Advanced Testing for Fuel Cell Reliability

Comprehensive testing and evaluation method for hydrogen fuel cell stacks that covers multiple dimensions to provide a complete and accurate assessment of stack performance. The testing includes gas leakage, activation, cycling, polarization, cathode and anode sensitivity, humidity sensitivity, pressure sensitivity, and temperature sensitivity tests.

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Method for real-time evaluation of fuel cell stack performance and aging to enable proactive aging mitigation strategies. The method involves collecting steady-state characteristic data from the fuel cell at different operating points, determining indices like activation, ohmic, and concentration losses, and using those indices to evaluate stack degradation and infer the underlying causes.

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Autonomous performance testing of high temperature methanol fuel cells to evaluate fuel cell health and optimize performance over time. The method involves using a fuel cell controller to continuously monitor operating data like voltage, current, temperature, and compare it against an initial optimal data set. If new data is better, it replaces the optimal set and re-evaluates fuel cell health. This allows the fuel cell to optimize itself over time by tracking and updating optimal parameters based on real-world operating conditions.

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A comprehensive testing platform for solid oxide fuel cells that allows simultaneous evaluation of fuel cell performance, electrochemical behavior, and catalytic activity of fuel cell materials. The platform has a modular design with separate gas distribution, reaction, and analysis units. The gas distribution unit has multiple independent gas supply paths with pressure regulators, valves, and flow meters. The reaction unit has a furnace with sections for full cell testing, electrochemical testing, and catalytic testing connected to the gas distribution. Downstream, the analysis unit has electrochemical sensors to monitor reaction products. This allows concurrent fuel cell testing, gas composition analysis, and electrochemical and catalytic performance evaluation.

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A rapid test method for evaluating the durability of medium and high temperature proton exchange membrane fuel cells. The method involves accelerated testing conditions to simulate real-world operating conditions and assess the stability of the fuel cell components under dynamic loading. The testing steps include open circuit voltage, hydrogen penetration current density, cyclic voltammetry, and electrochemical impedance analysis. The accelerated testing conditions are designed to simulate dynamic fuel cell operation and accelerate corrosion mechanisms like hydrogen crossover, electrochemical corrosion, and catalyst particle detachment.

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Efficiently determining optimal operating parameters for fuel cell stacks through progressive testing instead of exhaustive testing. The method involves activating the fuel cell stack and then sequentially testing sensitivity to parameters like air metering ratio, pressure, temperature, and humidity in a defined order. After each sensitivity test, the optimized parameter values are applied to the next sensitivity test until the final optimal operating conditions are found. This progressive testing reduces the number of required test samples compared to exhaustive testing.

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A solid-state hydrogen fuel cell testing system that uses machine learning to predict fuel cell degradation and monitor performance without extensive testing. The system collects power, gas, and environment data during normal operation. It learns from this data to predict fuel cell capacity change. This allows targeted monitoring of specific cells instead of extensive testing. It also provides quantitative calibration of internal reactions using matched cells. The system determines optimal data collection frequencies based on driving tasks. It analyzes hydrogen and air supply rates to quantify electrochemical reactions.

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Method and device to estimate and visualize fuel cell stack performance degradation over time without start-stop cycling that can damage the stack. It groups stack voltage and current data by analysis cycles, calculates representative voltages for each current level, and uses those to estimate parameters for predicting voltage vs current curves. This allows estimating stack degradation without damaging starts/stops.

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A real-time, visual fuel cell catalytic evaluation system that allows direct observation and analysis of fuel cell catalyst degradation. The system includes a visualized fuel cell stack placed in an electron microscope's viewing area. Gas processing units provide hydrogen and oxygen to the stack. Temperature stabilization and data acquisition systems complete the setup. This enables real-time, visual monitoring and analysis of the fuel cell catalyst reactions as they occur, providing insights into catalyst degradation mechanisms and performance over time.

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Fuel cell system design and control method that improves overall efficiency by individually controlling each fuel cell stack's air flow to provide consistent output while preventing water buildup and catalyst degradation. The system operates multiple fuel cell stacks in parallel, but each stack has its own square wave air control signal. This generates air flow variations within each stack that discharge generated water effectively. By setting different duty ratios based on stack performance, the system provides consistent total output while avoiding water flooding and catalyst degradation.

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Fuel cell stack offline testing system that uses network connection to improve efficiency and accuracy compared to manual testing. The system involves a test platform, a vehicle-mounted T-Box, a network connection platform, and a fuel cell system controller. The network platform sends test instructions to the T-Box, which relays them to the fuel cell controller. The controller runs tests and the T-Box records stack data. The network platform analyzes the data to calculate stack performance and generate reports. This allows remote monitoring and analysis of offline tests, reducing labor, time, and errors compared to manual testing.

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A method for more efficiently predicting the remaining life of a fuel cell. The method involves controlling the fuel cell to be tested to perform a discharge operation, and during the discharge, monitoring the current and voltage in real time. Using these values along with start and end times, the remaining life of the fuel cell is predicted. This allows faster and more accurate prediction compared to durability testing of stack components. It provides an alternative method for predicting fuel cell life that is less complex and provides quicker results.

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A real-time fuel cell testing system that accurately reflects the performance of a fuel cell vehicle. The system has a fuel cell unit on the vehicle, a DC converter, an auxiliary power supply, a test module, a simulation module, a data module, and a control module. The fuel cell, DC converter, and auxiliary power supply are connected. The control module connects to all modules and coordinates real-time testing by integrating the fuel cell, converter, and auxiliary power consumption. The simulation module emulates real-world conditions. The data module collects and analyzes performance data. This allows precise, real-time fuel cell system testing that closely mimics in-vehicle operation.

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Simulation system and testing method for fuel cell durability that enables accurate fuel cell power control and durability testing without physical testing. The system uses hardware-in-the-loop simulation to model the fuel cell and surrounding systems. It allows simulating fuel cell aging and testing fuel cell durability without the limitations of physical testing. The simulation can provide accurate fuel cell target current and voltage based on real-time stack conditions. This prevents inaccurate table lookup data and allows accurate fuel cell power control as the cell ages. It also enables detecting failures within the simulation range and avoids the issues of frequent loading/unloading and hydrogen leakage in physical testing.

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A method and device to quickly determine if a fuel cell can pass durability testing without running the full test duration. The method involves activating the fuel cell, getting a current at a target point, then running it for a preset time and getting another current. By using a voltage-current-time relationship, it can quickly determine if the cell passes durability based on the initial and final currents.

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Consistency testing of proton exchange membrane fuel cells (PEMFCs) to evaluate the performance and stability of stacks containing multiple cells. The method involves using a filter to measure the voltage changes between cells under different load conditions. The filter output is analyzed to determine if the cell voltages fluctuate within acceptable ranges. If not, it indicates inconsistencies between cells. By testing under various loads, it provides a comprehensive evaluation of stack consistency. The filter output analysis provides a repeatable and comparable metric to compare results between tests.

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Method to accurately predict the life of a fuel cell by modeling the decay of performance over time for different operating conditions. The method involves bench testing the fuel cell under multiple conditions, then running it at a specific current to calibrate performance. Fitting functions are derived for decay rate vs time under each single condition. Using those functions and the target condition, a second fitting is made for decay at the target condition. With the target cell degradation known, the second fitting is used to calculate cell life. This allows predicting fuel cell longevity under realistic conditions by dissecting the impact of individual factors.

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Predicting fuel cell performance degradation in vehicles using real-world driving data rather than lab tests. The method involves retrieving vehicle operating data over a time interval, cleaning the data, extracting the fuel cell operating data, fitting curves to predict performance decay as working time increases, and using the curves to predict future performance decay. This allows continuous, flexible, and low-cost fuel cell performance degradation analysis and prediction based on actual driving conditions.

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A testing device and method for evaluating the performance of a vehicle fuel cell system that provides more accurate and realistic results compared to existing methods. The testing setup includes a fuel cell stack, supply/exhaust module, heat dissipation module, DC/DC module, auxiliary power module, load module, and control module. The DC/DC module converts the lower fuel cell voltage to the higher vehicle voltage. The load module simulates vehicle power consumption. The setup allows testing the fuel cell system with realistic conditions and losses, including the DC/DC conversion efficiency. By testing the system under different operating points, it provides accurate net output power, efficiency, and capacity calculations.

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Estimating the durability of a fuel cell system using machine learning and transformations when there is insufficient training data. The method involves: (1) training a machine learning model using actual operating data from a first fuel cell system, (2) collecting durability test results for both the first and a second, different fuel cell system under the same operating conditions, (3) determining transformations between durability test results of the two systems using the collected data, and (4) estimating the durability of the second system using the transformations and the machine learning model's estimated durability for the first system.

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