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Due to the recent progress in electrification, lithium-ion batteries have been widely used for electric and hybrid vehicles. Lithium-ion batteries exhibit high energy density and high-power density which are critical for vehicle development with high driving range enhanced performance. However, high battery temperature can negatively impact the battery life, performance, and energy delivery. In this paper, we developed and applied an analytical algorithm to estimate battery life-based vehicle level testing. A set of vehicle level tests were selected to represent customer duty cycles. Thermal degradation models are applied to estimate battery capacity loss during driving and park conditions. Due to the sensitivity of Lithium-Ion batteries to heat, the effect of high ambient temperatures throughout the year is considered as well. The analysis provides an estimate of the capacity loss due to calendar and cyclic effects throughout the battery life.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

Gasoline particulate filters (GPF) have become a standard aftertreatment component in Europe, China, and since recently, India, where particulate emissions are based on a particle number (PN) standard. The anticipated evolution of regulations in these regions towards future EU7, CN7, and BS7 standards further enhances the needs with respect to the filtration capabilities of the GPFs used. Emission performance has to be met over a broader range in particle size, counting particles down to 10nm, and over a broader range of boundary conditions. The requirements with respect to pressure drop, aiming for as low as possible, and durability remain similar or are also enhanced further. To address these future needs new filter technologies have been developed. New technologies for uncatalyzed GPF applications have been introduced in our previous publications.

This work represents an advanced engineering research project partially funded by the U.S. Department of Energy (DOE). Ford Motor Company, FEV North America, and Oak Ridge National Laboratory collaborated to develop a next generation boosted spark ignited engine concept. The project goals, specified by the DOE, were 23% improved fuel economy and 15% reduced weight relative to a 2015 or newer light-duty vehicle. The fuel economy goal was achieved by designing an engine incorporating high geometric compression ratio, high dilution tolerance, low pumping work, and low friction. The increased tendency for knock with high compression ratio was addressed using early intake valve closing (EIVC), cooled exhaust gas recirculation (EGR), an active pre-chamber ignition system, and careful management of the fresh charge temperature.

Automotive Heating Ventilation and Air Conditioning (HVAC) system is essential in providing the thermal comfort to the cabin occupants. The HVAC noise which is typically not the main noise source in IC engine vehicles, is considered to be one of the dominant sources inside the electric vehicle cabin. As air is delivered through ducts and registers into the cabin, it will create an air-rush/broadband noise and in addition to that, any sharp edges or gaps in flow path can generate monotone/tonal noise. Noise emanating from the HVAC system can be reduced by optimizing the airflow path using virtual tools during the development stage. This paper mainly focuses on predicting the noise from the HVAC ducts and registers. In this study, noise simulations were carried-out with ducts and registers. A Finite Volume Method (FVM) based 3-dimensional (3D) Computational Fluid Dynamics (CFD) solver was used for flow as well as acoustic simulations.

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.

Water fording events are one of the most challenging situations that vehicles undergo during their lifetime. During these events the underbody components (e.g. Front fascia, Bellypan, wheel liner etc.) are subject to very high loads. Typically, vehicle water fording tests are performed for various depths of water at prescribed vehicle speeds. Water fording tests are usually carried out during the proto phase of the vehicle development program to ensure acceptable performance. If issues are discovered, making changes to the fascia or body panels are typically very expensive. To avoid late changes, a fully virtual methodology was developed to facilitate vehicle water fording performance. The simulation is targeted to evaluate multiple aspects such as air induction system and estimation of hydrodynamic loads on body panel components.

Recently, the automotive industry has experienced rapid growth in powertrain electrification, with more and more battery electric vehicles (BEV) and hybrid electric vehicles being launched. Lithium-ion batteries play an important role due to their high energy capacity and power density, however they experience high heat generation in their operation, and if not properly cooled it can lead to serious safety issues as well as lower performance and durability. In that way, good prediction of a battery behavior is crucial for successful design and management. This paper presents a 1D electrochemical model development of a 144 Ah prismatic rolled cell using the GT-Autolion software with a pseudo 2D approach. The model correlation is done at cell level comparing model results and test data of cell open circuit voltage at different temperatures and voltage and temperature profile under different C-rates and ambient temperatures.

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.

Cooling airflow is an essential factor when it comes to vehicle performance and operating safety. In recent years, significant efforts have been made to maximize the flow efficiency through the heat exchangers in the under-hood compartment. Grille shutters, new fan shapes, better sealings are only some examples of innovations in this field of work. Underhood cooling airflow simulations are an integral part of the vehicle development process. Especially in the early development phase, where no test data is available to verify the cooling performance of the vehicle, computational fluid dynamics simulations (CFD) can be a valuable tool to identify the lack of fan performance and to develop the appropriate strategy to achieve airflow goals through the heat exchangers. For vehicles with heat exchangers in the underhood section the airflow through those components is of particular interest.

As more electric vehicles (BEV, HEV, PHEV, etc.) are adopted in the upcoming decades, it is becoming increasingly important to conduct vehicle-level thermal simulations under different drive-cycle conditions while incorporating the various subsystem thermal losses. Thermal management of the various heat sources in the vehicle is essential both in terms of ensuring passenger safety as well as maintaining all the subsystems within their corresponding safe temperature limits. It is also imperative that these thermal simulations include energy consumption prediction, while considering the effect of battery degradation both in terms of increased thermal losses as well as reduction in the vehicle’s range. For this purpose, a three-dimensional transient thermal analysis framework was coupled with an electrochemical P2D-based battery model and a vehicle dynamics model to test different scenarios and their effect on a hybrid vehicle’s range and the lithium-ion battery life.

Automobile hood design is driven by many factors, such as strict government regulations, fuel economy, weight, manufacturability, aerodynamic performance, aesthetics, structural integrity, and pedestrian safety standards. The requirement of improved fuel economy and safety regulations like pedestrian protection drive designers to reduce the thickness of the hood parts and use lighter materials. This leads to significant reduction in the hood stiffness. The hood needs to withstand steady and unsteady aerodynamic loads and meet deflection and vibration targets. The susceptibility of the hood to adverse aero load response is increased as the stiffness of the hood is reduced. The objective of this study is to develop a methodology to simulate hood behavior under transient aerodynamic loads in controlled environments. This study mainly focuses on developing fluid structure interaction methodology to simulate the behavior of the hood system under transient aerodynamic loads.

Reducing emissions and the carbon footprint of our society have become imperatives requiring the automotive industry to adapt and develop technologies to strive for a cleaner sustainable transport system and for sustainable economic prosperity. Electrified hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and range extender powertrains provide potential solutions for reducing emissions, but they present challenges in terms of thermal management. A key requirement for meeting these challenges is accurately to predict the thermal loading and temperatures of an internal combustion engine (ICE) quickly under multiple full-load and part-load conditions. Computational Fluid Dynamics (CFD) and thermal survey database methods are used to derive thermal loading of the engine structure and are well understood but typically only used at full-load conditions.

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.

Stringent requirements for high fuel economy and energy efficiency mandate using increasingly complex vehicle thermal systems in most types of electrified vehicles (xEVs). Enabling the maximum benefits of such complex thermal systems under the full envelope of their operating modes demands designing complex thermal control systems. This is becoming one of the most challenging problems for electrified vehicles. Typically, the thermal systems of such vehicles have several modes of operation, constituting nonlinear multiple-input/multiple-output (MIMO) dynamic systems that cannot be efficiently controlled using classical or rule based strategies. This paper covers the different steps towards the design of a model-based control (MBC) strategy that can improve the overall performance of xEV thermal control systems. To achieve the above objective, the latter MBC strategy is applied to control cooling of the cabin and high voltage battery.

This review covers advances in regulations and technologies in the past year in the field of vehicular emissions. We cover major developments towards reducing criteria pollutants and greenhouse gas emissions from both light- and heavy-duty vehicles and off-road machinery. To suggest that the transportation is transforming rapidly is an understatement, and many changes have happened already since our review last year [1]. Notably, the US and Europe revised the CO2 standards for light-duty vehicles and electrification mandates were introduced in various regions of the world. These have accelerated plans to introduce electrified powertrains, which include hybrids and pure electric vehicles. However, a full transformation to electric vehicles and the required grid decarbonization will take time, and policy makers are accordingly also tightening criteria pollutant standards for internal combustion engines.

India BS6 Stage2 (2023) regulations demand all gasoline direct injection (GDI) vehicles to meet particulate number emissions (PN) below 6x10+11# per km. Gasoline particulate filters (GPF) are a proven technology and enable high PN filtration efficiencies throughout the entire vehicle lifetime. One challenge for GPF applications could be the changing emission performance characteristics as a function of mileage due to collected ash and/or soot deposits with implications on back pressure losses. The main objective of this technical contribution is to study the above-mentioned challenges while applying Indian driving conditions and typical Indian climate and other ambient conditions. The substrate technology selected for this study is a high porosity GPF designed to enable the integration of a three-way functionality into the GPF, commonly described as catalyzed GPF (cGPF).

For flows around a tire rotating over a ground plane, the Reynolds number is probably the most important parameter influencing the transition mechanism leading to flow separation from the tire surface, as it determines the viscous response of the boundary layer in the vortex-wall interaction. The present work investigates the effects of Reynolds number on an isolated tire model using a commercial Computational Fluid Dynamics (CFD) code. It validates the baseline simulation for this purpose against the Particle Image Velocimetry (PIV) data from Stanford University got using a Toyota Formula 1 race car tire model. Time-resolved velocity fields and vortex structures from the PIV data are used to correlate local and global flow phenomena to identify unsteady boundary-layer separation and the subsequent flow structures. The study will highlight the pre to post critical flow regimes where the aero coefficients and vortex structure will be studied.

Tire modeling has been an area of major research in automotive industries as the tires cause approximately 25% of vehicle drag. With the fast-paced growth of computational resources, Computational Fluid Dynamics (CFD) has evolved as an effective tool for aerodynamic design and development in the automotive industry. One of the main challenges in the simulation of the aerodynamics of tires is the lack of a detailed and accurate experimental setup with which to correlate. In this study, the focus is on the prediction of the aerodynamics associated with an isolated rotating Formula 1 tire and brake assembly. Literature has indicated differing mechanisms explaining the dominant features such as the wake structures and unsteadiness. Limited work has been published on the aerodynamics of a realistic tire geometry with specific emphasis on advanced turbulence closures such as the Detached Eddy Simulation (DES).