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Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

Higher compression ratio and turbocharging, with engine downsizing can enable significant gains in fuel economy but require engine operating conditions that cause engine knock under high load. Engine knock can be avoided by supplying higher-octane fuel under such high load conditions. This study builds on previous MIT papers investigating Octane-On-Demand (OOD) to enable a higher efficiency, higher-boost higher compression-ratio engine. The high-octane fuel for OOD can be obtained through On-Board-Separation (OBS) of alcohol blended gasoline. Fuel from the primary fuel tank filled with commercially available gasoline that contains 10% by volume ethanol (E10) is separated by an organic membrane pervaporation process that produces a 30 to 90% ethanol fuel blend for use when high octane is needed. In addition to previous work, this paper combines modeling of the OBS system with passenger car and medium-duty truck fuel consumption and octane requirements for various driving cycles.

Ground impact caused by road debris can result in very severe fire accident of Electric Vehicles (EV). In order to study the ground impact accidents, a Finite Element model of the battery pack structure is carefully set up according to the practical designs of EVs. Based on this model, the sequence of the deformation process is studied, and the contribution of each component is clarified. Subsequently, four designs, including three enhanced shield plates and one enhanced housing box, are investigated. Results show that the BRAS (Blast Resistant Adaptive Sandwich) shield plate is the most effective structure to decrease the deformation of the battery cells. Compared with the baseline case, which adopts a 6.35-mm-thick aluminum sheet as the shield plate, the BRAS can reduce the shortening of cells by more than 50%. Another type of sandwich structure, the NavTruss, can also improve the safety of battery pack, but not as effectively as the BRAS.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

Battery packs for Hybrids, Plug-in Hybrids, and Electric Vehicles are assembled from a system of modules (sheets) with a tight sheet metal casing around them. Each module consists of an array of individual cells which vary in the composition of electrodes and separator from one manufacturer to another. In this paper a general procedure is outlined on the development of a constitutive and computational model of a cylindrical cell. Particular emphasis is placed on correct prediction of initiation and propagation of a tearing fracture of the steel can. The computational model correctly predicts rupture of the steel can which could release aggressive chemicals, fumes, or spread the ignited fire to the neighboring cells. The initiation site of skin fracture depends on many factors such as the ductility of the casing material, constitutive behavior of the system of electrodes, and type of loading.

The NOx emission and knock characteristics of a PFI engine operating on ethanol/gasoline mixtures were assessed at 1500 and 2000 rpm with λ =1 under Wide-Open-Throttle condition. There was no significant charge cooling due to fuel evaporation. The decrease in NOx emission and exhaust temperature could be explained by the change in adiabatic flame temperature of the mixture. The fuel knock resistance improved significantly with the gasohol so that ignition could be timed at a value much closer or at MBT timing. Changing from 0% to 100% ethanol in the fuel, this combustion phasing improvement led to a 20% increase in NIMEP and 8 percentage points in fuel conversion efficiency at 1500 rpm. At 2000 rpm, where knocking was less severe, the improvement was about half (10% increase in NIMEP and 4 percentage points in fuel conversion efficiency).

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

This paper illustrates a methodology in which complete material-manufacturing process cases for closure panels, reinforcements, and assembly are modeled and compared in order to identify the preferred option for a lightweight closure design. First, process-based cost models are used to predict the cost of lightweighting the closure set of a sample midsized sports utility vehicle (SUV) via material and process substitution. Weight savings are then analyzed using a powertrain simulation to understand the impact of lightweighting on fuel economy. The results are evaluated in the context of production volume and total mass change.

This paper assesses the potential improvement of automotive powertrain technologies 25 years into the future. The powertrain types assessed include naturally-aspirated gasoline engines, turbocharged gasoline engines, diesel engines, gasoline-electric hybrids, and various advanced transmissions. Advancements in aerodynamics, vehicle weight reduction and tire rolling friction are also taken into account. The objective of the comparison is the potential of anticipated improvements in these powertrain technologies for reducing petroleum consumption and greenhouse gas emissions at the same level of performance as current vehicles in the U.S.A. The fuel consumption and performance of future vehicles was estimated using a combination of scaling laws and detailed vehicle simulations. The results indicate that there is significant potential for reduction of fuel consumption for all the powertrains examined.

The US Air Force (USAF) has evolved a policy for the acquisition of fighter jet engines (FJE). In the 1970s and 1980s that policy placed a premium on FJE performance primarily measured by the metric: thrust/engine weight. In the 1990s, the USAF policy changed from an emphasis on performance to reduced life-cycle cost with a premium on sustainment. This paper reports the results of a study of how the USAF and Corporation Alpha (Alpha) have adapted their processes, practices, and policies to design, develop, manufacture, test, and sustain a family of FJEs. Each member of the family of FJEs is sequentially linked relative to insertion of technology designed to reduce sustainment costs. In addition to the technology linkages, the development of the family of FJEs selected for this case study is also tracked relative to US Department of Defense and USAF policy and industry design, build, and maintain processes, methods, and tools.

A new monitoring system predicts the progression of welding temperature fields during resistance spot welding. The system captures welding voltages and currents to predict contact diameters and simulate temperature fields. The system accurately predicts fusion lines and heat-affected zones. Accuracy holds even for electrode tips used for a few thousand welds of zinc coated steels.

The transportation sector in the United States is a major contributor to global energy consumption and carbon dioxide emission. To assess the future potentials of different technologies in addressing these two issues, we used a family of simulation programs to predict fuel consumption for passenger cars in 2020. The selected technology combinations that have good market potential and could be in mass production include: advanced gasoline and diesel internal combustion engine vehicles with automatically-shifting clutched transmissions, gasoline, diesel, and compressed natural gas hybrid electric vehicles with continuously variable transmissions, direct hydrogen, gasoline and methanol reformer fuel cell hybrid electric vehicles with direct ratio drive, and battery electric vehicle with direct ratio drive.

Many vehicle-dynamics models exist to study the motion of a vehicle. Most of these models fall into one of two categories: very simple models for basic analyses and high-order models consisting of many degrees-of-freedom. For many scenarios, the simple models are not adequate. At the same time, for many vehicle handling and braking studies, the high-order models are more complex than necessary. This paper presents a model that includes the dynamics that are relevant to studying vehicle handling and braking, but is still simple enough to run in near real-time. The model was implemented in such a way that it is easily customized for a particular study. Predictions from this simplified model were compared against a high-order model and against actual vehicle test data. The simulations indicate a close agreement in the results.

A one-dimensional model for crevice HC post-flame oxidation is used to calculate and understand the effect of operating parameters and fuel type (propane and isooctane) on the extent of crevice hydrocarbon and the product distribution in the post flame environment. The calculations show that the main parameters controlling oxidation are: bulk burned gas temperatures, wall temperatures, turbulent diffusivity, and fuel oxidation rates. Calculated extents of oxidation agree well with experimental values, and the sensitivities to operating conditions (wall temperatures, equivalence ratio, fuel type) are reasonably well captured. Whereas the bulk gas temperatures largely determine the extent of oxidation, the hydrocarbon product distribution is not very much affected by the burned gas temperatures, but mostly by diffusion rates. Uncertainties in both turbulent diffusion rates as well as in mechanisms are an important factor limiting the predictive capabilities of the model.

The linked cell system gives both reduced cost and volume flexibility. The characteristics of the linked cell system are a consequence of decoupling the operators from the machines, using standard work in process between the cells and by integrating the information system with the cell and system design. By decoupling the operators from the machines the capacity can be increased/decreased in small increments by using more or fewer operators in the cell. The information system is integrated with the linked cell design by the use of a Heijunka box. The Heijunka is used to level production and to initiate the pace of production as a result of pulling withdrawal kanban at a standard time interval. This standard time interval is called the pitch of production. The kanban cards give information about what to produce, when to produce, when to make changeovers but they also give information to control the material replenishment.

This paper describes the use of a modified quasi-dimensional spark-ignition engine simulation code to predict the extent of cycle-to-cycle variations in combustion. The modifications primarily relate to the combustion model and include the following: 1. A flame kernel model was developed and implemented to avoid choosing the initial flame size and temperature arbitrarily. 2. Instead of the usual assumption of the flame being spherical, ellipsoidal flame shapes are permitted in the model when the gas velocity in the vicinity of the spark plug during kernel development is high. Changes in flame shape influence the flame front area and the interaction of the enflamed volume with the combustion chamber walls. 3. The flame center shifts due to convection by the gas flow in the cylinder. This influences the flame front area through the interaction between the enflamed volume and the combustion chamber walls. 4. Turbulence intensity is not uniform in cylinder, and varies cycle-to-cycle.

Enhanced information management techniques made available through emerging Information Technology platforms hold a promise of providing significant improvements in both the effectiveness and efficiency of developing complex products. Determining actual management implementations that deliver on this promise has often proven elusive. Work in conjunction with the Lean Aircraft Initiative at MIT has revealed a straight forward use of Information Technology that portends significant cost reductions. By integrating previously separate types of data involved in the process of product development, engineers and designers can make decisions that will significantly reduce ultimate costs. Since the results presented are not specific to particular technologies or manufacturing processes, the conclusions are broadly applicable.

Currently, states that are out of compliance with the National Ambient Air Quality Standards must, according to the Clean Air Act Amendments of 1990 (CAAA), develop and implement control strategies that demonstrate specific degrees of reduction in emissions-with the degree of reduction depending upon the severity of the problem. One tool that has been developed to aid regulators in both deciding an appropriate course of action and to demonstrate the desired reductions in mobile emissions is EPA's Mobile 5a emission estimation model. In our study, Mobile 5a has been used to examine the effects of regulatory strategies, as applied to the Northeast United States, on vehicle emissions under worst-case ozone-forming conditions.