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Effectively obtaining physical parameters for vehicle dynamic model is the key to successfully performing any computer-based dynamic analysis, control strategy development or optimization. For a spring and lump mass vehicle model, which is a type of vehicle model widely used, its physical parameters include sprung mass, unsprung mass, inertial properties of the sprung mass, stiffness and damping coefficient of suspension and tire, etc. To minimize error, the paper proposes a method to estimate these parameters from vehicle modal parameters which are in turn obtained through full-car dynamic testing. To verify its effectiveness, a visual vehicle with a set of given parameters, build in the Adams(Automatic Dynamic Analysis of Mechanical Systems)/Car environment, is used to perform the dynamic testing and provide the testing data for the parameter estimation.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

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.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Automobile industry is facing the great challenge of energy conservation and emission reduction. It's necessary to do some researches on some surface components of a car body to find out which of them may affect aerodynamic drag remarkably. This will help an aerodynamic engineer modify an initial car model more clearly. We also hope to reduce the cost during the process, including time and resources. In this paper, with the purpose of developing an aerodynamic shape optimization process and realizing its automation, a MIRA reference car model was studied and three commercial softwares were integrated-Altair HyperStudy, HyperMesh and CD-adapco STAR-CCM+. The optimization strategy in this paper was: firstly, a DOE (design of experiment) matrix, which contained four design factors and thirty levels was created. The baseline model was morphed according to the DOE matrix. Then the morphed model's aerodynamic drag coefficient (Cd) and lift coefficient (Cl) were calculated via CFD software.

To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.