The ability to detect and diagnose faults is essential for the safe and reliable control of mechanical and electrical systems. In the presence of a fault, the system behaviour may become unpredictable, resulting in a loss of control which can cause unwanted downtime as well as damage to the system. There are two main types of methods to detect and diagnose faults: signal-based and model-based. Signal-based fault detection methods typically use thresholds to extract information from available measurements. This information is then used to determine if a fault is present. Model-based methods, as the name suggests, makes use of faults which can be modelled, typically through system identification. This type of fault detection and diagnosis is popular when well-defined models can be created and utilized. A number of fault detection and identification (FDI) methods are being implemented and developed by our research group. For example, one area of research involves developing a new neural network strategy to be used determining and predicting the presence of faults in engines. Another area of research involves the development of an interacting multiple-model approach, in an effort to correctly identify the presence of faults in an electrohydrostatic actuator (EHA).

Testing E-Axles, Electric Motors, Inverters:

• Efficiency Mapping
• Performance Characterization
• Fault Detection, Diagnosis and Prognosis

CMHT has state of the art facilities for testing the e-axles, motors and inverters used in electric and hybrid vehicles. This test equipment is used for advanced research in the areas of performance testing, test optimization, integration testing and with the advanced fault detection and prognostics software, we are able to provide unprecedented insight into e-mobility research.

CMHT is researching with major suppliers and users, battery cells, packs and battery management systems.

CMHT has unique testing capabilities with specialized testing equipment from Digatron and D&V Electronics.  These resources allow CMHT to provide deep research into batteries, cells and the software running these systems.

Research areas include:

• EIS Model Fitting and Analysis
• State of Charge/ State of Health Estimation
• Li-Ion Battery Modelling

Case Studies:

• Battery State of Health Estimation by Marvin Messing

Estimation theory plays an important role in a variety of fields – ranging from astronomy to commerce, and biochemistry to mechanics. It involves determining a value of some parameter of interest, typically by extraction from noisy observations. In engineering, one is usually concerned with the system states of a mechanical or electrical system. Quite often, the states are representative of the dynamics of the system. For example, one may be interested in the position, velocity, and acceleration of an actuator piston. Knowledge of these states is important for the successful control of the actuating device. The states are observed or measured by the use of sensors in the environment. However, measurements typically contain unwanted information such as system and measurement noise. It is the role of a filter to extract the useful information from the measurements, while minimizing the effects of noise and other unwanted disturbances. The smooth variable structure filter (SVSF) is a relatively new (2007) estimation strategy based on sliding mode theory, and has been shown to be robust to modeling uncertainties. It is a predictor-corrector method that makes use of an existence subspace and of a smoothing boundary layer to keep the estimates bounded within a region of the true state trajectory. This creates an inherently stable estimation process. This area of research involves advancing the development of the filter, and working on implementations (simulated and experimental) of the SVSF.

Automotive tracking systems are integral part of any autonomous driving system. A generic automotive tracking system performs several functions. The environment is perceived using sensors such as LiDAR, radar, vision sensor, ultrasonic sensor, and infrared camera. The acquired information is processed for detection and classification of objects of interest. Artificial intelligence and machine learning techniques are among pioneering approaches to tackle this issue. The noisy information about the objects are later employed to estimate the quantities of interest such as position, direction, velocity, acceleration, shape, size, etc. for each object. This information is essential for decision making process for autonomous driving functionalities. In CMHT we have developed our in-house car detection and tracking technology, which has led to a number of publications, and an experimental setup for on-road driving, data collection, and real-time detection and tracking. We aim to use information from different sensors and sensor fusion methods to extend the scope of the project.

Mechatronics is a multidisciplinary subject, involving mechanical, electrical, computer, control, and systems design engineering. In our research group, mechatronics engineering is used to unite the principles of mechanics, electronics, and computing to generate a simpler, more economical and reliable system. Recently, a novel electrohydrostatic actuator (EHA) prototype was created by combining a computer controlled servomotor with a hydraulic actuator circuit. An EHA is an emerging type of actuator typically used in the aerospace industry, and are self-contained units comprised of their own pump, hydraulic circuit, and actuating cylinder. The main components of an EHA include a variable speed motor, an external gear pump, an accumulator, inner circuitry check valves, a double-rod double-acting cylinder, and a bi-directional pressure relief mechanism. The objective of the design was to provide an efficient and accurate method of actuating heavy loads. A variety of control strategies were studied and implemented on the system in an effort to enhance the system performance. For example, strategies such as fuzzy logic, PID, feedforward, and sliding mode control were implemented. The results of this research have led to a number of conference and journal publications.

Biography: Dr. Peyman Setoodeh received his B.Sc. and M.Sc. degrees (Hons.) in electrical engineering from Shiraz University, and his Ph.D.  in computational engineering and science from McMaster University. He was the Harrison McCain visiting professor with the Marine Additive Manufacturing Centre of Excellence (MAMCE), University of New Brunswick, and an associate professor with the School of Electrical and Computer Engineering, Shiraz University. Previously, he was a senior research engineer with Huawei Noah’s Ark Lab, and a lecturer with the Department of Electrical and Computer Engineering, McMaster University. Dr. Setoodeh was the recipient of the Monbukagakusho Scholarship from the Ministry of Education, Culture, Sports, Science, and Technology in Japan. He has coauthored two books: “Fundamentals of Cognitive Radio” and “Nonlinear Filters: Theory and Applications”. He is a coauthor of a paper on “Cognitive Control”, which was featured as the cover story of the Proceedings of the IEEE in the December issue of the centennial year. Dr. Setoodeh is a senior member of the IEEE.

Research Interests: Artificial Intelligence, Deep learning and deep reinforcement learning, Cognitive systems (integration of perception, memory, attention, learning, planning and action), Industry 4.0 (cyber-physical systems, IoT, Ios, smart factory), battery management sytsems, nonlinear estimation.

Biography: Farzaneh holds a B.Sc. from Tabriz University in Electrical Engineering (2015) and a M.A.Sc. in Control Engineering from Shahid Beheshti University (2019). She is currently a Ph.D. student with the department of Mechanical Engineering at McMaster University since May 2022. Farzaneh joined CMHT as part of the battery group working specializing in Battery Mangaement Systems.

Biography: Christian holds a B.Eng (Hons) degree in Mechanical Engineering from Coventry University in the United Kingdom. He has over 3 years of industry experience on High Horse Power Diesel and Natural Gas engines manufactured by Cummins and Jenbacher respectively. He joined the Centre of Mechatronics and Hybrid Technologies (CMHT) in September 2022 and is working on his M.A.Sc degree under the supervision of Dr. Habibi. His Masters research focuses on Fault Detection and Diagnostics of internal combustion engines.

Biography: Linnea received her B.Tech in Automotive Engineering from McMaster University in 2021. She joined CMHT as part of the Autonomous team in May 2022. Linnea is a M.A.Sc student under the supervision of Dr. Habibi. Her research focuses on the application of radar and vision systems for object detection and scenario identification.