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The work presented investigates the effect of road gradient, head-wind, horizontal road curvature, changes in tyre rolling radius, vehicle drag co-efficient and vehicle weight on real-world emission levels of a modern EURO-IV vehicle. A validated steady-state engine performance map based vehicle modeling approach has been used for the analysis. The results showed that a generalized correction factor to include the effect of road-gradient on real-world emission levels might not yield accurate results, since the emission levels are strongly dependent on the position of the vehicle operating parameters on the engine maps. In addition, it also demonstrated that the inclusion of horizontal road curvature such as roundabouts and traffic islands are essential for the estimation of the real-world emission levels.

With a push to continuously develop traditional engine technology efficiencies and meet stringent emissions requirements, there is a need to improve the precision of injection rate measurement used to characterise the performance of the fuel injectors. New challenges in precisely characterising injection rate present themselves to the Original Equipment Manufacturers (OEMs), with the additional requirements to measure multiple injection strategies, increased injection pressure and rate features. One commonly used method of measurement is the rate tube injection analyser; it measures the pressure wave caused by the injection within a column of stationary fluid. In a rate tube, one of the significant sources of signal distortion is a result of the injected fluid pressure waves reflected back from the tube termination.

Electrification is one of the main solutions for the decarbonization of the transport system. It is employed widely by the automotive industry in light- and medium-duty vehicles and recently started to be considered in heavy-duty applications. However, powertrain electrification of heavy-duty vehicles, especially for Class 8 trucks, is very challenging. In this study, the battery-electric powertrain energy and technical performance of a DAF 44 tones truck are compared for two different electric drives. The case study truck is modeled in AVL CRUISE M software and the battery electric powertrain is evaluated for long haul driving cycle. The minimum number of battery packs is determined by defining the lowest energy consumption of the powertrain designed for the proposed drive cycle. Also, a transient analysis is accomplished to investigate the impact of various electric drives on energy consumption and performance of the proposed electric powertrain.

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.

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.

Aerodynamic drag reduction is a critical part in the design of a novel electric, entry-level, formula car due to the modest energy density provided by the contemporary Lithium-ion battery cells. In order to improve track performance, aerodynamic development must focus on components which do not generate a considerable amount of downforce. Rotating front wheels are identified as the least aerodynamic part of the race car, since it is responsible for the third of the overall drag forces and producing moderate amounts of lift. In the present study, a parameterized wheel pod geometry is used to improve the overall aerodynamic performance of an open-wheel race car. The model is driven by seven parameters, which entails huge flexibility of the bodywork design. First, an unsteady Computational Fluid Dynamics (CFD) simulation was developed and validated to visualize the oscillating flow behavior and obtain averaged surface force measurements.

Autonomous vehicles have been shown to increase safety for drivers, passengers, and pedestrians and can also be used to maximize traffic flow, thereby reducing emissions and congestion. At the same time, governments around the world are promoting the usage of battery electric vehicles (BEVs) to reduce and control the emissions of CO2. This has made the development of autonomous vehicles and electric vehicles a very active research area and has prompted a significant amount of government funding. This article presents the detailed design of a low-cost platform for the development of an autonomous electric vehicle. In particular, it focuses on the design of the electrical architecture and the control strategy, tailored around the usage of affordable sensors and actuators. The specifications of the components are extensively discussed in relation to the performance target.

This study analyzes the performance of the Energy Recovery System (ERS) of a Formula One car (F1) based on the qualification performance of 19 drivers for the first calendar race of the 2019 FIA Formula One World Championship®. In this study, the race circuit analysed was split into different sectors to examine the energy transfer between the Motor Generator Unit-Kinetic (MGU-K) and the Energy Storage (ES) systems. Positive Kinetic Energy (PKE) concept was used for estimating the energy deployment potential of the ERS along with numerical simulations for estimating the energy recovering potential. This investigation highlights the strategies used by different drivers and the effect of driver to driver variation on their ERS performance during qualification. The methodology demonstrated in this study is able to identify the correlation between the unique driving style of individual drivers and the ERS strategies used by the teams for maximizing the performance of their car.

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.

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.

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.

Lithium-ion cells operate under a narrow range of voltage, current, and temperature limits, which requires a battery management system (BMS) to sense, control, and balance the battery pack. The state of power (SOP) estimation is a fundamental algorithm of the BMS. It operates as a dynamic safety limit, preventing rapid ageing and optimizing power delivery. SOP estimation relies on predictive algorithms to determine charge and discharge power limits sustainable within a specified time frame, ensuring the cell design constraints are not violated. This paper explores various approaches for real-time deployment of SOP estimation algorithms for a high-power lithium-ion battery (LIB) with a low-cost microcontroller. The algorithms are based on a root-finding approach and a first-order equivalent circuit model (ECM) of the battery.

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.

Lithium-ion batteries are ubiquitous in modern energy storage applications, necessitating efficient methods for assessing their state and performance. This study explores a non-destructive approach to extract vital battery parameters using machine learning techniques applied to simulated Electrochemical Impedance Spectroscopy (EIS) data. EIS is a powerful diagnostic tool for batteries and provides a safe and repeatable alternative to the physical intrusion of battery dismantling, which could alter the batteries properties. The research focuses on the design and training of machine learning models for accurate prediction of battery parameters within the widely used P2D model. By leveraging the power of machine learning, this approach aims to accurately characterize the battery parameters using an electrochemical model as opposed to the less accurate equivalent circuit models, contributing to the reliability and longevity of lithium-ion batteries in diverse applications.