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Vehicle fire investigators often use the existence of burn patterns, along with the amount and location of fire damage, to determine the fire origin and its cause. The purpose of this paper is to study the effects of ventilation location on the interior burn patterns and burn damage of passenger compartment fires. Four similar Ford Fusion vehicles were burned. The fire origin and first material ignited were the same for all four vehicles. In each test, a different door window was down for the duration of the burn test. Each vehicle was allowed to burn until the windshield, back glass, or another window, other than the window used for ventilation, failed, thus changing the ventilation pattern. At that point, the fire was extinguished. Temperatures were measured at various locations in the passenger compartment. Video recordings and still photography were collected at all phases of the study.

A significant route for water ingress into passenger cars is through the Heating, Ventilating, and Air-Conditioning (HVAC) system. The penetration of rainwater through the HVAC unit and the subsequent rise in moisture levels within the passenger compartment directly affect the provision of thermal comfort to the cabin occupants. It is speculated that up to 80% of water ingress into the cowl or engine bay is from water film movement over the bonnet of the car, and only the remaining 20% is from direct rain impact from above. Using a full-scale Climatic Wind Tunnel (CWT) facility, which incorporates accurate rain distribution modeling, it has been possible to study the movement of rainwater film over the exterior surface of the vehicle to ascertain the flow distribution of the film moving into the engine bay, into the cowl, advancing up and over the windscreen and shed to the sides and front of the vehicle.

Under the competitive pressure of automotive industry the customer’s focus is on a vehicle’s quality perception. Side door closing efforts make a considerable share of the overall impression as the doors are the first physical and haptic interface to the customer. Customer’s subjective feeling of vehicle quality demands for detailed analysis of each contributor of door closing efforts. Most contributors come from kinematic influences. Beside the losses due to mechanical subsystems like the checkarm, latch or hinge friction one of the biggest impacts originates from the pressure spike that builds up due to air being pushed into the cabin. Subject of this publication is to discuss the dependencies of closing efforts on cabin pressure and air extraction. It demonstrates an approach to simulate the development of the air pressure during door closing motions and the validation of the simulation method with the “EZ-Slam” measurement device.

Computational fluid dynamic modeling methodology was utilized to develop a Heating, Ventilation and Air Conditioning (HVAC) duct. The design was modeled and analyzed using the STAR-CD computational fluid dynamics software. A console mounted HVAC duct was designed and installed in a test vehicle to enhance the airflow distribution in the rear passenger compartment. A model of this design was constructed and computational analysis was performed to identify the opportunities for improvement. The location and position of the baffle was modified based on the computational fluid dynamic analysis. Validation and flow visualization tests of the revised design provided good correlation with the predicted behavior. The modified design increased the volumetric airflow by 25% and balanced the air distribution at each register.

A quiet passenger compartment is highly desired by today's customers. Vehicle design teams spend significant time and resources to minimize the vehicle's interior noise due to different exterior noise sources. Drive-by vehicles on the highways generate one of these noise sources. The objective of this study is to establish a correlation between customer's on-road experience due to drive-by noise and Noise Reduction Level (body transparency) tests conducted in a reverberation room. The average Noise Reduction Level (NRL) obtained in the reverberation room tests correlates (R2=0.89) with the peak loudness of on road tests. A sound quality listening study was also conducted to determine the most preferred NRL spectral distribution and its frequency range sensitivity.

The objective of this paper is to describe a Computer Aided Engineering (CAE) model and its applications for A/C system development at early design stages. This CAE tool calculates the heat load of the vehicle passenger compartment with considerations of solar radiation, conductive/convective heat transfer through the body shell, and any passengers present in the vehicle. A data bank of 6 glass types, 9 surface finishes and 15 material properties are available to increase simulation flexibility. This heat load model can be used as a stand alone tool to calculate the steady-state thermal load of the passenger compartment under users' pre-defined conditions. When interfaced with an A/C refrigerant subsystem model, this integrated CAE tool is capable of evaluating the impacts on A/C system performance when body structures and/or operating conditions are changed.

It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.

The Multi Material Lightweight Vehicle (MMLV) developed by Magna International and Ford Motor Company is a result of a US Department of Energy project DE-EE0005574. The project demonstrates the lightweighting potential of a five passenger sedan, while maintaining vehicle performance and occupant safety. Prototype vehicles were manufactured and limited full vehicle testing was conducted. The Mach-1 vehicle design, comprised of commercially available materials and production processes, achieved a 364 kg (23.5%) full vehicle mass reduction, enabling the application of a 1-liter 3-cylinder engine resulting in a significant environmental benefit and fuel reduction. This paper includes details associated with the noise, vibration and harshness (NVH) sound package design and testing. Lightweight design actions on radiating panels enclosing the vehicle cabin typically cause vehicle interior acoustic degradation due to the reduction of panel surface mass.

A comprehensive strategy to investigate the role of the body mounts on the passenger compartment response in a frontal crash event is presented. The activities of the study include quasi-static vehicle crush testing, development of a component-level dynamic body mount test methodology, lumped-mass computer modeling, as well as technical analysis. In addition, a means of investigating the effects the body mounts have on the passenger compartment response during a frontal barrier impact is addressed.

The design and operation of a vehicle’s heating, ventilation, and air conditioning (HVAC) system has great impact on the performance of the vehicle’s Automatic Speech Recognition (ASR) and Hands-Free Communication (HFC) system. HVAC noise provides high amplitudes of broadband frequency content that affects the signal to noise ratio (SNR) within the vehicle cabin, and works to mask the user’s speech. But what’s less obvious is that when the airflow from the panel vents or defroster openings can be directed toward the vehicle microphone, a mechanical “buffeting” phenomenon occurs on the microphone’s diaphragm that distresses the ASR system beyond its ability to interpret the user’s voice. The airflow velocity can be strong enough that a simple windscreen on the microphone is not enough to eliminate the problem. Minimizing this buffeting effect is a vital key to building a vehicle that meets the customer’s expectations for ASR and HFC performance.

The performance of a vehicle’s Automatic Speech Recognition (ASR) system is dependent on the signal to noise ratio (SNR) in the cabin at the time a user voices their command. HVAC noise and environmental noise in particular (like road and wind noise), provide high amplitudes of broadband frequency content that lower the SNR within the vehicle cabin, and work to mask the user’s speech. Managing this noise is a vital key to building a vehicle that meets the customer’s expectations for ASR performance. However, a speech recognition engineer is not likely to be the same person responsible for designing the tires, suspension, air ducts and vents, sound package and exterior body shape that define the amount of noise present in the cabin. If objective relationships are drawn between the vehicle level performance of the ASR system, and the vehicle or system level performance of the individual noise, vibration and harshness (NVH) attributes, a partnership between the groups is brokered.

This paper describes a method to validate in-vehicle speech recognition by combining synthetically mixed speech and noise samples with batch speech recognition. Vehicle cabin noises are prerecorded along with the impulse response from the driver's mouth location to the cabin microphone location. These signals are combined with a catalog of speech utterances to generate a noisy speech corpus. Several factors were examined to measure their relative importance on speech recognition robustness. These include road surface and vehicle speed, climate control blower noise, and driver's seat position. A summary of the main effects from these experiments are provided with the most significant factors coming from climate control noise. Additionally, a Signal to Noise Ratio (SNR) experiment was conducted highlighting the inverse relationship with speech recognition performance.