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This investigation utilizes energy analysis and statistical methods to optimize step gear automatic transmissions gear selection for fuel consumption. A full factorial matrix of simulations using energy analysis was performed to determine the optimal number of gears and gear ratios that provide the best fuel consumption performance for a particular vehicle - engine application. The full factorial matrix setup as a design of experiment (DOE) was applied to five vehicle applications, each with two engines to examine the potential differences that variations in road load and engine characteristics might have on optimal transmission gearing selection. The transmission gearing options considered in the DOE were number of gears, launch gear ratio and top gear ratio. Final drive ratio was also included due to its global influence on vehicle performance and powertrain operating speeds and torque.

This paper discusses steps for identifying, evaluating and recommending a quantifiable design metric or metrics for Side Airbag (SAB) development. Three functionally related and desirable attributes of a SAB are assumed at the onset, namely, effective SAB coverage, load distribution and efficient energy management at a controlled force level. The third attribute however contradicts the “banana shaped” force-displacement response that characterizes the ineffective energy management reality of most production SAB. In this study, an estimated ATD to SAB interaction energy is used to size and recommend desired force-deformation characteristic of a robust energy management SAB. The study was conducted in the following three phases and corresponding objectives: Phase 1 is a SAB assessment metric identification and estimation, using a uniform block attached to a horizontal impact machine.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

Due to increased demand for improved fuel economy and reduction in CO2 emissions, active grille shutter (AGS) has been considered as one option to increase fuel economy by reducing vehicle drag resistance. An AGS system will allow airflow through the grille when demand on cooling system or air conditioning system is high. Under conditions of light load and moderate ambient temperatures and humidity, the grille does not have to be fully open. A reduction in the effective grille size opening can be achieved by either partially or fully closing the grille through a stepped speed motor actuator. When the grille opening size is reduced, under-hood airflow will decrease. Therefore, the operating points for the grille shutter should take into account the effect of temperature rise for under-hood and underbody components and the performance of the cooling and climate control systems.

The need for improved vehicle energy efficiency has increased greatly in recent years along with regulatory fuel economy standards. One key aspect of energy efficiency for both conventional and alternative propulsion vehicles is the energy efficiency of the electrical architecture. In the design of electrical architectures there are several techniques available to increase the energy efficiency. One technique is to manage CAN serial data communication by using Partial Networks. This paper describes a model based approach for simulating the vehicle network behavior when CAN Partial Networking is used as the strategy for need based ECU activation. The simulation results will in turn provide ECU power consumption data to support various electrical architecture design decisions.

Rapidly increasing customer, financial, and regulatory pressures are creating clear changes in the calculus of vehicle design for modern automotive OEM's (Original Equipment Manufacturers). Customers continue to demand shorter product lifecycles; the increasingly competitive global market exerts pressure to reduce costs in all stages of development; and environmental regulations drive a continuous need to reduce mass and energy consumption. OEM's must confront these challenges while continuing to satisfy the customer. The foundation to meeting these challenges includes: (1) Continued development of objective metrics to quantify performance; (2) Frontloading vehicle design content and performance synthesis; (3) A precise understanding of the customer and their performance preferences under diverse usage conditions. These combined elements will enable products better optimized amongst competing (and often contradictory) imperatives.

The present paper reports on a study of the HVAC energy usage for an EREV (extended range electric vehicle) implementation of a localized cooling/heating system. Components in the localized system use thermoelectric (TE) devices to target the occupant's chest, face, lap and foot areas. A novel contact TE seat was integrated into the system. Human subject comfort rides and a thermal manikin in the tunnel were used to establish equivalent comfort for the baseline and localized system. The tunnel test results indicate that, with the localized system, HVAC energy savings of 37% are achieved for cooling conditions (ambient conditions greater than 10 °C) and 38% for heating conditions (ambient conditions less than 10 °C), respectively based on an annualized ambient and vehicle occupancy weighted method. The driving range extension for an electric vehicle was also estimated based on the HVAC energy saving.

Growing concerns about the environment, energy dependency, and unstable fuel prices have increased the market share of electric vehicles. This has led to an increased demand for energy efficient routing algorithms that are optimized for electric vehicles. Traditional routing algorithms are focused on finding the shortest distance or the least time route between two points. These approaches have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power and capacity limits, as well as vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present a simulated solution to the energy efficient routing for electric vehicles using Particle Swarm Optimization. Simulation results show improvements in the energy consumption of the electric vehicle when applied to a start-to-destination routing problem.

Traditional vehicle air conditioning systems are designed to cool the entire cabin to provide passenger comfort. Localized cooling, on the other hand, focuses on keeping the passenger comfortable by creating a micro climate around the passenger. Such a system also easily adapts to the number of passengers in the car and enables zonal control. The net impact of the localized cooling is that equivalent comfort can be achieved at reduced HVAC energy consumption rate. The present paper reports on a vehicle implementation of localized cooling using Thermoelectric Devices and the resulting energy saving.

A 2.0L twin-scroll turbocharged SIDI engine was used to evaluate low-pressure loop water-cooled external EGR at operating conditions between 1000 rpm 75 Nm and 3000 rpm 250 Nm. The engine compression ratio was increased from 9.3 to 10.9. The maximum fuel consumption reduction potential, the boost pressure requirements, and the optimized external EGR calibration were determined. Combination of higher compression ratio and external EGR achieved 5-7% better fuel economy over mid-load region when using the twin-scroll turbocharger. A similar (4-6%) better fuel economy was observed over much of the higher-load region, including peak torque condition at 1000rpm, when the required boost pressure was provided by an externally-driven auxiliary boost system (not connected to the engine). The power consumption of auxiliary boost system (supercharger loss) was estimated and considered in fuel economy assessment. The fuel consumption reduction mechanisms of EGR were also analyzed.