Qadeer Ahmed

Fault Detection and Isolation for Lithium-Ion Battery System Using Structural Analysis and Sequential Residual Generation

This paper presents a systematic methodology based on structural analysis and sequential residual generators to design a Fault Detection and Isolation (FDI) scheme for nonlinear battery systems. The faults to be diagnosed are highlighted using a detailed hazard analysis conducted for battery systems. The developed methodology includes four steps: candidate residual generators generation, residual generators selection, diagnostic test construction and fault isolation. State transformation is employed to make the residuals realizable. The simulation results show that the proposed FDI scheme successfully detects and isolates the faults injected in the battery cell with cooling system at different times. In addition, there are no false or missed detections of the faults.Copyright

Health Monitoring of Li-Ion Battery Systems: A Median Expectation Diagnosis Approach (MEDA)

The operations of Li-ion battery management system (BMS) are highly dependent on installed sensors. Malfunctions in sensors could lead to a deterioration in battery performance. This paper proposed an effective health monitoring scheme using a median expectation-based diagnosis approach (MEDA). MEDA calculates the median of a possible set of values, rather than taking their weighted average as in the case of a standard expected mean operator. Furthermore, a smoother was developed to capture important patterns in the estimation. The resulting filter was first derived using an one-dimensional (1-D) system example, where the iterative convergence of median-based proposed filter was proved. Performance evaluations were subsequently conducted by analyzing real-time measurements collected from Li-ion battery cells used in hybrid electric vehicles (HEV) and plug-in HEVs (PHEV) duty cycles. Results showed that the proposed filter was more effective and less sensitive to small sample size and curves with outliers.

Robust Control of a Closed-Loop Identified System with Parametric/Model Uncertainties and External Disturbances

This paper deals with the closed-loop identification of a two-tank process used in industry. The identified model is then utilized to develop robust controllers i.e. H E; and sliding mode controllers. It is shown that these controllers guarantee a satisfactory performance in the face of both model/parametric uncertainties and external disturbances. The designed controllers have been successfully tested through extensive simulation. In addition, this paper shows that the designed robust controllers far outperform traditional controllers such as P, PI, and PID, in the face of parametric model uncertainties and the effects of external disturbances. The successful use of the designed robust controllers encourages their extension to other physical systems.

Condition monitoring of gasoline engine air intake system using second order sliding modes

This paper presents a novel strategy for the condition monitoring of Air Intake System (AIS) of a gasoline engine. The aim is to detect and identify manifold leakages and throttle valve fault. These two faults directly affect AIS performance that results in poor engine efficiency. Two critical and immeasurable engine parameters i.e., Throttle Discharge Coefficient (CD) and Volumetric Efficiency (ηvol) are employed to monitor aforementioned faults. These parameters are estimated using Mean Value Engine Model (MVEM) and super twisting algorithm based Second Order Sliding Mode Observers (SOSMOs). Nominal values of the estimated parameters are used to generate residuals. These residuals are then evaluated to classify system health. A successful validation of the proposed scheme is conducted to diagnose and classify the health status of On Board Diagnostics version-II compliant commercial vehicle engine. The presented scheme is computationally cheap for online implementation and is readily applicable to cater for production line spread of single make.

Fault Modelling for Hierarchical Fault Diagnosis and Prognosis

Fault modeling, which is the determination of the effects of a fault on a system, is an effective way for conducting failure analysis and fault diagnosis for complex system. One of the major challenges of fault modeling in complex systems is the ability to model the effects of component-level faults on the system. This paper develops a simulation-based methodology for failure analysis through modeling component-level fault effect on the system level, with application to electric vehicle powertrains. To investigate how a component fault such as short circuit in a power switch or open circuit in a motor winding affects the vehicle system, this paper develops a detailed simulator which allows us to see system and subsystem failure behaviors by incorporating fault models in the system. This fault modeling process provides useful knowledge for designing a reliable and robust fault diagnosis and prognosis procedures for electrified powertrains.Copyright

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

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.

Determination of realistic uncertainty bounds for the Stewart Platform with payload dynamics

Robust Control of Stewart Platform has been successfully demonstrated by various authors [8-12]. The work done so far is based on the assumption that the bounds on uncertainty are known and the chosen reachability gains are greater than these bounds. This assumption can only be justified if those bounds could be quantified, which is not the case in the existing approaches. The problem gets severe when the controller has to compete against the variations in payload, especially when the payload is asymmetric. This paper addresses such uncertainties. The novelty of the paper lies in the extension of existing nonlinear model to include asymmetric payloads and quantification of uncertainties arising from the use of a variety of asymmetric payloads. The actual Moments of Inertia (MOI) of the Stewart Platform with asymmetric payload are computed and used to find worst case uncertainty bounds. The control performance of the proposed algorithm is verified by computer simulations. These simulations show that the system follows the desired trajectory and errors converge to equilibrium points efficiently.

Nonlinear robust control of marine diesel engine

ABSTRACT Diesel engines are widely used for the propulsion of marine vehicles and are exposed to uncertain working environment. In this paper, a robust nonlinear controller is proposed for a marine diesel engine that employees sliding mode control theory. The robust controller aims to maintain the desired diesel engine speed performance under harsh sea environment. The sliding surface has been carefully chosen that minimises the error in both angular velocity and acceleration. Robust control algorithm development and its tuning are also discussed. The performance of the proposed nonlinear robust controller is investigated thoroughly and is compared with a classical Proportional–Integral–Differentiation controller with integral windup scheme. Simulation results show that the proposed super-twisting-algorithm-based sliding mode controller can effectively improve the speed performance of the marine diesel engine in transient and steady operating conditions.

Review in thermal effects on the performance of electric motors

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

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.