Abstract:Energy storage systems are increasingly gaining importance with regard to their role in achieving load levelling, especially for matching intermittent sources of renewable energy with customer demand, as well as for storing excess nuclear or thermal power during the daily cycle. Compressed air energy storage (CAES), with its high reliability, economic feasibility, and low environmental impact, is a promising method for large-scale energy storage. Although there are only two large-scale CAES plants in existence, recently, a number of CAES projects have been initiated around the world, and some innovative concepts of CAES have been proposed. Existing CAES plants have some disadvantages such as energy loss due to dissipation of heat of compression, use of fossil fuels, and dependence on geological formations. This paper reviews the main drawbacks of the existing CAES systems and presents some innovative concepts of CAES, such as adiabatic CAES, isothermal CAES, micro-CAES combined with air-cycle heating and cooling, and constant-pressure CAES combined with pumped hydro storage that can address such problems and widen the scope of CAES applications, by energy and exergy analyses. These analyses greatly help us to understand the characteristics of each CAES system and compare different CAES systems.
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Parallel Hybrid Electric Vehicle consists of internal combustion engine, engine clutch, motor, automatic transmission, Integrated Starter Generator (ISG), and battery. Due to hybridizations such as using engine clutch to disengage the internal combustion engine and omitting torque converter from the automatic transmission to increase fuel economy, drivability will not be same as conventional vehicle. To ensure drivability comparable to conventional vehicle, dynamic simulation has been utilized to foresee the drivability issues for the proposed hybrid system and ideas for improvements are tested in simulation. CoSimulation of AMESim vehicle model and Simulink Hybrid Control Unit (HCU) model has been used to test and improve HCU logic.
If the future driving condition such as road information and traffic condition can be predicted, the use of electrical power source will be controlled appropriately in order to improve the fuel economy of Hybrid vehicle. In this paper the algorithm for the driving condition prediction model and the rule-based controller for HEV are developed and verified through simulation and road test. With road information and traffic from 3D navigation, the types of road (uphill, flat or downhill) and the traffic condition (congestion or free driving) can be predicted by the Driving Condition Prediction System (DCPS). The rule-based controller for HEV can determine the control strategy (discharge-oriented, charge-oriented, or normal) depending on the future driving condition. With this technology the system can secure more battery capacity for regenerating when downhill is anticipated and engine can be operated within high efficiency area by discharging battery energy when uphill is anticipated. When congestion is predicted the battery is charged in advance in order to increase electric driving (EV) range and prevent inefficient series-path driving. Compared to the previous study, the methodology to determine future road condition and control strategy of HEV suggested in this paper is simple and fast enough to apply to real-time controller.
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