Optimization of Diesel Engine and After-treatment Systems for a Series Hybrid Forklift Application 2020-01-0658

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle. In addition, the thermal management strategies like retarding injection timing and late post-injection of fuel during cold start are analyzed in this work. The results show the reduction of tailpipe- NO_x emission is possible by properly retarding the injection timing without a significant effect on unburned hydrocarbon emissions.

The designed series hybrid powertrain uses a heuristic-based controller to define different modes of operation. The performance of powertrain is then evaluated in the VDI 2198 cycle. The energy flows from the battery and the engine fuel consumption are optimized to overcome the rolling resistance and lifting hydraulic load in an energy-efficient way. The energy recuperation possibility in the forklift application is high as it consists of intermittent peak loads in the VDI cycle. The simulation results show that the designed series hybrid powertrain forklift can save fuel up to 20% compared to forklifts with conventional powertrain operating in the VDI 2198 cycle. In addition, the operational cost of the after-treatment system is reduced by 19.8%.