Showing papers by "Qadeer Ahmed published in 2016"

Review in thermal effects on the performance of electric motors

Abstract: Thermal effects play an important role on the performance of electric motors for the applications in traction/propulsion systems. In these type of applications, there is a feedback loop as well as transients are involved. The torque speed characteristics of the electric motors are dependent upon the motor parameters such as armature/rotor resistance, stator resistance etc. The parameters are significantly affected by the temperature because change in temperature is very high in propulsion systems. As a result, the performance of electric motors used in propulsion systems is degraded. In this paper, the thermal effects on electric motors are combined at one place which is not done before. This will be quite helpful for the researchers of this area to study the thermal effects on the performance of electric motors collectively.

Design and Evaluation of Model-Based Health Monitoring Scheme for Automated Manual Transmission

Abstract: Health monitoring of automated manual transmission (AMT) in modern vehicles can play a critical role to avoid its malfunctions and ensure vehicle functional safety. In order to meet this demand, this paper presents a model-based fault detection and identification (FDI) scheme for AMT. After developing the fault model of AMT, structural analysis (SA)-based fault detectability and isolability is realized with the available set of sensors, prior to design and development of residuals. The residuals are generated by employing the theory of SA, where the concepts of analytical redundant relationship (ARR) are utilized to make residuals stable and robust. Finally, the proposed FDI scheme is successfully evaluated to detect and isolate the sensor faults in EcoCAR2 AMT.

Estimation of thermally de-rated torque of an HEV drive using robust LPV observer

Abstract: A robust LPV observer has been designed to estimate the thermally de-rated torque of an hybrid electric vehicle (HEV) induction machine drive. The torque performance of conventional field-oriented control (FOC) deteriorates under the vast uncertainties in rotor and stator resistance due to the temperature variations during the HEV operation. Similar worsening of the response is expected due to the wide variations in rotor speed in HEV application. To cater for these uncertain scenarios, a robust observer is designed to estimate the de-rated torque performance so that the overall performance of HEV drive system can be improved. The robust observer is based on the linear parameter varying (LPV) technique. The stability of proposed robust LPV observer is also established. The proposed algorithm has been tested in shortened FUDS test cycle for HEV electric power-train. The nonlinear simulation results confirm the observer capability to successfully estimate the de-rated torque in the HEV electric drive in both theoretical and practical scenarios.

Effect of variation in rotor resistance on the dynamic performance of induction motor

Abstract: The paper presents a dynamic d-q model of the three-phase induction motor in the stationary reference frame using MATLAB Simulink environment. An intuitive and easy to use model is presented that clearly elaborates the steps regarding revolving reference frames. It gives a complete access to the monitoring of parameters of induction motor for verification and control purposes. The second part of the paper comprises of the effect of variation in rotor resistance on the dynamic performance of induction motor using the same model. Extensive nonlinear simulation results are incorporated that clearly elaborates the change in the dynamic performance of induction motor by changing the rotor resistance which becomes dominant due to temperature rise in motor.

Control-Oriented Model of Atkinson Cycle Engine with Variable Intake Valve Actuation

Abstract: With the advancement in the innovated technologies, optimum efficiency of spark ignition (SI) engine can be accomplished during the entire engine operating range, particularly at part load. In this research, a novel control-oriented extended mean value engine model (EMVEM) of the Atkinson cycle engine is proposed, wherein the Atkinson cycle, variable valve timing (VVT), overexpansion, and variable compression ratio (VCR) characteristics are incorporated. For this purpose, an intake valve timing (IVT) parameter is introduced, which has a vital role in modeling the inclusive dynamics of the system and to deal with engine performance degrading aspects. The proposed model is validated with the experimental data of a VVT engine, obtained from literature, to ensure that the proposed model has the capability to capture the dynamics of the Atkinson cycle engine, and engine load can be controlled by IVT parameter, instead of the conventional throttle. The potential benefits of late intake valve closing (LIVC) tactic and copious integrated characteristics are appreciated as well. Furthermore, simulation results of the developed model primarily indicate the reduction in the engine part load losses and enhancement in thermal efficiency due to overexpansion, which has a great significance in the enhancement of the performance, fuel economy, and emissions reduction. Besides, the constraints on LIVC and overexpansion become evident.

Pack-level current-split estimation for health monitoring in Li-ion batteries

Abstract: Due to the complicated structural hierarchy and electrochemical processes, lithium-ion battery packs are often monitored by numerous number of sensors. The performance of a pack is highly dependent on the health of these sensors. A faulty current sensor in particular, may affect the estimation accuracy of the state-of-charge and state-of-health. This may cause the battery to suffer from charging and aging issues. Therefore, a scheme to monitor health of these sensors has been proposed. The first step is to estimate the current-split among parallel connected cells, followed by diagnosing the health of sensors to improve the overall performance of battery at pack-level. A median-expectation based covariance intersection diagnosis approach (MCIA) is proposed. MCIA evaluates the median of a possible set of values by calculating the covariance of the interconnected cell structure to estimate the current-split. Performance evaluations have been conducted by analyzing sets of real-time measurements collected from Li-ion battery pack used in electric vehicles (EV). Results show that the proposed filter accurately estimated the battery parameters in the presence of temporary and permanent faults.

Availability estimation and management for complex processing systems

Abstract: “Availability” is the terminology used in asset intensive industries such as petrochemical and hydrocarbons processing to describe the readiness of equipment, systems or plants to perform their designed functions. It is a measure to suggest a facility’s capability of meeting targeted production in a safe working environment. Availability is also vital as it encompasses reliability and maintainability, allowing engineers to manage and operate facilities by focusing on one performance indicator. These benefits make availability a very demanding and highly desired area of interest and research for both industry and academia. In this dissertation, new models, approaches and algorithms have been explored to estimate and manage the availability of complex hydrocarbon processing systems. The risk of equipment failure and its effect on availability is vital in the hydrocarbon industry, and is also explored in this research. The importance of availability encouraged companies to invest in this domain by putting efforts and resources to develop novel techniques for system availability enhancement. Most of the work in this area is focused on individual equipment compared to facility or system level availability assessment and management. This research is focused on developing an new systematic methods to estimate system availability. The main focus areas in this research are to address availability estimation and management through physical asset management, risk-based availability estimation strategies, availability and safety using a failure assessment framework, and availability enhancement using early equipment fault detection and maintenance scheduling optimization.