Dynamic response assessment and multi-objective optimization of organic Rankine cycle (ORC) under vehicle driving cycle conditions. (15th January 2023) - Ping, Xu
Abstract: Organic Rankine cycle (ORC) can effectively utilize the waste heat energy of internal combustion (IC) engine. The ORC system has obvious large time-varying characteristic, strong fluctuation, and hysteresis under the synergistic influence of frequent fluctuation of vehicle speed and operating parameters in the system. The power system, waste heat recovery system, transmission system, and other subsystem models are constructed in this study. The key components are verified. An integrated system model of IC engine–ORC for driving cycle is constructed on the basis of the subsystem model. The dynamic response of ORC system is evaluated under different road conditions. The synergistic influence of multiple variables on system performance is analyzed. Obvious strong coupling and nonlinear characteristics are observed among different performances of ORC system under road conditions. Therefore, a multi-objective cooperative optimization framework of ORC system considering actual road conditions is proposed on the basis of multidimensional data. In the process of collaborative optimization, thermal efficiency and emissions of CO2 equivalent show a clear trade-off under different road conditions. The limit value of thermal efficiency under new European driving cycle is also 6.48% higher than that under worldwide harmonized light vehicles test cycle. Frequent fluctuation of vehicle speed is not conducive to obtaining the limit value of thermal efficiency. The evaluation and optimization of the comprehensive performance of ORC system under complex road conditions can provide a reference for the practical application of vehicle ORC system. Highlights: Proposed model integrates vehicle, organic Rankine cycle (ORC) and driving cycles. We investigate the dynamic response characteristic of ORC under four driving cycles. A complex multi-objective optimization framework is presented considering nonlinear. Comprehensive performance of ORC is optimized considering trade-off decisions.