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Diesel Particulate Filters (DPF) are a key component in many on- and off-road aftertreatment systems to meet increasingly stringent particle emissions limits. Efficient thermal management and regeneration control is critical for reliable and cost-effective operation of the combined engine and aftertreatment system. Conventional DPF control systems predominantly rely on a combination of filter pressure drop measurements and predictive models to indirectly estimate the soot loading state of the filter. Over time, the build-up of incombustible ash, primarily derived from metal-containing lubricant additives, accumulates in the filter to levels far exceeding the DPF's soot storage limit. The combined effects of soot and ash build-up dynamically impact the filter's pressure drop response, service life, and fuel consumption, and must be accurately accounted for in order to optimize engine and aftertreatment system performance.

Efficient aftertreatment management requires accurate sensing of both particulate filter soot and ash levels for optimized feedback control. Currently a combination of pressure drop measurements and predictive models are used to indirectly estimate the loading state of the filter. Accurate determination of filter soot loading levels is challenging under certain operating conditions, particularly following partial regeneration events and at low flow rate (idle) conditions. This work applied radio frequency (RF)-based sensors to provide a direct measure of the particulate filter soot levels in situ. Direct measurements of the filter loading state enable advanced feedback controls to optimize the combined engine and aftertreatment system for improved DPF management. This study instrumented several cordierite and aluminum titanate diesel particulate filters with RF sensors. The systems were tested on a range of light- and heavy-duty applications, which included on- and off-road engines.

Past experimental studies conducted by the current authors on a 13 liter 16.7:1 compression ratio heavy-duty diesel engine have shown that diesel-Compressed Natural Gas (CNG) Reactivity Controlled Compression Ignition (RCCI) combustion targeting low NOx emissions becomes progressively difficult to control as the engine load is increased. This is mainly due to difficulty in controlling reactivity levels at higher loads. For the current study, CFD investigations were conducted in CONVERGE using the SAGE combustion solver with the application of the Rahimi mechanism. Studies were conducted at a load of 5 bar BMEP to validate the simulation results against RCCI experimental data. In the low load study, it was found that the Rahimi mechanism was not able to predict the RCCI combustion behavior for diesel injection timings advanced beyond 30 degCA bTDC. This poor prediction was found at multiple engine speed and load points.

A novel approach to the Integration of Turbocompounding/WHR, Electrification and Supercharging technologies (ITES) to reduce fuel consumption in a medium heavy-duty diesel engine was previously published by FEV. This paper describes a modified approach to ITES to reduce fuel consumption on a heavy-duty diesel engine applied in a Class 8 tractor. The original implementation of the ITES incorporated a turbocompound turbine as the means for waste heat recovery. In this new approach, the turbocompound unit connected to the sun gear of the planetary gear set has been replaced by an organic Rankine cycle (ORC) turbine expander. The secondary compressor and the electric motor-generator are connected to the ring gear and the carrier gear respectively. The ITES unit is equipped with dry clutch and band brake allowing flexibility in mechanical and electrical integration of the ORC expander, secondary compressor and electric motor-generator to the engine.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

The Environmental Protection Agency (EPA) and California Air Resources Board (CARB) have recently announced rulemakings focused on tighter emission limits for oxides of nitrogen (NOx) from heavy-duty trucks. As part of the new rulemaking CARB has proposed a Low Load Cycle (LLC) to specifically evaluate NOx emission performance over real-world urban and vocational operation typically characterized by low engine loads, thereby demanding the implementation of continuous active thermal management of the engine and aftertreatment system. This significant drop in NOx levels along with continued reduction in the Green House Gas (GHG) limits poses a more significant challenge for the engine developer as the conventional emission reduction approaches for one species will likely result in an undesirable increase in the other species.

Although designed for the purpose of reducing engine-out Particulate Matter (PM) emissions to meet or exceed mandated emissions regulations, the particulate filter also incurs a fuel economy penalty. This fuel penalty is due to the increased exhaust flow restriction attributed to the PM accumulated in the filter, in addition to fuel consumed for active regeneration. Unlike the soot which may be oxidized through the regeneration process, incombustible material or ash continues to build-up in the filter following each regeneration event. Currently pressure- and model-based controls are used to provide an indirect estimate of the loading state of the particulate filter, in order to manage the filter operation and determine when to regenerate the filter. The challenges associated with pressure- and model-based particulate filter control over real-world operating conditions are well-known.