Steady-State Control of Fuel Cell Based on Boost Mode of a Dual Winding Motor

In recent years, a dual winding motor drive has been proposed in the field of fuel cell vehicles due to its advantages of good performance and high robustness. This new topology and its basic control method have been widely investigated. However, the previous research has not considered the current dynamic property of a fuel cell when studying the power sharing control method, but this is an important research objective for fuel cell durability. Considering the current change principle of a fuel cell, an optimal steady-state control method based on a new dual winding motor architecture boost charging is proposed to optimize the fuel cell life. In addition, in view of the current circulation problem of the fuel cell side winding in the boost mode, a Bang-Bang-PI control algorithm with a relatively constant reference value is proposed to realize the current sharing control. On this basis, the optimized control of the output current ripple of the fuel cell is realized to ensure the steady-state of the proton exchange membrane fuel cell (PEMFC). Finally, the results show that this method can control the stability of the fuel cell efficiently.

Operation and Control of a Supercritical CO2 Compressor

Among the list of advanced technologies required to support the energy industry’s novel Supercritical Carbon Dioxide (sCO2) power cycle is the need for a robust and responsive control system. Recent testing has been performed on a 2.5 MWe sCO2 compressor operating near the critical temperature (31C) and critical pressure (73.8 bar), developed with funding from the US DOE Apollo program and industry partners. While sCO2 compression has been performed before, operating near the critical point has many key benefits for power generation with its low head requirements and smaller physical footprint. However, with these benefits come unique challenges, namely controlling this system to steady-state operating conditions. Operating just above the critical point (35°C [95°F] and 8.5 MPa [1,233 psi]) there can be large and rapid swings in density produced by subtle changes in temperature, leading to increased difficulty in maintaining adequate control of the compressor system. This means that proper functionality of the entire compressor system, and its usefulness to a closed loop recompression Brayton power cycle, is largely dependent on variables such as thermal sources, precision and response time of the instrumentation, proper heat soaking, and strategic filling and venting sequences. While other papers have discussed the science behind and performance of sCO2 compressors, this paper will discuss the challenges associated with steady-state control of the compressor at or near operating conditions, how the fill process was executed for optimal startup, and changes that occurred while idling during trip events.

Steady-State Policy Synthesis in Multichain Markov Decision Processes

The formal synthesis of automated or autonomous agents has elicited strong interest from the artificial intelligence community in recent years. This problem space broadly entails the derivation of decision-making policies for agents acting in an environment such that a formal specification of behavior is satisfied. Popular formalisms for such specifications include the quintessential Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) which reason over infinite sequences and trees, respectively, of states. However, the related and relevant problem of reasoning over the frequency with which states are visited infinitely and enforcing behavioral specifications on the same has received little attention. That problem, known as Steady-State Policy Synthesis (SSPS) or steady-state control, is the focus of this paper. Prior related work has been mostly confined to unichain Markov Decision Processes (MDPs), while a tractable solution to the general multichain setting heretofore remains elusive. In this paper, we provide a solution to the latter within the context of multichain MDPs over a class of policies that account for all possible transitions in the given MDP. The solution policy is derived from a novel linear program (LP) that encodes constraints on the limiting distributions of the Markov chain induced by said policy. We establish a one-to-one correspondence between the feasible solutions of the LP and the stationary distributions of the induced Markov chains. The derived policy is shown to maximize the reward among the constrained class of stationary policies and to satisfy the specification constraints even when it does not exercise all possible transitions.