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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.

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.

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.

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.

Autonomous vehicles in formula student competition is a relatively new competition, with most of the teams testing new concepts every year with their challenger for the season. A background search conducted reflects the various concept changes offered by the FS teams in Formula student Germany each year. Hence, it can be concluded that the teams are uncertain about many concepts in an autonomous vehicle. This paper explores one such aspect; the choice of controller governs the steering capabilities of the autonomous vehicle. An MPC controller is used to build a basic controller model for the autonomous vehicle in the formula student competition. A bicycle model representative of the Oxford Brookes Racing team's electric vehicle is modeled, and the MPC controller is used to check various vehicle dynamic parameters in Simulink.

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.

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.

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.