Canopy air curtain （CAC） technology has been developed by the National Institute for Occupational Safety and Health （NIOSH） for use on continuous miners and subsequently roof bolting machines in underground coal m...Canopy air curtain （CAC） technology has been developed by the National Institute for Occupational Safety and Health （NIOSH） for use on continuous miners and subsequently roof bolting machines in underground coal mines to protect operators of these machines from overexposure to respirable coal mine dust. The next logical progression is to develop a CAC for shuttle cars to protect operators from the same overexposures. NIOSH awarded a contract to Marshall University and J.H. Fletcher to develop the shuttle car CAC. NIOSH conducted laboratory testing to determine the dust control efficiency of the shuttle car CAC. Testing was conducted on two different cab configurations： a center drive similar to that on a Joy 10SC32AA cab model and an end drive similar to that on a Joy 10SC32AB cab model. Three different ventilation velocities were tested-0.61, 2.0, 4.3 rrds （120, 400, and 850 fpm）. The lowest, 0.61 m/s （120 fpm）, represented the ventilation velocity encountered during loading by the continuous miner, while the 4.3 m/s （850 fpm） velocity represented ventilation velocity airflow over the shuttle car while tramming against ventilation airflow. Test results showed an average of the dust control efficiencies ranging from 74 to 83% for 0.61 m/s （120 fpm）, 39%-43% for 2.0 m/s （400 fpm）, and 6%-16% for 4.3 m/s （850 fpm）. Incorporating an airflow spoiler to the shuttle car CAC design and placing the CAC so that it is located 22.86 cm （9 in.） forward of the operator improved the dust control efficiency to 51%-55% for 4.3 m/s （850 fpm） with minimal impact on dust control efficiencies for lower ventilation velocities. These laboratory tests demonstrate that the newly developed shuttle car CAC has the potential to successfully protect shuttle car operators from coal mine respirable dust overexposures.展开更多
Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driv...Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.展开更多
In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Mo...In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.展开更多
The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based c...The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.展开更多
文摘Canopy air curtain （CAC） technology has been developed by the National Institute for Occupational Safety and Health （NIOSH） for use on continuous miners and subsequently roof bolting machines in underground coal mines to protect operators of these machines from overexposure to respirable coal mine dust. The next logical progression is to develop a CAC for shuttle cars to protect operators from the same overexposures. NIOSH awarded a contract to Marshall University and J.H. Fletcher to develop the shuttle car CAC. NIOSH conducted laboratory testing to determine the dust control efficiency of the shuttle car CAC. Testing was conducted on two different cab configurations： a center drive similar to that on a Joy 10SC32AA cab model and an end drive similar to that on a Joy 10SC32AB cab model. Three different ventilation velocities were tested-0.61, 2.0, 4.3 rrds （120, 400, and 850 fpm）. The lowest, 0.61 m/s （120 fpm）, represented the ventilation velocity encountered during loading by the continuous miner, while the 4.3 m/s （850 fpm） velocity represented ventilation velocity airflow over the shuttle car while tramming against ventilation airflow. Test results showed an average of the dust control efficiencies ranging from 74 to 83% for 0.61 m/s （120 fpm）, 39%-43% for 2.0 m/s （400 fpm）, and 6%-16% for 4.3 m/s （850 fpm）. Incorporating an airflow spoiler to the shuttle car CAC design and placing the CAC so that it is located 22.86 cm （9 in.） forward of the operator improved the dust control efficiency to 51%-55% for 4.3 m/s （850 fpm） with minimal impact on dust control efficiencies for lower ventilation velocities. These laboratory tests demonstrate that the newly developed shuttle car CAC has the potential to successfully protect shuttle car operators from coal mine respirable dust overexposures.
文摘Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.
文摘In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.
基金supported in part by the European Commission through the project P2P-Smartest:Peer to Peer Smart Energy Distribution Networks (H2020-LCE-2014-3,project 646469)
文摘The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.
