In this study, an electric-vehicle-based thermal management system was designed for dual energy sources. An experimental platform developed in a previous study was modified. Regarding the mechanical components, a heat exchanger with a radiator, proportional valve, coolant pipes, and coolant pump was appropriately integrated. Regarding the electric components, two heaters emulating waste heat were controlled using two programmable power supply machines. A rapid-prototyping controller with two temperature inputs and three outputs was designed. Rule-based control strategies were coded to maintain optimal temperatures for the emulated proton exchange membrane fuel cells and lithium batteries. To evaluate the heat power of dual energy sources, driving cycles, energy management control, and efficiency maps of energy sources were considered for deriving time-variant values. The main results are as follows: (a) an advanced mechatronics platform was constructed; (b) a driving cycle simulation was successfully conducted; and (c) coolant temperatures reached their optimal operating ranges when the proportional valve, radiator, and coolant pump were sequentially controlled. The benefits of this novel electric-vehicle-based thermal management system are (a) highefficiency operation of energy sources, (b) low occupied volume integrated with energy sources, and (c) higher electric vehicle traveling mileage. This system will be integrated with real energy sources and a real electric vehicle in the future.

In order to ensure surface shape accuracy of ground optical parts produced by locus forming factors of precision grinding machme that affects surface shape accuracy of the part are theoretically analyzed Formulas used to calculate the error that can be canceled through the machme fine adjusting device are deduced Usmg calculated error values to adjust the fme adjustmg device is able to reduce the surface shape error and then surface shape accuracy ofparts is ensured Keywords locus formmg spherical optical parts surface shape machining error precision grmdmg