Thermo-Economic Comparison and Parametric Optimizations among Two Compressed Air Energy Storage System Based on Kalina Cycle and ORC

A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries

The prognosis of lithium-ion batteries for their remaining useful life is an essential technology in prognostics and health management (PHM). In this paper, we propose a novel hybrid prediction method based on particle filter (PF) and extreme learning machine (ELM). First, we use ELM to simulate the battery capacity degradation trend. Second, PF is applied to update the random parameters of the ELM in real-time. An extreme learning machine prognosis model, based on particle filter (PFELM), is established. In order to verify the validity of this method, our proposed approach is compared with the standard ELM, the multi-layer perceptron prediction model, based on PF (PFMLP), as well as the neural network prediction model, based on bat-particle filter (BATPFNN), using the batteries testing datasets of the National Aeronautics and Space Administration (NASA) Ames Research Center. The results show that our proposed approach has better ability to simulate battery capacity degradation trends, better robustness, and higher Remaining Useful Life (RUL) prognosis accuracy than the standard ELM, the PFMLP, and the BATPFNN under the same conditions.

Thermodynamic Analysis of Kalina Based Power and Cooling Cogeneration Cycle Employed Once Through Configuration

The Kalina cycle (KC) has been considered one of the most efficient systems for harvesting low grade heat since its proposal and various modifications have been proposed. Recently, Kalina based power and cooling cogeneration cycles (KPCCCs) have attracted much attention and many studies have been conducted. In this paper, a cogeneration cycle of power and absorption refrigeration based on the Kalina cycle system 11 (KCS-11) is proposed. The cycle combines a KC and aqua-ammonia absorption refrigeration cycle (ABR) with once through configuration. Compared to the stand-alone KC, the proposed cycle showed significantly higher energy efficiency—as high as 60%—without the use of rectifier, superheater or subcooler. Parametric analysis showed that the ammonia fraction, separator pressure and source temperature have a significant impact on the system performance including mass flow rates, heat transfers, power generation, cooling capacity, energy efficiencies and optimum ammonia fraction for the maximum energy efficiency.

Thermodynamic Analysis of a Hybrid Power System Combining Kalina Cycle with Liquid Air Energy Storage

Liquid air energy storage (LAES) is a promising energy storage technology in consuming renewable energy and electricity grid management. In the baseline LAES (B-LAES), the compression heat is only utilized in heating the inlet air of turbines, and a large amount of compression heat is surplus, leading to a low round-trip efficiency (RTE). In this paper, an integrated energy system based on LAES and the Kalina cycle (KC), called KC-LAES, is proposed and analyzed. In the proposed system, the surplus compression heat is utilized to drive a KC system to generate additional electricity in the discharging process. An energetic model is developed to evaluate the performance of the KC and the KC-LAES. In the analysis of the KC subsystem, the calculation results show that the evaporating temperature has less influence on the performance of the KC-LAES system than the B-LAES system, and the optimal working fluid concentration and operating pressure are 85% and 12 MPa, respectively. For the KC-LAES, the calculation results indicate that the introduction of the KC notably improves the compression heat utilization ratio of the LAES, thereby improving the RTE. With a liquefaction pressure value of eight MPa and an expansion pressure value of four MPa, the RTE of the KC-LAES is 57.18%, while that of the B-LAES is 52.16%.