In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory.

Pure-pursuit algorithm is a popular trajectory tracking algorithm, widely used in mobile robotics and vehicular control for numerous reasons. The operation is simple and straightforward as it depends only on the kinematic model of the target mechanical system. The algorithm can be tuned by choosing look-ahead distance of points of the reference trajectory. On the other hand, pure-pursuit suffers from weaknesses highly attributed to improper selection of look-ahead distance, resulting in poor tracking performance. Recent developments focused on overcoming this drawback by dynamical change of this parameter. In this paper, we propose three enhancements to the original pure-pursuit algorithm, aiming to handle dynamic selection of look-ahead distance by selecting multiple goals, modifying look-ahead distance according to the curvatures and adjusting lateral deviation. Our work was motivated by an ongoing autonomous vehicle research at our university.

In this paper, we present an enhancement to the popular pure-pursuit algorithm, widely used in robotics and automotive applications. The original algorithm is simple and straightforward as it depends only on the very basic attributes of the kinematic model of the target mechanical system. The algorithm is usually tuned by choosing a look-ahead distance of points from the reference trajectory. On the other hand, pure-pursuit suffers from considerable tuning weaknesses highly attributed to improper selection of look-ahead distance, resulting in poor tracking performance. Our method proposes the dynamic change of lookahead distance based on selecting multiple-goal points, thus aiming a curvature fitting more the reference trajectory. Our work was motivated by an ongoing robotics and autonomous vehicle research project at our university.

In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage.

In this paper, determination of optimized regenerative braking-torque function and application in energy efficient driving strategies is presented. The study investigates a lightweight electric vehicle developed for the Shell Eco-Marathon. The measurement-based simulation model was implemented in the MATLAB/Simulink environment and used to establish the optimization. The optimization of braking-torque function was performed to maximize the recuperated energy. The determined braking-torque function was applied in a driving strategy optimization framework. The extended driving strategy optimization model is suitable for energy consumption minimization in a designated track. The driving strategy optimization was created for the TT Circuit Assen, where the 2022 Shell Eco-Marathon competition was hosted. The extended optimization resulted in a 2.97% improvement in energy consumption when compared to the result previously achieved, which shows the feasibility of the proposed methodology and optimization model.