by support vector regression of the power fade and capacity fade estimates. Survival function estimates for reliability analysis of the battery are obtained using a hidden Markov model (HMM) trained using time-dependent estimates of capacity fade and power fade as observations. The proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy.

An integrated health management process for automotive cyber-physical systems

Automobile is one of the most widely distributed cyber-physical systems. Over the last few years, the electronic explosion in automotive vehicles has significantly increased the complexity, heterogeneity and interconnectedness of embedded systems. Although designed to sustain long life, systems degrade in performance due to gradual development of anomalies eventually leading to faults. In addition, system usage and operating conditions (e.g., weather, road surfaces, and environment) may lead to different failure modes that can affect the performance of vehicles. Advanced diagnosis and prognosis technologies are needed to quickly detect and isolate faults in network-embedded automotive systems so that proactive corrective maintenance actions can be taken to avoid failures and improve vehicle availability. This paper discusses an integrated diagnostic and prognostic framework, and applies it to two automotive systems, viz., a Regenerative Braking System (RBS) in hybrid electric vehicles and an Electric Power Generation and Storage (EPGS) system.

A prognostic framework for health management of coupled systems

This paper describes a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic (time-series) data. The approach employs Cox proportional hazards model (Cox PHM) and soft dynamic multiple fault diagnosis algorithm (DMFD) for inferring the degraded state trajectories of components and to estimate their remaining useful life (RUL). This framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (soft test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via a soft DMFD algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The proposed prognostic framework has the potential to be applicable to a wide variety of systems, ranging from automobiles to aerospace systems.

Dynamic Coupled Fault Diagnosis With Propagation and Observation Delays

In this paper, we propose a delay dynamic coupled fault diagnosis (DDCFD) model to deal with the problem of coupled fault diagnosis with fault propagation/transmission delays and observation delays with imperfect test outcomes. The problem is to determine the most likely set of faults and their time evolution that best explains the observed test outcomes over time. It is formulated as a combinatorial optimization problem, which is known to be NP-hard. Since the faults are coupled, the problem does not have a decomposable structure as, for example, in dynamic multiple fault diagnosis, where the coupled faults and delays are not taken into account. Consequently, we propose a partial-sampling method based on annealed maximum a posteriori (MAP) algorithm, a method that combines Markov chain Monte Carlo and simulated annealing, to deal with the coupled-state problem. By reducing the number of samples and by avoiding redundant computations, the computation time of our method is substantially smaller than the regular annealed MAP method with no noticeable impact on diagnostic accuracy. Besides the partial-sampling method, we also propose an algorithm based on block coordinate ascent and the Viterbi algorithm (BCV) to solve the DDCFD problem. It can be considered as an extension of the method used to solve the dynamic coupled fault diagnosis (DCFD) problem. The model and algorithms presented in this paper are tested on a number of simulated systems. The results show that the BCV algorithm has better accuracy but results in large computation time. It is only feasible for problems with small delays. The partial-sampling algorithm has a smaller computation time with an acceptable diagnostic accuracy. It can be used on systems with large delays and complex topological structure.

Data-driven fault diagnosis in a hybrid electric vehicle regenerative braking system

Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. In this paper, we discuss a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The process involves data reduction techniques, exemplified by multi-way partial least squares, multi-way principal component analysis, for implementation in memory-constrained electronic control units and well-known fault classification techniques based on reduced data, such as support vector machines, k-nearest neighbor, partial least squares, principal component analysis and probabilistic neural network, to isolate faults in the braking system. The results demonstrate that highly accurate fault diagnosis is possible with the pattern recognition-based techniques. The process can be employed for fault analysis in a wide variety of systems, ranging from automobiles to buildings to aerospace systems.

Integrated model-based and data-driven fault detection and diagnosis approach for an automotive electric power steering system

Integrity of electric power steering system is vital to vehicle handling and driving performance. Advances in electric power steering (EPS) system have increased complexity in detecting and isolating faults. In this paper, we propose a hybrid model-based and data-driven approach to fault detection and diagnosis (FDD) in an EPS system. We develop a physics-based model of an EPS system, conduct fault injection experiments to derive fault-sensor measurement dependencies, and investigate various FDD schemes to detect and isolate the faults. Finally, we use an SVM regression technique to estimate the severity of faults.

Incremental Classifiers for Data-Driven Fault Diagnosis Applied to Automotive Systems

One of the common ways to perform data-driven fault diagnosis is to employ statistical models, which can classify the data into nominal (healthy) and a fault class or distinguish among different fault classes. The former is termed fault (anomaly) detection, and the latter is termed fault isolation (classification, diagnosis). Traditionally, statistical classifiers are trained using data from faulty and nominal behaviors in a batch mode. However, it is difficult to anticipate, a priori, all the possible ways in which failures can occur, especially when a new vehicle model is introduced. Therefore, it is imperative that diagnostic algorithms adapt to new cases on an ongoing basis. In this paper, a unified methodology to incrementally learn new information from evolving databases is presented. The performance of adaptive (or incremental learning) classification techniques is discussed when: 1) the new data has the same fault classes and same features and 2) the new data has new fault classes, but with the same set of observed features. The proposed methodology is demonstrated on data sets derived from an automotive electronic throttle control subsystem.

Fault Diagnosis in Hybrid Electric Vehicle Regenerative Braking System

Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. This paper presents a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The diagnostic process involves signal processing and statistical techniques for feature extraction, data reduction for implementation in memory-constrained electronic control units, and variety of fault classification methodologies to isolate faults in the regenerative braking system. The results demonstrate that highly accurate fault diagnosis is possible with the classification methodologies. The process can be employed for fault analysis in a wide variety of systems, ranging from automobiles to buildings to aerospace systems.

Fault Diagnosis in the Automotive Electric Power Generation and Storage System (EPGS)

In this paper, we present an initial study to develop fault detection and isolation techniques for the vehicle systems that are controlled by a network of electronic control units (ECUs). The root causes of the faults include hardware components such as actuators, software within the controllers (ECUs), or the interactions between hardware and software, i.e., between controllers and plants. These faults, originating from various interactions and especially between hardware and software, are particularly challenging to address. Our basic strategy is to divide the fault universe of the cyber-physical system in a hierarchical manner, and monitor the critical variables/signals that have impact at different levels of interactions. Diagnostic matrix is established to represent the relationship between the faults and the test outcomes (also known as fault signatures). A factorial hidden Markov model-based inference algorithm, termed dynamic multiple fault diagnosis, is used to infer the root causes based on the observed test outcomes. The proposed diagnostic strategy is validated on an electrical power generation and storage system controlled by two ECUs in an environment with CANoe/MATLAB co-simulation. Eleven faults are injected with the failures originating in actuator hardware, sensor, controller hardware, and software components (sensor faults are not considered in this paper). The simulation results show that the proposed diagnostic strategy is effective in addressing the interaction-caused faults.

Multiple model moving horizon estimation approach to prognostics in coupled systems

The key objectives of this paper are to analyze and implement a novel moving horizon model predictive estimation scheme based on constrained nonlinear optimization techniques for inferring the survival functions and residual useful life (RUL) of components in coupled systems. The approach employs a data-driven prognostics framework that combines failure time data, static and dynamic (time-series) parametric data, and the Multiple Model Moving Horizon Estimation (MM-MHE) algorithm for predicting the survival functions of components based on their usage profiles. Validation of the approach has been provided based on data from an electronic throttle control (ETC) system. The proposed prognostic approach is modular and has the potential to be applicable to a wide variety of systems, ranging from automobiles to aerospace.