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Pulsenics is a leading technology company specializing in advanced electrochemical analysis tools and methodologies. Their primary focus is on developing cutting-edge solutions for monitoring and optimizing electrochemical systems, including batteries, electrolyzers, and fuel cells. The company’s flagship technology, Electrochemical Impedance Spectroscopy (EIS), is utilized to assess the performance, health, and efficiency of various electrochemical devices without causing damage. Pulsenics aims to enhance the reliability and longevity of these systems by providing real-time data and insights into their internal processes.
Pulsenics is facing the challenge of improving the accuracy and reliability of battery stack performance evaluations. Traditional EIS methods, while effective, are time-consuming and can be limited by hardware constraints, such as power limitations and signal distortions. There is a need to optimize EIS protocols to provide faster, more detailed, and more reliable data, which will help in the early detection of issues and the enhancement of battery performance. The priority is to develop an advanced EIS protocol that leverages multisine signals and integrates machine learning techniques to enhance the resolution and reliability of battery performance data. This improved protocol should address hardware constraints and be validated through both simulation and real-world testing. This project will significantly advance Pulsenics’ capabilities
Francis Dawson
Pulsenics
Engineering
Professional, scientific and technical services
University of Toronto
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