Predictive Model of Adaptive Cruise Control Speed to Enhance Engine Operating Conditions

This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP’s indicates that the predictive model outperforms constant ACCSSP.

Predictive Model of ACC Speed to Enhance Engine Operating Conditions

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

Coupled Engine-Propeller Selection Procedure to Minimize Fuel Consumption at a Specified Speed

This study presents a practical optimization procedure that couples the NavCad power prediction tool and a nonlinear optimizer integrated into the Matlab environment. This developed model aims at selecting a propeller at the engine operating point with minimum fuel consumption for different ship speeds in calm water condition. The procedure takes into account both the efficiency of the propeller and the specific fuel consumption of the engine. It is focused on reducing fuel consumption for the expected operational profile of the ship, contributing to energy efficiency in a complementary way as ship routing does. This model assists the ship and propeller designers in selecting the main parameters of the geometry, the operating point of a fixed-pitch propeller from Wageningen B-series and to define the gearbox ratio by minimizing the fuel consumption of a container ship, rather than only maximizing the propeller efficiency. Optimized results of the performance of several marine propellers with different number of blades working at different cruising speeds are also presented for comparison, while verifying the strength, cavitation and noise issues for each simulated case.

Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a, and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%; compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

Optimization and Uncertainty Analysis of a Diesel Engine Operating Point Using Computational Fluid Dynamics

This study describes the use of an analytical model, constructed using sequential design of experiments (DOEs), to optimize and quantify the uncertainty of a diesel engine operating point. A genetic algorithm (GA) was also used to optimize the design. Three engine parameters were varied around a baseline design to minimize indicated specific fuel consumption without exceeding emissions (NOx and soot) or peak cylinder pressure (PCP) constraints. An objective merit function was constructed to quantify the strength of designs. The engine parameters were start of injection (SOI), injection duration, and injector included angle. The engine simulation was completed with a sector mesh in the commercial computational fluid dynamics (CFD) software CONVERGE, which predicted the combustion and emissions using a detailed chemistry solver with a reduced mechanism for n-heptane. The analytical model was constructed using the SmartUQ software using DOE responses to construct kernel emulators of the system. Each emulator was used to direct the placement of the next set of DOE points such that they improve the accuracy of the subsequently generated emulator. This refinement was either across the entire design space or a reduced design space that was likely to contain the optimal design point. After sufficient emulator accuracy was achieved, the optimal design point was predicted. A total of five sequential DOEs were completed, for a total of 232 simulations. A reduced design region was predicted after the second DOE that reduced the volume of the design space by 96.8%. The final predicted optimum was found to exist in this reduced design region. The sequential DOE optimization was compared to an optimization performed using a GA. The GA was completed using a population of nine and was run for 71 generations. This study highlighted the strengths of both methods for optimization. The GA (known to be an efficient and effective method) found a better optimum, while the DOE method found a good optimum with fewer total simulations. The DOE method also ran more simulations concurrently, which is an advantage when sufficient computing resources are available. In the second part of the study, the analytical model developed in the first part was used to assess the sensitivity and robustness of the design. A sensitivity analysis of the design space around the predicted optimum showed that injection duration had the strongest effect on predicted results, while the included angle had the weakest. The uncertainty propagation was studied over the reduced design region found with the sequential DoE in the first part. The uncertainty propagation results demonstrated that for the relatively large variations in the input parameters, the expected variation in the indicated specific fuel consumption and NOx results were significant. Finally, the predictions from the analytical model were validated against CFD results for sweeps of the input parameters. The predictions of the analytical model were found to agree well with the results from the CFD simulation.

Aerodynamic Optimization of the High Pressure Turbine and Interstage Duct in a Two-Stage Air System for a Heavy-Duty Diesel Engine

Turbochargers reduce fuel consumption and CO2 emissions from heavy-duty internal combustion engines by enabling downsizing and downspeeding through greater power density. This requires greater pressure ratios and thus air systems with multiple stages and interconnecting ducting, all subject to tight packaging constraints. This paper considers the aerodynamic optimization of the exhaust side of a two-stage air system for a Caterpillar 4.4 l heavy-duty diesel engine, focusing on the high pressure turbine (HPT) wheel and interstage duct (ISD). Using current production designs as a baseline, a genetic algorithm (GA)-based aerodynamic optimization process was carried out separately for the wheel and duct components to evaluate seven key operating points. While efficiency was a clear choice of cost function for turbine wheel optimization, different objectives were explored for ISD optimization to assess their impact. Optimized designs are influenced by the engine operating point, so each design was evaluated at every other engine operating point, to determine which should be carried forward. Prototypes of the best compromise high pressure turbine wheel and ISD designs were manufactured and tested against the baseline to validate computational fluid dynamics (CFD) predictions. The best performing high pressure turbine design was predicted to show an efficiency improvement of 2.15% points, for on-design operation. Meanwhile, the optimized ISD contributed a 0.2% and 0.5% point efficiency increase for the HPT and low pressure turbine (LPT), respectively.

Conceptual Design of an Axial Turbocharger Turbine

Despite engine turbocharging being a widespread technology, there are still drawbacks present in current turbocharging systems stemming from the apparent mismatch between the periodic operation of a piston engine operating in conjunction with an essentially steady-state, rotordynamic machine (turbocharger). The primary issue remains the provision of adequate transient response thereby suppressing the issue of turbocharger lag (turbo-lag) or the poor initial response of the turbocharger to driver-commanded, engine operating point changes due to its inertia. Another problem is engine-turbocharger matching and operation under pulsating conditions in the exhaust manifold and generally unsteady engine operating conditions. The exhaust flows of internal combustion engines are characterized by pulsating flows at constant engine speeds (local pulsating effect) as well as “global” unsteadiness during engine transient events. Because of the volute volume and the length of the flow path, this unsteadiness generates a phase shift between mass flow, temperature and pressure at rotor inlet, and a stronger circumferential variation of the rotor inlet condition than in steady flow conditions. The shift and the variation increase the losses in the turbine, resulting in lower turbine efficiency. The current paper develops original concept work carried out at Brunel University London to develop an innovative fluid-dynamic design for an axial turbine for turbocharger application. An axial flow turbine coupled with a specially-designed, outflow volute, arranged in a non-classical way, are the target of this work. CFD analysis and 1D simulation of an engine coupled with the innovative turbine have been performed to highlight the design potential.

Optimization and Uncertainty Analysis of a Diesel Engine Operating Point Using CFD

This study describes the use of an analytical model, constructed using sequential design of experiments (DOEs), to optimize and quantify the uncertainty of a Diesel engine operating point. A genetic algorithm (GA) was also used to optimize the design. Three engine parameters were varied around a baseline design to minimize indicated specific fuel consumption (ISFC) without exceeding emissions (NOx and soot) or peak cylinder pressure constraints. An objective merit function was constructed to quantify the strength of designs. The engine parameters were start of injection (SOI), injection duration, and injector included angle. The engine simulation was completed with a sector mesh in the commercial computational fluid dynamics (CFD) software CONVERGE, which predicted the combustion and emissions using a detailed chemistry solver with a reduced mechanism for n-heptane. The analytical model was constructed using the SmartUQ software using DOE responses to construct kernel emulators of the system. Each emulator was used to direct the placement of the next set of DOE points such that they improve the accuracy of the subsequently generated emulator. This refinement was either across the entire design space or a reduced design space that was likely to contain the optimal design point. After sufficient emulator accuracy was achieved, the optimal design point was predicted. A total of 5 sequential DOEs were completed, for a total of 232 simulations. A reduced design region was predicted after the second DOE that reduced the volume of the design space by 96.8%. The final predicted optimum was found to exist in this reduced design region. The sequential DOE optimization was compared to an optimization performed using a GA. The GA was completed using a population of 9 and was run for 71 generations. This study highlighted the strengths of both methods for optimization. The GA (known to be an efficient and effective method) found a better optimum, while the DOE method found a good optimum with fewer total simulations. The DOE method also ran more simulations concurrently, which is an advantage when sufficient computing resources are available. In the second part of the study, the analytical model developed in the first part was used to assess the sensitivity and robustness of the design. A sensitivity analysis of the design space around the predicted optimum showed that injection duration had the strongest effect on predicted results, while the included angle had the weakest. The uncertainty propagation was studied over the reduced design region found with the sequential DoE in the first part. The uncertainty propagation results demonstrated that for the relatively large variations in the input parameters, the expected variation in the ISFC and NOx results were significant. Finally, the predictions from the analytical model were validated against CFD results for sweeps of the input parameters. The predictions of the analytical model were found to agree well with the results from the CFD simulation.

Comparison of the Aerodynamics of Acoustically Designed Exit Guide Vanes and a State-of-the-Art Exit Guide Vane

Within previous EU projects, possible modifications to the engine architecture have been investigated, which would allow for an optimized aerodynamic or acoustic design of the exit guide vanes (EGVs) of the turbine exit casing (TEC). However, the engine weight should not be increased and the aerodynamic performance must be at least the same. This paper compares a state-of-the art TEC (reference TEC) with typical EGVs with an acoustically optimized TEC configuration for the engine operating point approach. It is shown that a reduction in sound power level for the fundamental tone (one blade passing frequency (BPF)) for this acoustically important operating point can be achieved. It is also shown that the weight of the acoustically optimized EGVs (only bladings considered) is almost equal to the reference TEC, but a reduction in engine length can be achieved. Measurements were conducted in the subsonic test turbine facility (STTF) at the Institute for Thermal Turbomachinery and Machine Dynamics, Graz University of Technology. The inlet guide vanes (IGVs), the low pressure turbine (LPT) stage, and the EGVs have been designed by MTU Aero Engines.