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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.

The proposed Euro-7 regulations are expected to build on the significant emissions reductions that have already been achieved using advanced Euro VI compliant after treatment systems (ATS). The introduction of in-service conformity (ISC) requirements during Euro VI paved the way for enabling compliance during real-world driving conditions. The diverse range of applications and resulting operating conditions greatly impact ATS design and the ability of the diesel particulate filter (DPF) to maintain performance under the most challenging boundary conditions including cold starts, partial/complete regenerations, and high passive soot burn operation. The current study attempts to map the particle number (PN) filtration performance of different DPF technologies under a variety of in-use cycles developed based on field-data from heavy duty Class-8 / N3 vehicles.

With more stringent CO2 emission regulation in the world, Plug-in Hybrid Electric Vehicle (PHEV, also known as off-vehicle charging hybrid electric vehicle, OVC-HEV) plays a more important role in the current modern market, such as in China. At the same time, Real Driving Emission (RDE) was introduced in both Euro 6d and China 6b regulation, which covers more factors in the real driving practice including altitude, environment temperature, fuel quality, driving behaviors, and so on, which could potentially impact the pollutant emissions. Besides above mentioned, for PHEV, the state of charge (SOC) of the battery is also considered as one important factor, which could impact the engine load and emissions.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

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.

The gasoline particulate filter (GPF) represents a practical solution for particulate emissions control in light-duty gasoline-fueled vehicles. It is also seen as an essential technology in North America to meet the upcoming US EPA tailpipe emission regulation, as proposed in the “Multi-pollutant Rule for Model Year 2027”. The goal of this study was to introduce advanced, uncoated GPF products and measure their particulate mass (PM) reduction performance within the existing US EPA FTP vehicle testing procedures, as detailed in Code of Federal Regulations (CFR) part 1066. Various state-of-the-art GPF products were characterized for their microstructure properties with lab-bench checks for pressure drop and filtration efficiency, then pre-conditioned with an EPA-recommended 1500 mile on-road break-in, and finally were tested on an AWD vehicle chassis-dyno emissions test cell at both 25°C and -7°C ambient conditions.

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.

The ever-tightening regulation norms across the world emphasize the magnitude of the air pollution problem. The decision to leapfrog from BS4 to BS6 – with further reduction in emission limits -showed India’s commitment to clean up its atmosphere. The overall cycle emissions were reduced significantly to meet BS6 targets [1]. However, the introduction of RDE norms in BS6.2 [1] demanded further reduction in emissions under real time operating conditions – start-stop, hard acceleration, idling, cold start – which was possible only through strategies that demanded a cost effective yet robust solutions. The first few seconds of the engine operation after start contribute significantly to the cycle gaseous emissions. This is because the thermal inertia of the catalytic converter restricts the rate at which temperature of the catalyst increases and achieves the desired “light-off” temperature.

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.

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.

As the official proposal for emission regulation Euro 7 has been released by European Commission, PN above 10nm is taken into consideration for the ultrafine particulate emissions control. The challenges of GPF filtration efficiency emerge for the light-duty manufactures to meet the future emission standards. In the present study, a China 6 compliant vehicle was tested to reveal its performance over the China 6 standards and potential to meet the upcoming Euro 7. Three GPF product types (Gen 1, Gen 2, and concept Gen 3) were mounted to the tested vehicle. WLTC tests were conducted on chassis dynamometer in laboratory as well as a self-designed aggressive cycle (“Base Cycle”) tests. To explore the GPFs performance for PN emissions above 10nm against the proposed limit 6.0E11 #/km, PN emission above 10nm were measured in our laboratory tests for both engine out and tailpipe as well as the PN emission above 23nm.

The LEV IV FTP PM limit in the recently approved CARB ACC II regulations for passenger cars and light duty trucks will be 1 mg/mile starting in 2025. Gravimetric PM measurement at these levels is very challenging as the net mass of PM on the filter in full flow tunnel testing ranges between 8 to 32 micrograms depending on amount of dilution. This is approaching tunnel background levels which, in combination with filter handling, static charge removal and microbalance instability, compounds the uncertainty. One major source of the uncertainty at these low levels is the tunnel contamination resulting in high variability from test to test and cell to cell. This tunnel background is mostly HC artifact which cannot be easily controlled and can be significantly higher than the 5-μg CFR allowable correction limit in some test cells.

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.

As in the past several years, we provide here an overview of recent major regulatory and technological changes for reducing emissions from the transport and off-road sector. In the past, this review was focused mostly on improvement in engine efficiency and tailpipe emissions of criteria pollutants. However, starting last year [1] we have increased the scope to broadly address the increased focus on greenhouse gas emissions and the emergence of various non-conventional fuel pathways to achieve the various decarbonization goals. There are two broad themes that are emerging, and which we describe here. Firstly, that we are approaching the implementation of the last of the major regulations on criteria pollutant emissions from cars and trucks, led by Europe, through Euro 7 standards and US, through multi-pollutant standards for light- and heavy-duty vehicles.

RDE test has been introduced to the light-duty vehicle certification process in both China 6 and Euro 6d standards. The RDE test shall be performed on-road with PEMS, which is developed to complement the current laboratory certification of vehicles and ensure cars to deliver low emissions under more realistic on-road driving conditions. Particulate matter has been highly perceived as a significant contributor to human health risks and thus strictly regulated globally. For the RDE evaluation, the MAW method used by the China 6 standard is usually found less stringent than the RMA method used by the Euro 6d standard. In the present study, both of the MAW and RMA methods were applied to different driving cycles and operating conditions, which met the general RDE test requirements, yet resulted in different evaluated PN results.

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.

In the light duty diesel segment, the need persists for an advanced control system to monitor the health of an aftertreatment system throughout a vehicle’s life in order to maintain compliance with ever tightening emissions levels. In on-board diagnostics (OBD), every diagnostic is validated during development stages to detect when a system under monitoring of that diagnostic has failed. This necessitates the need to create parts which represent a failure that would be observed on the vehicle. These failed parts, referred to as limit or threshold parts, are developed through a limit part creation process. Although there are commonalities amongst Original Equipment Manufacturers (OEM), each OEM has their own detection logic which will require a unique and specific limit part. Various methods exist for creating these limit parts, and each method produces a different combination of ability to detect the failure and its associated tailpipe emissions.