技术聚焦:北美市场的干式双离合器变速器

<正>双离合器变速器(DCT)是由两个离合器组合在一起的自动换档变速器,一个离合器控制奇数档,另一个控制偶数档,让驾驶员更加平顺地换档,没有扭矩迟滞。从一个离合器转换到另一个离合器只需几毫秒。双离合器变速器(DCT)的效率在于它不会像使用行星齿轮结构     

Dynamic model of supercritical Organic Rankine Cycle waste heat recovery system for internal combustion engine

The supercritical Organic Rankine Cycle (ORC) for the Waste Heat Recovery (WHR) from Internal Combustion (IC) engines has been a growing research area in recent years, driven by the aim to enhance the thermal efficiency of the ORC and engine. Simulation of a supercritical ORC-WHR system before a real-time application is important as high pressure in the system may lead to concerns about safety and availability of components. In the ORC-WHR system, the evaporator is the main contributor to thermal inertia of the system and is considered to be the critical component since the heat transfer of this device influences the efficiency of the system. Since the thermo-physical properties of the fluid at supercritical pressures are dependent on temperature, it is necessary to consider the variations in properties of the working fluid. The wellknown Finite Volume (FV) discretization method is generally used to take those property changes into account. However, a FV model of the evaporator in steady state condition cannot be used to predict the thermal inertia of the cycle when it is subjected to transient heat sources. In this paper, a dynamic FV model of the evaporator has been developed and integrated with other components in the ORC-WHR system. The stability and transient responses along with the performance of the ORC-WHR system for the transient heat source are investigated and are also included in this paper.     

Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization

Simultaneous Perturbation Stochastic Approximation (SPSA) has gained favor as an efficient optimization method for calibrating computationally intensive, “black box” traffic flow simulations. Few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but at a very slow rate. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, have limited time to reach a high-quality solution. The existing literature indicates that SPSA provides the potential for upgrading both off-line and on-line solutions alike, by delivering high-quality solutions within seconds. This article describes an extensive set of optimization tests involving SPSA and genetic algorithms (GAs). The final results suggest that GA was slightly more efficient than SPSA. Moreover, the results suggest today's signal timing solutions could be improved significantly by incorporating GA, SPSA, and “playbooks” of preoptimized starting points. However, it may take another 5–10 years before our computers become fast enough to simultaneously optimize coordination settings (i.e., cycle length, phasing sequence, and offsets) at numerous intersections, using the most powerful heuristic methods, at speeds that are compatible with real-time adaptive solutions.     

Energy Consumption Analysis of Different Bev Powertrain Topologies by Design Optimization

Flexible layout of electric motors in battery electric vehicles (BEVs) has enabled different powertrain topologies to be used. However, these different powertrain topologies also affect the overall efficiency of energy conversion from the electrochemical form stored in the battery to the mechanical form on the driving wheels for vehicle propulsion. In this study, a methodology combining an energy-based BEV simulation model with the genetic algorithm optimization approach is applied to evaluate the energy efficiency of three different BEV powertrain topologies. The analysis is carried out assuming two different urban driving conditions, as exemplified by the New European Drive Cycle (NEDC) and the Japanese JC08 drive cycle. Each of the three BEV powertrain topologies is then optimized – in terms of its electric motor size and, where applicable, gear reduction ratio – for minimum energy consumption. The results show that among the three powertrain topologies, the wheel-hub drive without gear reducers consumes the least energy. The energy consumption of BEVs under the more aggressive JC08 drive cycle is consistently 8 % above that under NEDC for all three powertrain topologies considered.