Dynamic Optimization of Diesel Air-Path Control for Reduced Pumping Work

One potential method to reduce fuel consumption in diesel engines with variable geometry turbines (VGT) and exhaust gas recirculation (EGR) is to reduce the transient engine pumping work through improved EGR-VGT control. Numerical dynamic programming is applied to investigate optimal EGR-VGT control policies for reduced pumping work on a three-state model of a 6.7-liter medium-duty diesel engine. Optimality is defined by a multi-objective cost function that penalizes pumping work, EGR rate control error, and boost pressure control error. Multiple dynamic programs, each with a different set of cost function weights, are performed over an acceleration in the Heavy-Duty Federal Test Procedure cycle to generate the optimal trade-off between the stated objectives. Additionally, a production-representative EGR-VGT controller is simulated, and the resulting suboptimal performance is compared to the optimal frontier to establish the potential fuel consumption benefit of improved EGR-VGT control.

Automotive Transmission Clutch Fill Control Using a Customized Dynamic Programming Method

Clutch to clutch shift control technology, which is the key enabler for a compact and low cost transmission design, is important for both automatic and hybrid transmissions. To ensure a smooth clutch to clutch shift, precise synchronization between the on-coming and off-going clutches is critical. This further requires the on-coming clutch to be filled and ready for engagement at the predetermined time. Due to the compact design, currently there is no pressure sensor inside the clutch chamber, and therefore the clutch fill can only be controlled in an open loop fashion. The traditional clutch fill approach, by which the clutch fill input pressure command is manually calibrated, has a couple of limitations. First, the pressure profile is not optimized to reduce the peak flow demand during clutch fill. Moreover, it is not systematically designed to account for uncertainties in the system, such as variations of solenoid valve delay and parameters of the clutch assembly. In this paper, we present a systematic approach to evaluate the clutch fill dynamics and synthesize the optimal pressure profile. First, a clutch fill dynamic model, which captures the key dynamics in the clutch fill process, is constructed and analyzed. Second, the applicability of the conventional numerical dynamic programming (DP) method to the clutch fill control problem, which has a stiff dynamic model, is explored and shown to be ineffective. Thus, we proposed a customized DP method to obtain the optimal and robust pressure profile subject to specified constraints. The customized DP method not only reduces the computational burden significantly, but also improves the accuracy of the result by eliminating the interpolation errors. To validate the proposed method, a transmission clutch fixture has been designed and built in the laboratory. Both simulation and experimental results demonstrate that the proposed customized DP approach is effective, efficient and robust for solving the clutch fill optimal control problem.

Transmission Clutch Fill Control Using a Customized Dynamic Programming Method

Clutch fill control is critical for automotive transmission performance and fuel economy, including both automatic and hybrid transmissions. The traditional approach, by which the clutch fill pressure command is manually calibrated, has a couple of limitations. First, the pressure profile is not optimized to reduce the peak clutch fill flow demand. Moreover, it is not systematically designed to account for uncertainties in the system, such as variations of solenoid valve time delay and parameters of the clutch assembly. In this paper, we present a systematic approach to evaluate the clutch fill dynamics and synthesize the optimal pressure profile. First, a clutch fill dynamic model is constructed and analyzed. Second, the applicability of the conventional numerical Dynamic Programming (DP) algorithm to the clutch fill control problem is explored and shown to be ineffective. Thus we developed a new customized DP method to obtain the optimal and robust pressure profile subject to specified constraints. After a series of simulations and case studies, the new customized DP approach is demonstrated to be effective, efficient, and robust for solving the clutch fill optimal control problem.

Optimal beef cattle management under agricultural policy reforms in Finland

The supply for domestic beef has been decreasing sharply since Finland joined the European Union (EU) because profitability of beef production has been low. The goal of this study is to search for optimal beef management practices that increase returns on beef production in Finland. Numerical dynamic programming (DP) is used to simultaneously optimise feeding and timing of slaughtering. The DP-algorithm is solved under alternative subsidy, output price, and silage price scenarios. At 1998 prices and subsidies, the optimal carcass weight is estimated above 250 kg. The European Agenda 2000 reform is predicted to decrease the optimal carcass weight to 200 kg, which is 50 kg lower than under the 1998 prices and subsidies. This reform will increase farmer returns significantly and its income effect depends crucially on the price of silage. The results indicate also that the reform will result in adjustment of feeding. Particularly, farmers having high silage production costs will substitute feed concentrates for roughage in the diet. A farmer is entitled to a premium subsidy of FIM 200 (e33.63), provided that the carcass weight of a culled animal exceeds 270 kg. But when the Agenda 2000 reform is fully implemented, this premium subsidy is not large enough to supply carcasses heavier than 270 kg. The results suggest that carcass weights of at least 270 kg would require a premium subsidy of FIM 400-800 (e67-134). Rearing heavy animals will significantly increase production costs and, therefore, most of the subsidy will be taken away from the farm in terms of increased costs.;