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A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

Engine air induction shell noise is a structure borne noise that radiates from the surface of the air induction system. The noise is driven by pulsating engine induction air and is perceived as annoying by vehicle passengers. The problem is aggravated by the vehicle design demands for low weight components packaged in an increasingly tight under hood environment. Shell noise problems are often not discovered until production intent parts are available and tested on the vehicle. Part changes are often necessary which threatens program timing. Shell noise should be analyzed in the air induction system design phase and a good shell noise analytical process and targets must be defined. Several air induction clean side ducts are selected for this study. The ducts shell noise is assessed in terms of material strength and structural stiffness. A measurement process is developed to evaluate shell noise of the air induction components. Noise levels are measured inside of the clean side ducts.

There are many noise sources from the vehicle fuel system to generate noise inside a vehicle. Among them, the pressure pulsation due to the rapid opening and closing of gasoline engine injectors can cause undesirable fuel pulse noise. As the pressure pulsation propagates in the fuel supply line toward to rear end of the vehicle, the pressure energy is transferred from fuel lines to the vehicle underbody through clips and into the passenger compartment. It is crucial to attenuate the pressure pulsation inside the fuel line to reduce the fuel pulse noise. In this paper, a case study on developing an effective countermeasure to reduce the objectionable fuel pulse noise of a V8 gasoline injection system at engine idle condition is presented. First, the interior noise of a prototype vehicle was tested and the objectionable fuel pulse noise is exhibited. The problem frequency ranges of the pulse noise were identified.

This paper proposes a domain-centralized powertrain E/E (electrical and/or electronic) architecture for all-electric vehicles that features: a powerful master controller (domain controller) that implements most of the functionality of the domain; a set of smart actuators for electric motor(s), HV (High Voltage) battery pack, and thermal management; and a gateway that routes all hardware signals, including digital and analog I/O, and field bus signals between the domain controller and the rest of the vehicle that is outside of the domain. Major functional safety aspects of the architecture are presented and a safety architecture is proposed. The work represents an early E/E architecture proposal. In particular, detailed partitioning of software components over the domain’s Electronic Control Units (ECUs) has not been determined yet; instead, potential partitioning schemes are discussed.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

This paper presents a novel Kalman filter based road grade estimation method using measurements from an accelerometer, a gyroscope and a velocity sensor. The accelerometer measures the longitudinal proper acceleration of the vehicle, and the accelerometer measurement is almost drift free but it is heavily corrupted by the accelerometer noise. The gyroscope measures the pitch rate of the vehicle, and the gyroscope measurement is quite clean but it is substantially disturbed by the gyroscope bias. The velocity sensor measures the longitudinal velocity of the vehicle, and the velocity sensor measurement is also considerably corrupted by the measurement noise. The developed Kalman filter based estimation method uses the models of the sensors and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The simulation results show that the developed method is very effective in producing an accurate road grade estimate.

The demand for vehicles with electrified powertrain systems is increasing due to government regulations on fuel economy. The battery systems in a PHEV (Plug-in Hybrid-electric Vehicle) have achieved tremendous efficiency over past few years. The system has become more delicate and complex in architecture which requires sophisticated thermal management. Primary reason behind this is to ensure effective cooling of the cells. Hence the current work has emphasized on developing a “Physics based” thermal management modeling framework for a typical battery system. In this work the thermal energy conservation has been analyzed thoroughly in order to develop necessary governing equations for the system. Since cooling is merely a complex process in HEV battery systems, the underlying mechanics has been investigated using the current model. The framework was kept generic so that it can be applied with various architectures. In this paper the process has been standardized in this context.

The noise radiated from the snorkel of an air induction system (AIS) can be a major noise source to the vehicle interior noise. This noise source is typically quantified as the snorkel volume velocity which is directly related to vehicle interior noise through the vehicle noise transfer function. It is important to predict the snorkel volume velocity robustly at the early design stage for the AIS development. Design For Six Sigma (DFSS) is an engineering approach that supports the new product development process. The IDDOV (Identify-Define-Develop-Optimize-Verify) method is a DFSS approach which can be used for creating innovative, low cost and trouble free products on significant short schedules. In this paper, an IDD project which is one type of DFSS project using IDDOV method is presented on developing a robust simulation process to predict the AIS snorkel volume velocity. First, the IDDOV method is overviewed and the innovative tools in each phase of IDDOV are introduced.

As the automotive industry is quickly changing towards electric vehicles, we can highlight the importance of aerodynamics and its critical role in reaching extended battery ranges for electric cars. With all new smooth underbodies, a lot of attention has turned into the effects of rim designs and tires brands and the management of these tire wakes with the vehicle. Tires are one of the most challenging areas for aerodynamic drag prediction due to its unsteady behavior and rubber deformation. With the simulation technologies evolving fast regarding modeling spinning tires for aerodynamics, this paper takes the prior work and data completed by the authors and investigates the impact on the flow fields and aerodynamic forces using the most recent developments of an Immerse Boundary Method (IBM). IBM allows us to mimic realistically a rotating and deformed tire using Lattice Boltzmann methods.

Acoustic comfort of automotive cabins has progressively become one of the key attributes of passenger comfort within vehicle design. Wind noise and the heating, ventilation, and air conditioning (HVAC) system noise are two of the key contributors to noise levels heard inside the car. The increasing prevalence of hybrid technologies and electrification has an associated reduction in powertrain noise levels. As such, the industry has seen an increasing focus on understanding and minimizing HVAC noise, as it is a main source of noise in the cabin particularly when the vehicle is stationary. The complex turbulent flow path through the ducts, combined with acoustic resonances can potentially lead to significant noise generation, both broadband and tonal.

The trend to simultaneously improve fuel economy and engine performance has led to industry growth of turbocharged engines and as a result, the need to address their undesirable airborne noise attributes. This presents some unique engineering challenges as higher customer expectations for Noise Vibration Harshness (NVH), and other vehicle-level attributes increase over time. Turbocharged engines possess higher frequency noise content compared to naturally aspirated engines. Therefore, as an outcome, whoosh noise in the Air Induction System (AIS) during tip in conditions is an undesirable attribute that requires high frequency attenuation enablers. The traditional method for attenuation of this type of noise has been to use resonators which adds cost, weight and requires packaging space that is often at a premium in the under-hood environment.

In this paper, a CAE methodology based on a multiphysics approach for engine gear noise evaluation is reviewed. The method comprises the results and outputs from several different analytical domains to perform the noise risk assessment. The assessment includes the source-path analysis of the gear-induced rattling and whining noise. The vibration data from the exterior surface of the engine is extended through acoustic analysis to perform the engine noise simulation and to identify acoustic hot spots contributing to the noise. The study includes simulations under different engine loading conditions with results presented in both time and frequency domains. Various sensitivity analyses involving different gear geometries and micro-geometries are investigated as well. Finally, the simulation results from three different engines are compared vis-a-vis.

Lithium-ion batteries (LIBs) have been widely used as the energy storage system in plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) due to their high power and energy density and long cycle life compared to other chemistries. However, LIBs are sensitive to operating conditions, including temperature, current demand and surface pressure of the cell. One very well understood phenomenon of lithium-ion battery is the reduction in charge capacity over time due to cycling and storage commonly known as capacity fade. Considering the need for predicting the behavior of an aged cell and the need for estimating battery useful life for warranty purpose, it is crucial to predict the capacity fade with reasonable accuracy. To accommodate this need, a novel cell level empirical aging model is built based on storage tests and cycle tests. The storage test captures the calendar aging of the lithium-ion cell while the cycle test estimates the cycle aging of the cell.

Thermal management of lithium-Ion battery (LIB) has become very critical issue in recent years. One of the challenges for the design and packaging of the battery is to maintain the battery temperature within acceptable ranges and also reduce temperature gradients within the battery cells. Controlling the battery temperature is essential for the battery performance and the long-term battery life. Increased difference between battery cell temperatures can lead to non-uniform charging and non-uniform ageing of battery cells. The purpose of this paper is to investigate available technologies using heat pipes as a means of improving battery thermal management. Several studies have been conducted regarding the effect of heat pipes on battery temperature. However, in this paper we present a comprehensive study of heat pipes effects through transient analysis of a complete vehicle thermal model.

Environmental noises such as wind, road, powertrain, and HVAC noise are important aspects to consider when implementing a hands-free terminal for mobile phone calling from within a car. Traditionally, these environmental noises have been exclusively considered for driver comfort; however, with the introduction of the hands-free terminals (HFT) and increasing consumer demand relative to mobile phone call quality, a broader implication of high background noise levels should be considered. HFT algorithm development and implementation can and does provide a high level of background noise suppression to mitigate these concerns, but this is often done at the expense of computational power and cumulative delay during a phone call. The more advantageous solution would be to address the problem from a source and path perspective with emphasis on reduction of noise in the frequency bands which most influence call quality performance.

The automotive industry continues to develop technologies for reducing vehicle fuel consumption. Specifically, vehicle lightweighting is expected to be a key enabler for achieving fleet CO2 reduction targets for 2025 and beyond. Hybrid glass laminates that incorporate fusion draw and ion exchange innovations are thinner and thereby, offer more than 30% weight reduction compared to conventional automotive laminates. These lightweight hybrid laminates provide additional benefits, including improved toughness and superior optics. However, glazing weight reduction leads to an increase in transmission of sound through the laminates for certain frequencies. This paper documents a study that uses a systematic test-based approach to understand the sensitivity of interior vehicle noise behavior to changes in acoustic attenuation driven by installation of lightweight glass.

To achieve an accurate estimate of losses in a battery it is necessary to consider the reversible entropic losses, which may constitute over 20% of the peak total loss. In this work, a procedure for experimentally determining the entropic heating coefficient of a lithium-ion battery cell is developed. The entropic heating coefficient is the rate of change of the cell’s open-circuit voltage (OCV) with respect to temperature; it is a function of state-of-charge (SOC) and temperature and is often expressed in mV/K. The reversible losses inside the cell are a function of the current, the temperature, and the entropic heating coefficient, which itself is dependent on the cell chemistry. The total cell losses are the sum of the reversible and irreversible losses, where the irreversible losses consist of ohmic losses in the electrodes, ion transport losses, and other irreversible chemical reactions.

In the NVH domain, the cooling module is an important subsystem in ground vehicles. Recently, with the development of small high output turbocharged internal combustion (IC) engines, cooling module noise and vibration has become more challenging. Furthermore, with plug-in hybrid electric vehicle (PHEV), in some cases the cooling fan could be operational while the IC engine is not running. This poses a significant challenge for cabin noise enhancement. Small turbocharged IC engines typically require higher cooling capacity resulting in larger fan size designs with higher speed. Accurate prediction of the unbalance loads generated by cooling fan and loads transferred to the body are critical for the Noise Vibration and Harshness (NVH) performance of the vehicle. If the NVH risk of cooling module operation is not well quantified and addressed early in the program, attempts to find solutions in post launch stage could be very expensive and not as effective.

One approach of reducing weight of vehicles is using composite materials, and short glass fiber reinforced polypropylene is one of most popular composite materials. To more accurately predict durability performance of structures made of this kind of composite material, static and fatigue performance of coupons and components made of a short glass fiber reinforced polypropylene has been physically studied. CAE simulations have been conducted accordingly. This paper described details of CAE model setup, procedures, analysis results and correlations to test results for static, fiber orientation flow and fatigue of coupons and a battery tray component. The material configurations include fiber orientations (0, 20 and 90 degrees), and mean stress effect (R = -1.0, -0.5, -0.2, 0.1 and 0.4). The battery tray component samples experience block cycle loading with loading ratio of R = -0.3 and 0.3. The CAE predictions have reasonable correlations to the test results.

Upper frame deflection of automobile doors is a key design attribute that influences structural integrity and door seal performance as related to NVH. This is a critical customer quality perception attribute and is a key enabler to ensure wind noise performance is acceptable. This paper provides an overview of two simulation methodologies to predict door upper frame deflection. A simplified simulation approach using point loads is presented along with its limitations and is compared to a new method that uses CFD tools to estimate aerodynamic loads on body panels at various vehicle speeds and wind directions. The approach consisted of performing external aerodynamic CFD simulation and using the aerodynamic loads as inputs to a CAE simulation. The details of the methodology are presented along with results and correlation to experimental data from the wind tunnel.