Intelligent control system design and deployment for fuel cell air supply systems

Abstract

The mobility industry invests in intensive research and development programs to find a sustainable energy source without polluting the environment. A fuel cell system is an electrochemical device that generates electricity and is one of the promising energy sources. However, the commercialization of fuel cell systems is limited due to their lifespan. In this thesis, an intelligent control system has been designed and deployed for fuel cell air supply systems to enhance the net output power and mitigate the degradation due to oxygen starvation, resulting in a longer cell lifespan. To minimize the risk of starvation, more air than needed for chemical reactions is supplied to the fuel cell system. Here, the ratio between supplied and consumed oxygen amounts is defined as the excess ratio. The net output power of fuel cell systems deteriorates when the oxygen excess ratio is high, yet starvation occurs if the oxygen excess ratio is low. Therefore, an accurate control of oxygen excess ratio is crucial to not only maximize the net output power but also reduce the risk of starvation to mitigate cell degradation. To address this challenge, a 2DOF control structure fused with artificial intelligence is proposed in this thesis. The proposed control system involves a data-driven reference generator, a feedforward controller, and a feedback controller. The data-driven reference generator calculates the setpoint value of the oxygen excess ratio. On the other hand, the data-driven feedforward controller calculates the open-loop control signal to anticipate known system dynamics for improving control performance and reducing the control effort by the feedback controller. A PI controller is used as the feedback controller to track the desired setpoint value and calculate the closed-loop control signal. Then, the sum of open-loop and closed-loop control signals is applied to the compressor motor as a voltage input. A fuel cell system was simulated for various current loads and oxygen excess ratio values at the optimal stack temperature to understand the characteristics of fuel cell systems. The results showed that each stack current maximizes the net output power for a specific oxygen excess ratio. The relationship between the stack current and oxygen excess ratio that produces maximum net output power is highly nonlinear, which is challenging to model via traditional lookup-based solutions. Similarly, the compressor voltage needed to reach the optimal oxygen excess ratio, maximizing the net output power and stack current, also has a complex relationship. Therefore, data-driven reference generators and feedforward controllers are considered for learning the complex characteristics of fuel cell systems. The data to learn the data-driven models is acquired through steady-state analysis. Firstly, the data for single input models where the stack current is the only input of data-driven models is acquired by alternating the current and oxygen excess ratio at optimal stack temperature. Moreover, the stack temperature is changed by considering possible temperature fluctuations around the optimal stack temperature, and its effect on net power output is investigated. The results show that the net output power significantly changes with stack temperature. Therefore, it needs to be considered in the design of data-driven models. In this manner, the double-input models are designed with stack current and temperature inputs. The data-driven reference generator and feedforward controller for single and double-input models are learned via fuzzy models and neural networks. Various internal configurations of fuzzy models and neural networks have been studied to investigate their effects on modeling accuracy. The fuzzy models were constructed with various membership functions and learned through various techniques. On the other hand, different activation functions were utilized to build the neural network models. Moreover, the learning data was pre-processed through standardization and normalization to examine their effect on learning performance. Besides, polynomial regression-based reference generators and feedforward controllers were learned for performance comparison. Even though the learning performances of data-driven reference generators and feedforward controllers are pretty satisfactory compared to polynomial regression-based models, their contribution to net output power and degradation must be shown. In this manner, the proposed artificial intelligence fused 2DOF control system was simulated with various fuzzy models and neural networks as the reference generator and feedforward controller. A PI controller was utilized as the closed-loop controller. The PI controller coefficients were tuned through an iterative trial-and-error method in a defined operating point. The same PI controller was employed during the simulations of each design variant to have a fair comparison. In addition, a 1DOF control system was designed to expose the contribution of the 2DOF control structure. To assess the test results, evaluation criteria for the net output power and oxygen excess ratio were defined. In the evaluations, the tracking error and settling time of both targets were considered. In addition, a degradation model that depends on oxygen starvation was created to assess the contribution of data-driven models on cell life. A set of operation points depending on stack current was developed to test the proposed control system. Moreover, stack temperature changes were considered to assess the performance of the proposed control system under disturbances. The results showed that the proposed intelligent control system with fuzzy models and neural networks could efficiently track the desired oxygen excess ratio. Thanks to the data-driven models, the high-performing oxygen excess ratio control structure increases the net output power of fuel cell systems and reduces cell degradation due to oxygen starvation. In brief, the intelligent control system proposed in this thesis is a promising development in fuel cell systems to enable their widespread usage as a clean and sustainable energy source.