Iterative optimal and adaptive control of a near isothermal liquid piston air compressor in a compressed air energy storage system

Abstract

Abstract-The power density and efficiency of high compression ratio (∼200:1) air compressors/expanders are crucial for the economical viability of a Compressed Air Energy Storage (CAES) system such as the one proposed in [1]. There is a trade-off between power density and efficiency that is strongly dependent on the heat transfer capability within compressor/expander. In previous papers, we have shown that the compression or expansion trajectory can be optimized so that for a given power, the efficiency can be optimized and vice versa. Theoretically, for high compression ratios, the improvement over ad-hoc trajectories can be significant-for example, at the same efficiency of 90%, the power can be increased by 3-5 folds [2], [3], [4], [5]. Yet, the optimal trajectories depend on the heat transfer coefficient profile that is often unknown. In this paper, we focus on the experimental study of an iterative control algorithm to track a compression trajectory that optimizes the efficiency-power trade-off in a liquid piston air compressor. First, an adaptive controller is developed to track any desired compression trajectory characterized by the temperature-volume profile. The controller adaptively estimates the unknown heat transfer coefficient. Second, the estimated heat transfer coefficient from one iteration is then used to estimate the optimal compression trajectory for the next iteration. As the estimate of the heat transfer coefficient improves from one iteration to the next, the quality of the estimated optimal trajectory also improves. This leads to successively improved efficiency. The experimental results of optimal trajectories show up to 2% improvement in compression efficiency compared to linear trajectories in a same power density.

Optimal Control Experimentation of Compression Trajectories for a Liquid Piston Air Compressor

“…The experimental setup and procedure were discussed in detail. An adaptive nonlinear controller designed in [11] is employed to command the desired flow-rate to the compressor chamber and track the temperaturevolume optimal trajectories. Since the actual value of h is not known, an iterative procedure was introduced to obtain the optimal trajectory using the experimental data.…”

“…Figure 4 illustrates the tracking of the temperature-volume optimal trajectory for compression times of 3s, 5s and 7s. The reader is referred to [11] for more details on design and implementation of the controller employed to track the optimal trajectories. The experimental temperature profiles for compression times of 1.5s and 2s are also shown for optimal and constant flow-rate trajectories in Figure 5.…”

“…To overcome these issues, a temperature-volume trajectory tracking controller that adaptively estimates the unknown heat transfer coefficient h was developed and presented in our previous paper [11]. This controller is used to track an estimated optimal temperature-volume profile.…”

Modeling and Trajectory Optimization of Water Spray Cooling in a Liquid Piston Air Compressor

“…Application of porous inserts for improving heat transfer during air compression has also been investigated [7]. In addition, the compression/expansion trajectory can be optimized and controlled to increase the efficiency for a given power or to increase power for a given efficiency [3,[7][8][9].…”

Heat transfer analysis of liquid piston compressor for hydrogen applications

“…However, none of these studies considered the real properties of the operating gas and liquid, heat transfer through interface, and role of the compressor 's wall resistance [4] [6]. Several other studies have investigated the trajectories of the compressor/ expander that lead to optimal tradeoff between efficiency and power in compressed air energy storage systems [7][8] [9] [10]. Although, heat transfer model plays an important role in finding the optimal 3 trajectory, none of these studies used a detailed heat transfer model.…”