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Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling.

Li-ion batteries (LIBs) optimum performance and lifetime depend on temperature, with the commonly suggested operating temperature being in the range of 25 to 40 °C. It's also crucial to keep the temperature difference between battery cells below 5°C. Operation at different temperature ranges can adversely affect or degrade the performance and lifetime of LIBs. A battery thermal management system (BTMS) is essential for keeping the battery temperature within the optimum range. This paper aims to develop and analyze the BTMS for an electric heavy-duty truck. To achieve this aim, battery cells and modules are modelled in ANSYS Fluent software. Validation with experimental results and mesh sensitivity studies are also performed to increase confidence in simulation data. The model is then analyzed for a specific cooling systems to investigate its effect on battery thermal performance during the operation.

This paper presents a method for analysing the characteristics of nano-scale particles emitted from a 1.6 Litre, 4-stroke, gasoline direct injection (GDI) and turbocharged spark ignition engine fitted with a three-way catalytic converter. Ensemble Empirical Mode Decomposition (EEMD) is employed in this work to decompose the nano-scale particle size spectrums obtained using a differential mobility spectrometer (DMS) into Intrinsic Mode Functions (IMF). Fast Fourier Transform (FFT) is then applied to each IMF to compute its frequency content. The results show a strong correlation between the IMFs of specific particle ranges and the IMFs of the total particle count at various speed and load operating conditions. Hence, it is possible to characterise the influence of specific nano-scale particle ranges on the total particulate matter signal by analysing the frequency components of its IMFs using the EEMD-FFT method.

The number of electric vehicles is increasing in line with the global carbon reduction targets. More households are installing electric charging points to complement the existing charging infrastructure. The increasing electricity prices affected by the global energy/economic crisis however pushed more households towards coupling their charging points with renewable energy generation and storage systems to manage the supply and demand of energy more effectively. In this study, an electric charging station equipped with Photovoltaic panels and an electric storage system utilising second-life Electric Vehicles (EV) batteries is designed and analysed. Various electricity generation capacities are considered to be installed on the roof of the case study building ranging from 5m2 and 20m2. The second-life batteries are disposed from EVs with an 80% state of health. MATLAB Simulink is used for mathematical modelling of system.

To decarbonize heavy-duty vehicles solely through electrification with batteries is challenging as large batteries are required for a meaningful range, severely impacting payload. Employment of hybrid electric powertrains where fuel cells are integrated with batteries can deliver increased range and payload. However, the energy balance between the fuel cell and the battery needs to be analyzed to optimize the sizing of the powertrain components. This study has performed a multi-objective optimization using genetic algorithm to obtain the optimum range and hydrogen consumption for a DAF 44 tons heavy-duty truck. The proposed truck powertrain has been numerically modelled in AVL CRUISE M software. The electric drive from Involution Technologies Ltd and Bramble Energy Ltd’s printed circuit board fuel cell (PCBFC) are used in the model.

Drive cycles have been the official way to create standardized comparisons of fuel economy and emission levels between vehicles. Since the 1970s these have evolved to be more representative of real-world driving, with today’s standard being the World Harmonized Light Vehicle Testing Procedure. The performance of battery electric vehicles which consist of electric drives, battery, regenerative braking and their management systems may differ when compared to that of vehicles powered by conventional internal combustion engines. However, drive cycles used for evaluating the performance of vehicles, were originally developed for conventional powered vehicles. Moreover, the kinematic parameters that can distinguish the real-world performance of the differently powered vehicles are not fully known. This work aims to investigate the difference between vehicles powered by pure internal combustion engine, electric hybrid and pure electric drive.

Formula (1) vehicles have transitioned from E5 to E10 fuel for the 2022 season to reduce carbon emissions and by 2026 the vehicles are required to use 100% sustainable fuels. The aim of this paper is to identify the operating envelope of the F1 power unit for E10-E100 fuel and the resulting emission levels for these fuel compositions using numerical simulations. To achieve this aim an F1 engine model has been developed in GT-Suite with reference to the FIA 2022 Technical Regulations. The combustion model has been validated using data obtained from literature relating to laminar and turbulent flame speed, friction and heat transfer characteristics within the combustion chamber. One of the main challenges of using ethanol-based fuels is the increased levels of formaldehyde in the tailpipe.

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles.

Electrification of the transportation sector requires an energy-efficient electric powertrain supported by renewable sources of energy to limit the use of fossil fuels. However, the integration of battery electric powertrains in heavy-duty trucks seems more challenging than other types due to the high battery demand and negative impacts on the truck’s cargo capacity. In this paper, the battery sizing of a 41-tons Mercedes Actros truck is performed based on battery safety zone operating conditions. A parametric study is conducted to assess the impacts of sizing on a truck’s total cargo capacity as well as the body dynamic parameters. The numerical model of the Mercedes Actros electric powertrain is developed in AVL CRUISETM M software. The hybrid pulsed power characterization tests are performed on 3Ah lithium-ion NMC cells in the lab for fitting the second-order equivalent circuit model’s parameters used in the analysis.

Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory’s FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls.

Internal combustion (IC) engines are the most common power unit technology found in road vehicles. The process of combustion within IC engines is linked to the output torque and overall powertrain performance. This work presents a method of analysing the parameters of cylinder pressure and crankshaft instantaneous speed signals obtained from a turbocharged, 4-stroke, 4-cylinder, 1.6 Litre, spark ignition, gasoline direct injection engine at various speed and load operating conditions. Whereas cepstrum analysis is used in the present work to extract critical features characterising the combustion process. Cepstrum analysis showed that the location of maximum heat release can be directly obtained from the quefrency of the instantaneous crank speed. This paper presents a systematic scheme for applying cepstrum for obtaining combustion features from the instantaneous crank speed signal.

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation.

While the shift to vehicle electrification plays a pivotal role in governments’ targets towards carbon neutrality, there exists certain technical challenges that need to be addressed. The motorsport car industry is also affected by this policy with the electric cars being included in the formula SAE and formula E competitions as one of the main categories. Moreover, there is a gap in the literature in energy assessment of the electric powertrain used in Formula SAE (FSAE) and Formula Student (FS) cars. In this paper, a Formula Student electric car powertrain was designed as a case study for energy analysis. The proposed electric powertrain is equipped with a four-wheel drive system. The vehicle was modelled in AVL CRUISE M software using technical and measured lab data as input parameters. Simulations were run in a transient driving cycle for a real circuit layout used in previous SAE competitions.

Formula One (F1) is considered to be the forefront of innovation for the automotive and motorsport industry. One of the key provisions has been towards the inclusion of the Energy Recovery System (ERS) since 2014 in F1 regulations. ERS comprises Motor Generator Unit-Heat (MGU-H), Motor Generator Unit-Kinetic (MGU-K) and an Energy Storage (ES). This has not only converted the conventional powertrain into a hybrid power-split device, but also imposed constraints on the fuel energy available, energy recovered and deployed by MGU-K, and charge stored in ES, along with various other parameters. Although the objective for a F1 race is to minimize lap-time, it is obvious that there is no unique control path or decision to meet this objective. This builds up needs to optimally control the power-split and energy of the system.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

The COVID-19 pandemic has forced governments to implement rigorous containment measures on reduction or cessation of human mobility, transportation and economic activities, to control the spread of the virus. This is considered as a unique opportunity to study the impact of local lockdowns periods, especially, on the vehicle emission levels, and urban air quality in cities with high pollution levels, such as Bogota, Colombia. The first case was confirmed in Colombia on March 6, 2020, since then to prevent its propagation, the government declared a national lockdown starting from March 20 until August 31, 2020. Therefore, this study attempted to analyse the air quality in Bogota by assessing the concentrations of the atmospheric pollutants NO₂, SO₂, O₃, CO, PM₂.₅ and PM₁₀ during the lockdown period and the corresponding concentrations levels during the same period in 2018 and 2019. The data for this pilot study was obtained from the air quality monitoring stations of Bogota.

Internal combustion (IC) engines are the current dominant power source used in the automotive industry for hybrid vehicles. The combustion process of IC engines involves various parameters, which are linked to the overall performance of the driveline. Therefore, finding the frequency coupling between the manifold pressure, in-cylinder pressure and output crankshaft speed will provide an insight into the reasons for torque fluctuations and its effect on driveline performance. The present work introduces a methodology to analyze cylinder pressure, manifold pressure and instantaneous crank speed signals measured from a 4 cylinder, 1.6 Litre, Gasoline Direct Injection Engine at different speed conditions to identify the frequency coupling between these signals. This work uses Ensemble Empirical Mode Decomposition (EEMD) as a de-noising method and Bispectral analysis for examining the presence of a frequency coupling from the signals.

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate.

Electric Vehicles (EVs) are considered to be a worthy alternative to automobiles powered by internal combustion engines to achieve the goal of sustainable transportation. For their many known advantages, Li-ion cells are considered to be the most practical energy storage solution for the purpose of EVs propulsion currently. The capability of Li-ion cells to store energy in extreme cold operating temperatures is significantly lower than that at nominal operating temperatures due to greater power losses at cold temperatures. Therefore, it leads to degradation of performance of EVs in sub-zero temperatures. The present work proposes a novel approach to use numerical simulation technique to build an EV model based on BMW i3 using GT Suite at sub-zero temperatures. The model is validated against experimental data obtained from Argonne National Laboratory for US06, HWY and UDDS legislative drive cycles.

A novel surrogate model is presented, which predicts the engine-out Particle Number (PN) emissions of a light-duty, spray-guided, turbo-charged, GDI engine. The model is developed through extensive CFD analysis, carried out using the Siemens Simcenter STAR-CD, and considers a range of part-load operating conditions and single-variable sweeps where control parameters such as start of injection and injection pressure are varied in isolation. The work is attached to the Ford-led APC6 DYNAMO project, which aims to improve efficiency and reduce harmful emissions from the next generation of gasoline engines. The CFD work focused on the air exchange, fuel spray and mixture preparation stages of the engine cycle. A combined Rosin-Rammler and Reitz-Diwakar model, calibrated over a wide range of injection pressure, is used to model fuel atomization and secondary droplets break-up.