Stationary methanol steam reforming to hydrogen fuel for fuel-cell filling stations

Renewable hydrogen (H2) carriers such as methanol (MeOH) can be reformed back into H2 and is a fundamental chemical conversion for the long-term viability of the H2 economy due to its high density and ease of transportability compared to H2. MeOH is an especially important carrier as it is a simple C1 chemical that can be produced from solar-PV-generated H2 and direct-air-captured CO2 with a current commercially practical solar-to-fuel efficiency of 10% from renewable solar energy.

Faults Detection and Recovery in Automobile Engines Machining and Assembly Systems Using AI and Machine Learning

Ford is applying Industry 4.0 smart manufacturing technologies in its re-tooled and new plants in Canada. This project aims to research, in cooperation with Ford, intelligent methods for recognizing patterns of machines faults and their causes and develop recovery strategies based on artificial intelligence (AI) and Machine Learning (ML) using data analytics, neural networks, and deep learning methods. It will also investigate the contribution of workers-automation interactions to faults occurrence using data, and minimizing, and discovering possible correlations or trends.

Human Motion Library and Predictive Capabilities for Digital Human Ergonomics Simulation Solution

Within their ergonomics process, automotive manufacturers rely heavily on computer simulation technology, specifically "Jack" (Siemens PLM, Plano, TX). Advancements to "Jack" provide users the ability to create workstations, yet much time is required to produce a single simulation. This 3-year industrial collaboration will reduce the time necessary for their completion and, improve on the accuracy of digital simulations.

Cognitive Powertrain and Metaveillogrammetric sensing for transportation

There is massive growth in the area of smart cities (e.g. sensors in streetlights), smart cars, and "smart people" (sensors on people, e.g. wearable computing). In some cities like San Diego, there are cameras and microphones in nearly every streetlight in the downtown core area. Most cars made now have one or more cameras in them, and numerous other kinds of sensors are being invented. These sensors are important regarding autonomous vehicles as well as technologies for extended human intelligence and safety.

High Energy, RAnge extending battery for Electrical Vehicles (HERA-EV)

We can deal with the daunting challenges of depleting fossil fuels as-well-as the toxic and greenhouse gases emitted from the gasoline/diesel driven vehicles by replacing their internal combustion engine with rechargeable batteries. This project focuses on the development of an all-metal-free and biocompatible rechargeable battery system that will compete with the existing counterparts (Lithium-ion batteries, LIBs) in Electric Vehicles (EVs) currently under development.

Manufacturing and stabilization of nanocellulose in a scale up process using functional maleates as a colloidal dispersion

Cellulose nanocrystals (CNC), a form of cellulose, shows a lot of promise in the development of sustainable materials thanks to its unique properties such as high performance, large surface area, is readily available, renewable, and biodegradable. Early methods to synthesize CNC have not been very successful. In order to isolate CNC, to make them ready for fast production of industry needs, these challenges need to be resolved by chemical modifications.

Ignition control on DME/OME engines

Considering the soot-free burning characteristics of DME/OME fuel, highly diluted intake charge can be introduced in order to realize ultra-low nitrogen oxides emissions. However, the ignition process of the highly diluted fuel/air mixture is difficult, and an unstable ignition process is detrimental to fuel efficiency and engine performance. In this project, innovative ignition systems including a multi-site ignition system. and volumetric ignition system, together with novel ignition strategies will be developed.

Evaluating the prospective benefits of physical demands description (PDD) data created from job simulations

Every job at Ford Motor Company should have an associated document describing the physical demands (i.e., lifting, climbing, pushing, etc.) required in that job. This information is important to select job candidates with the right blend of capabilities to be able to safely meet these demands. Perhaps more importantly, this information also provides a benchmark to guide health care providers in helping injured workers rehabilitate their capabilities so that they can again return to their job, safely meeting the job’s demands.

Advanced wearable inertial tracking system to monitor automotive assembly operator motion for human simulation applications

Capturing the real human motions on the assembly plant floor is the key point for developing accurate virtual simulations. The real human motions of specific workstation operations at Ford Oakville Assembly Plant by using wearable inertial sensors will be collected. After data collection, virtual simulations will be performed for all the recorded operation tasks. Based on simulations, physicians can observe and conduct ergonomic assessment of each of operation tasks on the plant floor.

Ergonomic Design of an Automotive Material Sequencing Centre - Year two

The Ford Motor Company is bringing 800 jobs into the Oakville Assembly Plant. These jobs will be concerned with sequencing parts for the new Material Sequencing Centre. To ensure that workers remain healthy, and their
productivity and quality output is up to Ford's high standards, Ford (through this fellowship) wants to establish clear ergonomic guidelines for this type of work. The post-doctoral fellow will conduct surveys in the plant, as well as review existing ergonomic guidelines within Ford.

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