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With all the environmental concern of diesel fuelled vehicle, it is a formidable challenge to phase out them completely specifically from Heavy duty application. Most pragmatic solution lies in solutions which improves the fuel economy and reduce the carbon emission of existing diesel fuelled vehicle fleet and have economical & feasible for vast geographical extent of country. With implementation of BS VI emission norms across country from April 2020, supply of BS VI complied diesel fuel started and BS VI complied vehicles with upgraded engine technologies and after treatment devices have started to come which make present vehicle fleets heterogeneous with substantive number of BS IV vehicle. Beside improvement of engine technologies, existing BS IV vehicle fleet performance can be enhanced through improved fuel and lubricants solutions.

Light-duty vehicle emissions regulations worldwide impose stringent limits on particulate matter (PM) emissions, necessitating accurate modelling and prediction of particulate emissions across a range of sizes (as low as 10 nm). It has been shown that the decision tree-based ensemble machine learning technique known as Random Forest can accurately predict particle size, concentration, and accumulation mode geometric standard deviation (GSD) for 23 nm and greater diameter particulate emissions from a highly boosted gasoline direct injection (GDI) engine operating on a single fuel, while also offering insights into the underlying factors of emissions production because of the interpretable nature of decision trees. This work builds on this research as its basis and further investigates the relative performance of five decision tree-based machine learning techniques in predicting these particulate emission parameters and extends the work to 10 nm particles.

The population growth has led to a significant rise in car demand, raising pollution levels. It compelled governments to enact more stringent pollution standards. Researchers are exploring alternative fuels and advanced low-temperature combustion modes to meet the emission standards. The reactivity-controlled compression ignition strategy is one of the potential combustion strategies to reduce the particulate matter and NOx simultaneously while maintaining higher fuel conversion efficiency. This study investigated the influence of Diesel injection timing and premixing ratio on the cyclic variation of RCCI engines fueled with gasoline and butanol as low reactivity fuel. The experiments were carried out on the single-cylinder diesel engine modified to operate in RCCI mode. All the experiments were carried out at constant load and engine speed. A developmental ECU was used to regulate fuel mass and injection time.

Waste cooking oil can be converted into fuel for internal combustion (IC) engine applications by transesterification or pyrolysis. Transesterification results in the production of fatty acid methyl esters called biodiesel. The variability in biodiesel composition and properties from diesel fuel leads to engine re-calibration that requires significant time and effort. Diesel-like hydrocarbons can be produced by catalytic pyrolysis of used cooking oil. Such fuel can be used as a drop-in fuel in IC engine applications. Hydrogen at high pressures and a catalyst generally promote deoxygenation during pyrolysis. Recently, novel heterogenous acid catalysts such as Ni-impregnated activated carbon (AC) and Ag-Co-impregnated AC catalysts were developed to produce deoxygenated fuel by pyrolysis at atmospheric pressure without using hydrogen. Homogenous base catalysts such as sodium hydroxide can also be used in pyrolysis to produce diesel-like fuel.

Oxygenated substances are a promising approach in the field of alternative fuels. A current example of such a fuel are Polyoxymethylene Dimethyl Ethers (OME). With their physical and chemical properties, alternative fuels like OME pose new challenges for diesel engine injection systems. As the heating value is low compared to conventional Diesel fuel, measures must be taken to increase the amount of fuel injected. Possible solutions include increasing the nozzle hole diameter, the injection pressure, and the number of nozzle holes. All mentioned adaptions have an influence on the mixture formation and make it necessary to examine the injection process in detail also with regard to phenomena such as cavitation. In this study, three passenger car Diesel injector nozzles are compared, two of which are adapted in terms of nozzle hole diameter (increase by 20%) and number of nozzle holes (increase from 8 to 12) in order to increase the mass flow rate of fuel to the required elevated level.

A power steering system is a component that helps the heavy-duty operator to move easily the vehicle by the hydraulic pump providing the fluid pressure and facilitating adequate operation. Some failures in the power steering system are due to external and internal factors that can reduce its life. The external factor could be identified by ocular inspection but normally to internal failures is necessary to use a hydraulic pressure flow meter. However, with this device is impossible to detect failures by the lubricant selected. This work aims to investigate the causes of power steering system seizure by using the tribological wear examination process and the lubricant characterization under some actual operation conditions.

Vehicle electrification has emerged as a pivotal area of research across diverse industries. When applied to heavy-duty vehicles (i.e., school buses), it presents a unique opportunity for the automotive sector to lead sustainability efforts. Beyond energy savings, the electrification of school buses offers advantages such as improved children's health, reduced greenhouse gas emissions, and enhanced green vehicle routing, inspiring broader adoption of eco-friendly practices. This study primarily aims to analyze the energy-saving effects and economic viability resulting from the electrification of existing school bus routes. Specifically, the research examines the drive cycles of operational bus routes and evaluates the energy demand and cost implications associated with transitioning from conventional buses (diesel buses) to electric buses or other alternative powertrains for fleet operations.

The automotive industry is moving rapidly to electrification through development of Battery Electric Vehicles (BEV). Development and sizing of the battery and powertrain requires a detailed understanding of battery cell behaviour under different conditions. Achieving this is difficult due to the range of cells available and the large range of condition variables of each cell. Equivalent Standard Circuit (ESC) are used for BEV development. However, conventional battery cell characterization testing to parameterise these models are time and resource intensive. Characterization can be performed using well-known techniques such as Hybrid Pulse Power Characterisation (HPPC) or Galvanostatic Intermittent Titration Technique (GITT) which are used to optimise parameters of an ESC model pertinent to the dynamics of its voltage response.

The effectiveness of immersion cooling for the thermal management of Electric-Vehicle (EV) batteries is crucially influenced by the thermophysical and rheological properties of the heat-transfer liquid. This study emphasizes upon the design requirements for such a fluid in terms of bulk properties, i.e., high electrical resistivity and thermal conductivity, low viscosity, but also relevant to the rheological properties maximizing the heat transfer rate. Key concepts of the implemented research constitute: (i) the promotion of vortical motion in the laminar flow regime, which, in turn, enhances heat transfer by disrupting boundary layers; (ii) vortex stabilization through the addition of viscoelasticity-inducing agents in the base heat-transfer liquid. To improve cooling efficiency, the primary objective is to maximize the achievable heat transfer rate for minimal pumping losses.

Lithium-ion and lithium-metal battery cells are susceptible to a phenomenon known as thermal runaway under failure conditions. Thermal runaway entails a rapid escalation in battery cell temperature accompanied by the emission of flammable lithium ions, particulates, electrons, hydrocarbon, and hydrogen gases. These gases pose a significant ignition risk, potentially leading to fires and endangering occupants and bystanders. Therefore, the timely detection of thermal runaway is paramount for ensuring safety in proximity to such battery systems. Traditionally, thermal runaway sensors comprise intricate assemblies of pressure, temperature, and gas sensors, strategically positioned at the pressure relief valve of battery modules. Calibration of all sensors is essential to reliably detect thermal runaway conditions.

Li-ion battery cell degradation has a strong impact on vehicle performance through performance degradation and deviation from original control calibration. Ageing leads to complex changes in resistance, arising from various contributions. This results in non-uniform resistance changes with temperature and state-of-charge (SoC) as well as altered time-based dynamic responses to current application. This study examines in detail the complex resistance changes with ageing and the dependencies on temperature, SoC and time. The paper evaluates data from a 9-month NMC/Gr cell aging study at 45°C, using both frequency and time-based methods. Electrochemical impedance spectroscopy with Distribution of Relaxation Times is employed for frequency analysis, while time domain data is extracted from monthly Hybrid Pulse Power Characterization tests using model fitting approaches.

Inadequately designed flow field layouts in bipolar plates within Proton Exchange Membrane fuel cells (PEMFCs) may lead to ineffective water removal and impede reactant transport. This work examines the typical flow channel designs found in bipolar plates of fuel cells and implements modifications to certain designs to alleviate pressure drops within the flow channels using computational fluid dynamics (CFD) analysis. These designs are optimized by changing different parameters such as size of the channel and rib width utilizing Taguchi L27 standard orthogonal array. The resultant reduction in pressure drop is anticipated to enhance the overall performance of the fuel cell. The optimal flow field design of bipolar plates (Graphite and Aluminum) are manufactured using CNC milling. Tests evaluating flexural strength, surface roughness, hardness, contact angle, and corrosion resistance are conducted to assess and compare the performance of these plates.

This work presents a simulation-based modeling of the equivalent greenhouse gas (GHG) of plugin hybrid electric vehicles (PHEVs) for real driving patterns obtained from monitoring of real vehicles in public survey data sets such as the California Household Travel Survey (CHTS). Aim of the work is to highlight differences in attainable GHG reduction by adopting a PHEV instead of a conventional vehicle (CV) for different driving patterns obtained from real-world sub-populations of vehicles. Modeling of the equivalent GHG for a trip made by a PHEV can be challenging since it not only depends on the vehicle design and driving pattern of the trip in question, but also on: i) all electric range (AER) of the PHEV, ii) “well to tank” (W2T) equivalent GHG of the electricity used to charge the battery, as well as, iii) battery depletion in previous trips since the last charging event.

Sales of plug-in vehicles (PEVs), which include battery-only electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have steadily grown in the past years amidst various incentive programs. While incentives are important, or perhaps essential during early phases of market introduction, they may not be feasibly sustainable when/if PEV sales ramp up to mass-market levels. As such, much interest and speculation exist on how soon could PEVs become cost-competitive without incentives. Research in this paper adopts a bottom-up approach for estimating sale price and total cost of ownership (TCO) for new vehicles. A critical review is also conducted for various publicly available sources of information pertaining to PEVs and conventional internal combustion-engine (CICE) vehicles. Due to the existence of some uncertainty about current costs, let alone future costs, a sensitivity analysis is conducted.

Software tools for simulations of vehicle fuel economy/energy efficiency play an important role strategic decision-making in advanced powertrains. In general, there is a trade-off between the level of detail in a numerical model of a vehicle (higher detail provides better simulation accuracy), and the computational time resources to run the model. However, even with detailed models of a vehicle, there remains some uncertainty about how the vehicle performs in the real-world. Calibration of simulation models versus real-world data is a challenging task due to variations in vehicle usage by different owners. This work utilizes datasets of real-world driving in vehicles that have been equipped with OBD/GPS loggers. The loggers record at fairly high frequency the vehicle speed, road slope, cabin heating/air-conditioning loads, as well as energy/fuel consumption.

Plug-in hybrid electric vehicles (PHEVs) have the capability to drive an appreciable fraction of their miles travelled on electric power from the grid, similar to battery-only electric vehicles (BEVs). However, unlike BEVs which cannot drive unless charged, PHEVs can automatically switch to gasoline power and operate similar to a regular (non-plug-in) hybrid electric vehicle (HEV). Though operating similar to HEV is already beneficial in terms of fuel economy, greenhouse gas (GHG) emissions and criteria pollutants compared to conventional internal combustion engine (ICE) vehicles, much of the attractiveness and allure of PHEVs comes from their capability to drive “almost like a BEV”, but without range anxiety about running out of battery charge.

Software simulation tools for vehicle fuel economy/energy efficiency can play an important role in strategic decisions about advanced powertrains. One such tool that has been developed by the National Renewable Energy Laboratory (NREL) is known as FASTSim. The philosophy of FASTSim aims to strike a difficult balance between simplifying the task of creating/editing vehicle models, fast computation time and high-fidelity simulation results. In the “baseline” version of FASTSim, which is open-source and freely available in Python or Excel, the instantaneous efficiency of an engine, motor or fuel cell is estimated via reference curves as function of power demand. The reference efficiency curve for each powertrain subsystem (e.g. for a spark-ignition engine) in baseline FASTSim has the same profile irrespective of what vehicle is being modelled, which is a compromise in accuracy in favor of ease of modeling.

Plug-in hybrid electric vehicles (PHEVs) combine some of the attractive traits of both fully electric vehicles (EVs) and non-plug-in hybrid vehicles (HVs). EV traits shared by PHEVs include the capability to charge the battery via electricity from the grid while the vehicle is parked and the ability to drive an appreciable distance without having to turn the engine on, in what is known as charge depletion mode. HV traits shared by PHEVs include the ability to use the engine to maintain the state of charge (SOC) of the batteries within certain limits, in what is known as charge sustaining mode. Charge sustaining mode allows a PHEV to drive long distances without requiring stops for electrical charging (unlike EVs) but comes at the trade-off that fuel needs to be used.

Future Automotive Systems Technology Simulator (FASTSim) is a free and open-source tool developed by National Renewable Energy Lab (NREL). Among the attractive capabilities of the FASTSim is that it can perform computationally efficient fuel economy simulations of automotive vehicles with reasonable accuracy for standard or arbitrary drive cycles. The modeling capability includes vehicles with various types of powertrains such as: conventional vehicles (CVs), hybrid-electric vehicles (HEVs), plugin hybrid electric vehicles (PHEVs) and battery-only electric vehicles (BEVs). The public version of FASTSim available from NREL is implemented in Excel, which achieves the goal of good accessibility to a broad audience, but has some limitations, including: i) bottleneck in computations when importing arbitrary drive cycles, ii) slower computations in general than other scripting or programming languages, and iii) less portable to integration with other applications and/or other platforms.

Electric drive vehicles (EDV) have the potential to greatly reduce greenhouse gas (GHG) emissions and thus, there are many policies in place to encourage the purchase and use of gasoline-hybrid, battery, plug-in hybrid, and fuel cell electric vehicles. But not all vehicles are the same, and households use vehicles in very different ways. What if policies took these differences into consideration with the goal of further reducing GHG emissions? This paper attempts to answer two questions: i) are there certain households that, by switching from a conventional vehicle to an EDV, would result in a comparatively large GHG reduction (as compared to other households making that switch), and, if so, ii) how large is the difference in GHG reductions? The paper considers over 65,000 actual GPS trip traces (generated by one-second interval recording of the speed of approximately 2,900 vehicles) collected by the 2013 California Household Travel Survey (CHTS).