Daniel Christopher Bitsis’s research while affiliated with Southwest Research Institute and other places

div class="section abstract"> Battery electric vehicles (BEVs) are well-suited for many passenger vehicle applications, but high cost, short range, and long recharging times have limited their growth in commercial vehicle markets. These constraints can be eliminated with plug-in hybrid electric vehicles (PHEVs) which combine many benefits of BEVs with those of conventional vehicles. In this study, research was conducted to determine the optimal hybrid electric powertrain system for a Class 3, light duty commercial vehicle. The key technologies used in this hybrid powertrain include engine downsizing, P3 architecture hybridization, and active thermal management of aftertreatment. A vehicle cost of ownership analysis was conducted to determine the economic viability, a very important consideration for commercial vehicles. Several combinations of E-motor and battery pack sizes were evaluated during the cost analysis and the best possible configuration was determined. The resulting vehicle powertrain demonstrated ~60% reduction in CO₂ over the World Harmonized Light Duty Transient Cycle (WLTC) and Federal Transient Procedure (FTP75) test cycles compared to the baseline internal combustion engine (ICE) vehicle. The NO_X emissions were also evaluated during those test cycles, and the test results indicated that intermittent engine operation associated with Plug-in Hybrid Electric Vehicle (PHEV) operation, can result in higher NO_X emissions. Advanced aftertreatment thermal management strategies are required to reduce NO_X emissions in PHEVs. Finally, an exhaust heater was used to reduce tailpipe (TP) NO_X emissions, and a pathway for even lower NO_X emissions is identified. </div

div class="section abstract"> In this study, engine-out gaseous emissions are reviewed using the Fourier Transform Infrared (FTIR) spectroscopy measurement of methanol diesel dual fuel combustion experiments performed in a heavy-duty diesel engine. Comparison to the baseline diesel-only condition shows that methanol-diesel dual fuel combustion leads to higher regulated carbon monoxide (CO) emissions and unburned hydrocarbons (UHC). However, NO_X emissions were reduced effectively with increasing methanol substitution rate (MSR). Under dual-fuel operation with methanol, emissions of nitrogen oxides (NO_X), including nitric oxide (NO), nitrogen dioxide (NO₂), and nitrous oxide (N₂O), indicate the potential to reduce the burden of NO_X on diesel after-treatment devices such as selective catalytic reduction (SCR). Other unregulated gaseous emissions, such as formaldehyde (CH₂O) methane (CH₄), increased with higher MSR, but their emissions can be mitigated if advanced injection timing or increased intake temperature is used as reported in our separate study. In summary, this study suggests the potential use of methanol as a low-carbon fuel (LCF) to meet emissions regulations but indicates a slight increase in emissions of unregulated species. </div

div class="section abstract"> In this work, a novel bearing test rig was used to evaluate the impact of oil viscoelasticity on friction torque and oil film thickness in a hydrodynamic journal bearing. The test rig used an electric motor to rotate a test journal, while a hydraulic actuator applied radial load to the connecting rod bearing. Lubrication of the journal bearing was accomplished via a series of axial and radial drillings in the test shaft and journal, replicating oil delivery in a conventional engine crankshaft. Journal bearing inserts from a commercial, medium duty diesel engine (Cummins ISB) were used. Oil film thickness was measured using high precision eddy current sensors. Oil film thickness measurements were taken at two locations, allowing for calculation of minimum oil film thickness. A high-precision, in-line torque meter was used to measure friction torque. Four test oils were prepared and evaluated. The first was a monograde, Newtonian oil, while the remaining three oils were multigrade oils having varying levels of viscoelasticity. Importantly, each test oil was carefully blended to ensure similar kinematic and high temperature high shear viscosities, isolating viscoelasticity as the only variable. Viscoelasticity was quantified as Trouton ratio (ratio of extensional to shear viscosity), and ranged from approximately 64 to 162, for the viscoelastic oils. Results for bearing friction and oil film thickness are presented at various operating speeds and loads. All multigrade oils were observed to produce lower friction torque compared to the monograde baseline. Among the multigrade oils, minimum oil film thickness was observed to increase with increasing viscoelasticity. However, only a single multigrade oil (highest viscoelasticity) resulted in a larger minimum oil film thickness compared to the monograde baseline. </div

Meeting regulatory and customer demands requires detailed powertrain calibration which can be expensive and time-consuming. There is often a reliance on mathematical optimization tools to convert experimental learnings into a final calibration. This work focuses on developing multiple neural network machine learning (ML) models which were trained on different test-train data splits of test-cell recorded steady-state medium-duty (MD) diesel engine data. The output data was used to develop engine actuator maps by utilizing a genetic algorithm (GA). The genetic algorithm contains a fitness function which was varied to target different combinations of low NOx and CO2 emissions. The input variables used for the ML model were engine speed, engine torque, fuel rail pressure, exhaust gas recirculation (EGR) valve command, main injection timing, and wastegate valve command. The output variables predicted were NOx mass flow rate, exhaust temperature, fuel flow rate, and dry intake mass flow rate. The ML models were used to predict cycle-averaged engine-out emissions and time-series predictions of all output variables for different transient drive cycles. The drive cycles used for this case were the Heavy-Duty Federal Test Procedure (HDFTP) transient cycle, the Non-Road Transient Cycle (NRTC), the Ramped Mode Cycle (RMC) and the newly proposed on-road Low-Load Cycle (LLC).