This paper investigates different regenerative braking strategies applied to Battery Electric Vehicles, such as series and parallel brake blends. The comparison includes energy efficiency assessment using homologation and real-world drive cycle and objective and subjective drivability evaluation. Multiple simulations are performed using a one-dimensional (1D) vehicle model developed in Simulink and a static driving simulator. The driving simulator provides a fair comparison of real-world driving since it creates repeatable highway and urban traffic conditions. These simulations compare the system energy efficiency by looking at the battery's state of charge (SOC). The drivability is assessed on top of consumption by using the static driving simulator. It is objectively measured by calculating the longitudinal acceleration change ratio over time, which occurs during the regeneration ramp-in and ramp-out, for different pedal positions and pedal gradients. The drivability is also subjectively evaluated by assessing the system's smoothness and absence of shakes during braking maneuvers and the deceleration feels while “freely” coasting at high speeds. This study clarifies the utilization of a driving simulator integrated with a model-based design apporoach to develop regenertive braking controls and braking system architectures for electrified vehicles. In the study case presented in this paper, the Series regenerative braking shows better efficiency and better drivability, especially for conditions of low accelerations lower than 0.3g.

Governments in all continents are regulating and limiting the emissions generated by the transportation system. In this scenario, diesel engines will be out of all major markets between 2030 and 2040. In Brazil, the most prominent automotive market in South America, the introduction of the PL-8 regulations imposes the auto manufacturers to introduce new propulsion technologies starting in 2025. This paper studies the architecture selection and component sizing of an electric propulsion system for a light-commercial vehicle transformation from an internal diesel combustion (IC) engine to a full battery electric vehicle (BEV). The paper investigates four different driveline architectures and compares the results with the original IC vehicle regarding longitudinal performances (e.g., acceleration, maximum speed, and gradeability), energy consumption efficiency, CO2 emissions, and the total cost of ownership. In the end, the electric vehicle is evaluated as an investment by calculating its internal rate of return (IRR), payback (PB), and return on investment ( ROI). The longitudinal performances and energy consumption efficiency are estimated using a one-dimensional (1D) model developed using Matlab/Simulink. The total cost of ownership and the projected vehicle retail price are determined based on the system sizing defined in this study and cost models from the literature review.

The increase in pollution and carbon dioxide in the transportation sector motivates many countries to establish rules to reduce the emission and carbon footprint. The partial or total electrification of the automotive propulsion system is mandatory to minimize the impact of transportation on the environment. There are multiple possible architectures to electrify the vehicle propulsion system, with different degrees of electrification, and each of the architectures can provide a different balance between energy efficiency, vehicle performance, comfort, drivability, and safety. This article aims to present the definition of the automotive propulsion system electrification, the various degrees of electrification and operation modes, and the possible configurations of an electrified propulsion system by the electric machine position. This article also introduces the definition of the subsystems of the propulsion systems and their components, with an explanation of the subsystem and each of their parts. Based on the state-of-the-art technology development, it intends to present the industry's common sense and knowledge on automotive propulsion system electrification and proposes a process to follow in the electrified propulsion system architecture selection.

In the last decade, the automotive industry has undergone a paradigm shift towards electrification. Electric vehicles have become increasingly popular, but so far, they have almost solely utilized single-ratio gearboxes. The use of multiple gear ratios has several potential benefits, including enabling the electric traction machine and inverter to operate in a more efficient region, increasing vehicle acceleration, gradeability, and top speed, and reducing overall traction system mass and volume. Performance vehicles, light to heavy-duty trucks, and buses may especially benefit from multi-speed gearboxes due to their high torque and power requirements. This paper covers the fundamentals of applying multi-speed gearboxes to EVs, the latest designs, and future trends. The efforts of both academia and industry in this field are covered. A range of topics are discussed, including gearbox topologies, gear ratio selection, gearbox losses, noise vibration and harshness, gearbox control, shift scheduling, and regenerative braking. Prior studies are presented showing that depending on the drive cycle, vehicle type, and gearbox configuration, drivetrain energy consumption may be reduced slightly or increased anywhere from a few percent to thirty percent when utilizing a multi-speed configuration. While multi-speed EV traction systems do show considerable promise, more investigation is needed to conclusively determine in what cases they can outperform highly optimized single-speed systems.

The presented study aims to propose a new method of driving behavior recognition using a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) in combination with an Energy Consumption Minimization Strategy (ECMS), resulting in a Multi-Mode Adaptive Energy Consumption Minimization Strategy (A-ECMS) for a P1-P2 series parallel Hybrid Electric Vehicle (HEV). Novelty is achieved by focusing on efficient driving mode switching instead of single mode optimization. Therefore, offline optimization was performed over different driving situations to gather different calibrations, which will be utilized in the hybrid propulsion system master controller with the purpose of determining the most fuel-efficient driving mode based on the current driving behavior. A LSTM RNN is used to classify the current driving behavior online based on vehicle speed, acceleration and distance per stop. This paper compares the effect of the proposed method in different driving conditions in order to investigate the benefits and applicability of such a control strategy. Simulations were performed representing a conventional engine-driven vehicle and a hybrid electric vehicle equipped with a P1-P2 series-parallel hybrid propulsion systems. Improvement in fuel consumption against a conventional vehicle of around 52% in average over all driving cycles can be achieved through this approach.

Using experimental data from a hybrid energy storage system (HESS) composed of two 12V batteries in parallel 60Ah Lead acid (LA) and 8Ah Lithium Iron Phosphate (LFP)–a machine learning approach known as feedforward backpropagation artificial neural network (BPNN) was developed to estimate the state-of-charge (SOC) of both batteries using only one neural network structure. In order to minimize the SOC estimation error the BPNN was trained using a set of five different homologation automotive drive cycles and tested on a sixth drive cycle known as worldwide harmonized light vehicles test cycle (WLTC) to compute the estimation accuracy. A rootmean- squared error (RMSE) of 0.33% and 0.84% is obtained over the test cycle for the LFP and the LA batteries, respectively. The results were then compared to the estimation obtained from a commercially available battery management system (BMS) showing better performance for the proposed approach.