The investigation of extreme environments can place considerable operational difficulties on the engineering systems that people rely upon to safely analyze and at times operate within.
When it comes to high-value and safety-critical applications, like sub-surface drilling or space exploration, the extreme and occasionally dynamic operating conditions in the environment, can make it hard to comprehend the expected service life of crucial sub-systems and components. Therefore, it is an extremely complex and at times unviable situation to precisely comprehend and forecast.
To enable resilient, economically viable, and safe operations in such challenging surroundings, it is essential to comprehend the impact of high temperatures on critical devices like Electrochemical Capacitors (ECs).
In contrast to a battery, ECs, also called supercapacitor, electrochemical double-layer capacitor, or ultracapacitor, have the ability to resist high charge-discharge currents and are hence appropriate for resisting peak power demands. The long cycle life of ECs while being operated in a high-temperature environment makes them perfect for hard and extreme surroundings.
Inside extreme environment systems, it is usual to operate the components over the limits of the manufacturer’s suggestions. This makes the potential to comprehend and predict the end of life of such components a considerable challenge.
To deal with this difficulty, the researchers concentrated on ECs run at temperatures ranging up to 200 °C, particularly the operation of ECs onboard downhole drilling equipment for geothermal or oil and gas exploration.
Downhole tools are complicated electromechanical systems that execute crucial functions in drilling operations and have been designed particularly to resist vibrations, shocks, and extreme temperatures.
In the study, the researchers deployed a machine learning algorithm to forecast degradation trends for electrochemical double-layer capacitors beyond the knee-point start when being cycled at high temperature in an oil and gas drilling surrounding.
Operations performed at high temperature expedites EC degradation and thus the worst-case scenario for the referred application was analyzed.
The study was recently reported in the journal IEEE Access and guided by Professor Flynn’s Smart Systems Group at Heriot-Watt University, in collaboration with Baker Hughes, University of Maryland, and the Lloyds Register Foundation.
This research demonstrates a significant advancement in the ability to understand and predict the life expectancy of critical components. Our experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%.
David Flynn, Professor, Heriot-Watt University
“Using lifecycle test data, something that is not readily available and very challenging to obtain, we create a machine learning model that has an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. Our model can be utilized to determine the EC degradation rate at a range of operating temperature values,” added Flynn.
Roman, D., et al. (2021) A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature. IEEE Access. doi.org/10.1109/ACCESS.2021.3057959.