Search Results

As vehicle emissions regulations become increasingly stringent, there is a growing need to accurately model aftertreatment systems to aid in the development of ultra-low NOx vehicles. Common solutions to this problem include the development of complex chemical models or expansive neural networks. This paper aims to present the development process of a simpler Selective Catalytic Reduction (SCR) conversion efficiency Simulink model for the purposes of modeling tail pipe NOx emission levels based on various inputs, temperature shifts and SCR locations, arrangements and/or sizes in the system. The main objective is to utilize this model to predict tail pipe NOx emissions of the EPA Federal Test Procedures for heavy-duty vehicles. The model presented within is focused exclusively on heavy-duty application compression ignition engines and their corresponding aftertreatment setups.

The need of upfront modeling, simulation and design optimization has been ever increasing during full vehicle product development process. The overall vehicle system and component subsystem performances remain critical considerations for making final product release decision. With these challenges in mind, the work of this paper discusses the development of feasible CAE methods, tools, and processes for multi-objective design optimization. A full integrated tractor trailer truck vehicle is used as an example to demonstrate this capability. The proposed approach allows several design objectives to be simultaneously optimized, which might otherwise be extremely difficult to achieve with experimental methods.

Efficient thermal management of an engine cooling system and its surrounding components has been one of the most frequently visited topics in automotive industry. Especially, modern diesel truck engines have to deal with more heat rejection than ever to meet the rigorous emission and efficiency standards. As the maximum heat dissipated by a cooling system is limited to available inlet area to radiator, which is constrained by cab configuration, it is crucial to maximize the cooling airflow availability under given conditions. To be able to do so means to avoid additional development cost accompanying an altered cab configuration. At the same time, truck manufacturers have to deal with reduced product life cycles and develop reliable products economically.

With the emission norms becoming more and more stringent along with the focus on reducing ownership and operating costs, the need to optimize the aftertreatment system becomes much more evident. Thus, the well monitored, optimized usage of urea or ammonia (NH₃) for the NOx reduction in an SCR system is critical to reduce the operating cost of the vehicles and to comply with emission regulations. In Ammonia Storage and Delivery System (ASDS), pure gaseous NH₃ from the NH₃ cartridges is being used for the reduction of the engine-out NOx in the exhaust stream over the NPF (NOx Particulate Filter). In almost all NOx reduction systems, NOx sensors play an important role in determining the amount of urea or NH₃ to be dosed for efficient NOx reduction with minimal NH₃ consumption and slippage for best possible fluid economy.

Tighter emission norms and fuel economy demands have prompted diesel engine manufacturers to implement Aftertreatment systems for both light-duty and heavy-duty diesel applications. After implementing Diesel Particulate Filter (DPF) technology to comply with 2007 Environmental Protection Agency (EPA) emissions regulations, OEMs have turned their attention towards NOx reductions with SCR technology. Current SCR technologies include liquid based Urea injection into the exhaust stream for NOx reductions and Solid Ammonia Storage and Delivery System (ASDS) which involves dosing gaseous Ammonia. Irrespective of the technology in use, the estimation of engine-out NOx emissions plays a vital role in reductant (Urea/Ammonia) dosing estimation via feed-back controls. The general method for determination of the engine-out NOx emissions is to use commonly available NOx emission sensors (NOx Sensors). However, NOx sensors have their own drawbacks.

Low temperature combustion through in-cylinder blending of gasoline and diesel offers the potential to improve engine efficiency while yielding low engine-out soot and NOx emissions. This investigation utilized 3-D KIVA combustion simulation to guide the development of viable dual-fuel low temperature combustion strategies for heavy-duty applications. Model-based combustion optimization was performed at 1531rpm and 11 bar BMEP for a 12.4 L heavy-duty truck engine. Various engine operating parameters were explored through design of experiments (DoE). The parameters involved in the optimization process included compression ratio, air-fuel ratio, EGR rate, gasoline-to-diesel ratio, and diesel injection strategy (i.e., single-diesel injection vs. two-diesel injections, diesel injection timings, and the split ratio between two-diesel injections). Optimal cases showed near zero soot emissions and very low NOx emissions.