Mutasim A. Salman

Fuzzy logic control for parallel hybrid vehicles

In this paper, a fuzzy logic controller is developed for hybrid vehicles with parallel configuration. Using the driver command, the state of charge of the energy storage, and the motor/generator speed, a set of rules have been developed, in a fuzzy controller, to effectively determine the split between the two powerplants: electric motor and internal combustion engine. The underlying theme of the fuzzy rules is to optimize the operational efficiency of all components, considered as one system. Simulation results were used to assess the performance of the controller. A forward-looking hybrid vehicle model was used for implementation and simulation of the controller. Potential fuel economy improvement is shown by using fuzzy logic, relative to other controllers, which maximize only the efficiency of the engine.

Energy management strategies for parallel hybrid vehicles using fuzzy logic

This paper presents a fuzzy-logic-based energy management and power control strategy for parallel hybrid vehicles (PHV). The main objective is to optimize the fuel economy of the PHV, by optimizing the operational efficiency of all its components. The controller optimizes the power output of the electric motor/generator and the internal combustion engine by using vehicle speed, driver commands from accelerator and braking pedals, state of charge (SOC) of the battery, and the electric motor/generator speed. Separate controllers optimize braking and gear shifting. Simulation results show potential fuel economy improvement relative to other strategies that only maximize the efficiency of the combustion engine.

Control strategies for parallel hybrid vehicles

Presents a fuzzy logic based energy management and control strategy for parallel hybrid vehicles. Using the driver commands, the state of charge of the battery, and the motor/generator speed, a set of fuzzy logic control rules has been developed, to effectively split the power between the two powerplants: electric motor and internal combustion engine. The underlying theme of the fuzzy rules is to optimize the operational efficiency of all components, considered as one system. Simulation results are used to assess the performance of the controller. A forward-looking hybrid vehicle simulation model is used to implement the control strategies. Potential fuel economy improvement has been shown by using fuzzy logic, relative to other strategies, which maximize only the efficiency of the internal combustion engine.

Emissions and fuel economy trade-off for hybrid vehicles using fuzzy logic

In this paper, a generalized fuzzy logic controller (FLC) is used to optimize the fuel economy and reduce the emissions of hybrid vehicles with parallel configuration. Using the driver input, the state of charge (SOC) of the energy storage, the motor/generator speed, the current gear ratio and vehicle speed, a set of 44 roles have been developed, in a fuzzy controller, to effectively determine the power split between the electric machine and the internal combustion engine (ICE). The underlying theme of the fuzzy controller is to optimize the fuel flow and reduce NO x emission. The parameters in the fuzzy rules can be adjusted to trade-off the fuel economy and the NO x emission of the vehicle. Simulation results are used to assess the performance of the controller. A forward-looking hybrid vehicle simulation model is used to implement the control strategies. By using fuzzy logic, trade-off between fuel economy and emission improvement has been shown.

Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models

Diagnosis classifies the present state of operation of the equipment, and prognosis predicts the next state of operation and its remaining useful life. In this paper, a prognosis method for the gear faults in dc machines is presented. The proposed method uses the time-frequency features extracted from the motor current as machine health indicators and predicts the future state of fault severity using hidden Markov models (HMMs). Parameter training of HMMs generally needs huge historical data, which are often not available in the case of electrical machines. Methods for computing the parameters from limited data are presented. The proposed prognosis method uses matching pursuit decomposition for estimating state-transition probabilities and experimental observations for computing state-dependent observation probability distributions. The proposed method is illustrated by examples using data collected from the experimental setup.

Remaining useful life prediction of individual units subject to hard failure

To develop a cost-effective condition-based maintenance strategy, accurate prediction of the Remaining Useful Life (RUL) is the key. It is known that many failure mechanisms in engineering can be traced back to some underlying degradation processes. This article proposes a two-stage prognostic framework for individual units subject to hard failure, based on joint modeling of degradation signals and time-to-event data. The proposed algorithm features a low computational load, online prediction, and dynamic updating. Its application to automotive battery RUL prediction is discussed in this article as an example. The effectiveness of the proposed method is demonstrated through a simulation study and real data.

Evaluation and Comparison of Mixed Effects Model Based Prognosis for Hard Failure

Failure prognosis plays an important role in effective condition-based maintenance. In this paper, we evaluate and compare the hard failure prediction accuracy of three types of prognostic methods that are based on mixed effect models: the degradation-signal based prognostic model with deterministic threshold (DSPM), with random threshold (RDSPM), and the joint prognostic model (JPM). In this work, the failure prediction performance is measured by the mean squared prediction error, and the power of prediction. We have analyzed characteristics of the three methods, and provided insights to the comparison results through both analytical study and extensive simulation. In addition, a case study using real data has been conducted to illustrate the comparison results as well.

Reduced order design of active suspension control

The presence of fast and slow modes in suspension systems is utilized in the design of two reduced-order active suspension-control strategies. The first strategy is obtained by combining the solutions of slow and fast control subproblems. The second strategy is based on a two-level hierarchical control design. In spite of their simplified structure, simulation results indicate that their performance is comparable to that of the full-state-feedback design.<<ETX>>

Model-based Diagnosis of an Automotive Electric Power Generation and Storage System

This paper presents mathematical models, design and experimental validation, and calibration of a model-based diagnostic algorithm for an electric-power generation and storage automotive system, including a battery and an alternator with a rectifier and a voltage regulator. Mathematical models of these subsystems are derived, based on the physics of processes involved as characterized by time-varying nonlinear ordinary differential equations. The diagnostic problem focuses on detection and isolation of a specific set of alternator faults, including belt slipping, rectifier fault, and voltage regulator fault. The proposed diagnostic approach is based on the generation of residuals obtained using system models and comparing predicted and measured value of selected variables, including alternator output current, field voltage, and battery voltage. An equivalent input-output alternator model, which is used in the diagnostic scheme, is also formulated and parameterized. The test bench used for calibration of thresholds of the diagnostic algorithm and overall validation process are discussed. The effectiveness of the fault diagnosis algorithm and threshold selection is experimentally demonstrated.

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.