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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.

The performance and durability of the lead acid battery is highly dependent on the internal battery temperature. The changes in internal battery temperatures are caused by several factors including internal heat generation and external heat transfer from the vehicle under-hood environment. Internal heat generation depends on the battery charging strategy and electric loading. External heat transfer effects are caused by customer duty cycle, vehicle under-hood components and under-hood ambient air. During soak conditions, the ambient temperature can have significant effect on battery temperature after a long drive for example. Therefore, the temperature rise in a lead-acid battery must be controlled to improve its performance and durability. In this paper a thermal model for lead-acid battery is developed which integrates both internal and external factors along with customer duty cycle to predict battery temperature at various driving conditions.

Integration of automatic engine Stop/Start systems in “conventional” drivetrains with 12V starters is a relatively cost-effective measure to reduce fuel consumption. Therefore, automatic engine Stop/Start systems are becoming more prevalent and increasing market share of such systems is predicted. A quick, reliable and consistent engine start behavior is essential for customer acceptance of these systems. The launch of the vehicle should not be compromised by the Stop/Start system, which implies that the engine start time and transmission readiness for transmitting torque should occur within the time the driver releases the brake pedal and de-presses the accelerator pedal. Comfort and NVH aspects will continue to play an important role for customer acceptance of these systems. Hence, the engine stop and re-start behavior should be imperceptible to the driver from both a tactile and acoustic standpoint.

Significant reduction in Nitrogen Oxide (NOx) emissions will be required to meet LEV III/Tier III Emissions Standards for Light Duty Diesel (LDD) passenger vehicles. As such, Original Equipment Manufacturers (OEMs) are exploring all possible aftertreatment options to find the best balance between performance, durability and cost. The primary technology adopted by OEMs in North America to achieve low NOx levels is Selective Catalytic Reduction (SCR). The critical parameters needed for SCR to work properly are: an appropriate reductant such as ammonia (NH3) provided as Diesel Exhaust Fluid (DEF), which is an aqueous urea solution 32.5% concentration in weight with water (CO(NH2)2 + H2O), optimum operating temperatures, and optimum nitrogen dioxide (NO2) to NOx ratios (NO2/NOx). The NO2/NOx ratio is most influenced by Precious Group Metals (PGM) containing catalysts upstream of the SCR catalyst.

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.

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.

Interest in natural gas as a fuel for light-duty transportation has increased due to its domestic availability and lower cost relative to gasoline. Natural gas, comprised mainly of methane, has a higher knock resistance than gasoline making it advantageous for high load operation. However, the lower flame speeds of natural gas can cause ignitability issues at part-load operation leading to an increase in the initial flame development process. While port-fuel injection of natural gas can lead to a loss in power density due to the displacement of intake air, injecting natural gas directly into the cylinder can reduce such losses. A study was designed and performed to evaluate the potential of natural gas for use as a light-duty fuel. Steady-state baseline tests were performed on a single-cylinder research engine equipped for port-fuel injection of gasoline and natural gas, as well as centrally mounted direct injection of natural gas.

This paper focuses on detailed numerical simulations of direct injection diesel and gasoline sprays from production grade, multi-hole injectors. In a dual-fuel engine the direct injection of both the fuels can facilitate appropriate mixture preparation prior to ignition and combustion. Diesel and gasoline sprays were simulated using high-fidelity Large Eddy Simulations (LES) with the dynamic structure sub-grid scale model. Numerical predictions of liquid penetration, fuel density distribution as well as transverse integrated mass (TIM) at different axial locations versus time were compared against x-ray radiography data obtained from Argonne National Laboratory. A necessary, but often overlooked, criterion of grid-convergence is ensured by using Adaptive Mesh Refinement (AMR) for both diesel and gasoline. Nine different realizations were performed and the effects of random seeds on spray behavior were investigated.

New Particulate Matter (PM) and Particulate Number (PN) regulations throughout the world have created a need for aftertreatment solutions that include particulate control as an option to comply with the legislation. However, limitations in other criteria emissions cannot be sacrificed to accomplish the reduction of PM/PN. For this work, three-way washcoat catalyzed wall-flow Gasoline Particulate Filters (GPF) and similarly catalyzed flow-through catalysts of common defined volume were tested. Their catalytic performance was determined by measuring NOx, CO and HC conversion efficiencies and CO2 levels over the U.S. Federal Test Procedure 75 (FTP-75) and US06 Supplemental Federal Test Procedure (US06) cycles. Analysis of the impact on CO2 emissions was also evaluated in relation to backpressure from 1-D modeling analysis. All exhaust systems used the same loading and ratio of Platinum Group Metals (PGM), but employed different cell structures in their substrates.

Significant reduction in Nitrogen Oxide (NOx) emissions will be required to meet LEV III Emissions Standards for Light Duty Diesel passenger vehicles (LDD). As such, Original Equipment Manufacturers (OEMs) are exploring all possible aftertreatment options to find the best balance between performance, robustness and cost. The primary technology adopted by OEMs in North America to achieve low NOx levels is Selective Catalytic Reduction (SCR) catalyst. The critical parameters needed for SCR to work properly are: an appropriate reductant such as ammonia (NH3) typically provided as urea, adequate operating temperatures, and optimum Nitrogen Dioxide (NO2) to NOx ratios (NO2/NOx). The NO2/NOx ratio is mostly influenced by Precious Group Metals (PGM) containing catalysts located upstream of the SCR catalyst. Different versions of zeolite based SCR technologies are available on the market today and these vary in their active metal type (iron, copper, vanadium), and/or zeolite type.

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.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.

The automotive industry is moving towards larger SUVs and also electrification is a need to meet the carbon neutrality target. As a result, we see an increase in overall gross vehicle weight (GVW), with the additional weight coming from the HV battery pack, electric powertrain, and other electrical systems. Tow-eye is an essential component that is provided with every vehicle to use for towing during an emergency vehicle breakdown. The tow-eye is usually connected to the retainer/sleeve available in the bumper system and towed using the recovery vehicle or other car with towing provision. Therefore, the tow-eye should meet the functional targets under standard operating conditions. This study is mainly for cars with bumper and tow-eye sleeves made of aluminum which is used in the most recent development of vehicles for weight-saving opportunities. Tow-eye systems in aluminum bumpers are designed to avoid any bending or buckling of the sleeve during towing for whatever the GVW loads.

One of the building blocks of the Stellantis hybrid powertrain embedded control software computes the maximum and minimum values of objective functions, such as output torque, as a function of engine torque, hybrid motor torque and other variables. To test such embedded software, an offline reference function was created. The reference function calculates the ideal minimum and maximum values to be compared with the output of the embedded software. This article presents the offline reference function with an emphasis on mathematical novelties. The reference function computes the minimum and maximum points of a linear objective function as a function of six independent variables, subject to 42 linear and two nonlinear constraints. Concave domains, curved surfaces, disjoint domains and multiple local extremum points challenge the algorithm. As a theorem, the conditions and methods for running trigonometric calculations in 6D Euclidean space are presented.

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 key hurdles to achieving wide consumer acceptance of battery electric vehicles (BEVs) are weather-dependent drive range, higher cost, and limited battery life. These translate into a strong need to reduce a significant energy drain and resulting drive range loss due to auxiliary electrical loads the predominant of which is the cabin thermal management load. Studies have shown that thermal sub-system loads can reduce the drive range by as much as 45% under ambient temperatures below −10 °C. Often, cabin heating relies purely on positive temperature coefficient (PTC) resistive heating, contributing to a significant range loss. Reducing this range loss may improve consumer acceptance of BEVs. The authors present a unified thermal management system (UTEMPRA) that satisfies diverse thermal and design needs of the auxiliary loads in BEVs.

Vehicle weight reduction through the use of components made of magnesium alloys is an effective way to reduce carbon dioxide emission and improve fuel economy. In the design of these components, which are mostly under cyclic loading, notches are inevitably present. In this study, surface strain distribution and crack initiation sites in the notch region of AZ31B-H24 magnesium alloy notched specimens under uniaxial load are measured via digital image correlation. Predicted strains from finite element analysis using Abaqus and LS-DYNA material types 124 and 233 are then compared against the experimental measurements during quasi-static and cyclic loading. It is concluded that MAT_233, when calibrated using cyclic tensile and compressive stress-strain curves, is capable of predicting strain at the notch root. Finally, employing Smith-Watson-Topper model together with MAT_233 results, fatigue lives of the notched specimens are estimated and compared with experimental results.

One of the advantages of hybrid vehicles is the ability to operate the engine more optimally at a low brake specific fuel consumption (BSFC) as compared to conventional vehicles. This ability of hybrid vehicles is a major factor contributing to the fuel economy improvement over conventional vehicles. Unlike conventional gasoline powertrains, hybrid powertrains allow engine to be switched off and use battery power to propel vehicles. In order to maintain battery state of charge neutral operation between the start and end of a drive cycle, the net electrical energy consumption from the battery requires to be zero. An optimization algorithm can be developed and calibrated in different ways to achieve net zero battery energy over the cycle. For instance, the engine can be operated at powers higher than the power of the drive cycle to charge the battery. This accumulated energy can be used for all-electric propulsion by turning off the engine.

Rapid Compression Machines (RCM) offer the ability to easily change the compression ratio and the pressure/mixture composition/temperature to gather ignition delay data at various engine relevant conditions. Therefore, RCMs with optical access to the combustion chamber can provide an effective way to analyze macroscopic spray characteristics needed to understand the spray injection process and for spray model development, validation and calibration at conditions that are suitable for engines. Fuel surrogates can help control fuel parameters, develop models for spray and combustion, and perform laser diagnostics with known fluorescence characteristics. This study quantifies and evaluates the macroscopic spray characteristics of multicomponent gasoline surrogates in comparison to their gasoline counterparts, under gasoline direct injection (GDI) engine conditions.

In this work autoignition delay times of two multi-component surrogates (high and low RON) were experimentally compared with their target full blend gasoline fuels. The study was conducted in a rapid compression machine (RCM) test facility and a direct test chamber (DTC) charge preparation approach was used for mixture preparation. Experiments were carried over the temperature range of 650K-900K and at 10 bar and 20 bar compressed pressure conditions for equivalence ratios of (Φ =) 0.6-1.3. Dilution in the reactant mixture was varied from 0% to 30% CO2 (by mass), with the O2:N2 mole ratio fixed at 1:3.76. This dilution strategy emulates exhaust gas recirculation (EGR) substitution in spark ignition (SI) engines. The multicomponent surrogate captured the reactivity trends of the gasoline-air mixtures reasonably well in comparison to the single component (iso-octane) surrogate.