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Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

Alternative fuel vehicles (AFV) are a promising alternative to reduce greenhouse gas emissions in the transportation sector and ultimately, to contribute to a more sustainable society. Within the current commitments of the Brazilian Government to international agreements, in terms of emissions reductions by 2030, some new policies have been implemented, in order to incentivize the diffusion of AFV in the country. Yet, how will these policies contribute to the goal of increasing its fleet in the long-term, how long will it take and what are the differences (if any) among the policies, in terms of their effectiveness, are questions that remain unanswered. In this sense, the main aim of this paper is to investigate the impact of public policies in the long-term diffusion dynamics of AFV in Brazil. In order to do so, a system dynamics model is developed, based on the Generalized Bass Diffusion model, the Cobb-Douglas function and the learning curve theory. The model is used to test four different policies and some uncertainties related to: (i) the exemption of the import duty; (ii) the reduction of the motor vehicle property tax; (iii) the exemption of the tax on manufacturing goods; and (iv) a banning regulation for internal combustion engine vehicles in the long-term. The results highlight the importance of the current incentive policies but at the same time call for reinforcing efforts in order to increase the AFV fleet to significant values by 2030. Conversely, the banning regulation obtains a higher diffusion rate, but only achieves significant values by 2060. The paper concludes by showing the main contributions and policy implications of the effectiveness of the four policies studied as well as some suggestions for future work. [ABSTRACT FROM AUTHOR]