Vehicle Operations

This review article examines the deterministic control model and centralized control model, the types of EV models, and their tabular comparison. Additionally, expressing the communication standards to deal with compatibility challenges in charging stations, the effects of EV integration with the power grid, and various methods such as smart charging, dumb charging, and flexible charging are the main goals of this review article.

The article delves into the evolution of battery chemistries, energy densities, and thermal management systems, which collectively impact battery life and overall vehicle longevity. Additionally, insights into battery recycling and second-life applications are discussed as essential strategies to mitigate environmental impacts and enhance the sustainability of EV operations.

This paper presents a novel Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) infrastructure designed to optimize energy flow between electric vehicles and the electrical grid. The system is equipped with a bidirectional converter and a three-phase inverter/rectifier, minimizing the number of switches to reduce weight and size. A model predictive control (MPC) scheme is introduced to regulate the converter's operation and maintain grid stability, while also functioning as an active power filter when idle. Simulation results using MATLAB/Simulink demonstrate the system's efficiency, verifying its ability to manage energy transfer and mitigate harmonic distortion effectively.

In this study, the basic structure of the pure electric command vehicle is studied, the main components of the command vehicle power system, namely the selection of the drive motor and the power battery, are analyzed, and the main parameters of the drive motor and the power battery are designed and calculated. The calculation results show that the power and transmission system developed in this paper meets the design requirements, and the design scheme is feasible and reasonable.

This article explores battery health monitoring in electric vehicles (EVs) using machine learning to address challenges in battery durability and enable new business models. It introduces a virtual battery prototype that applies supervised learning methods, such as Random Forest and Deep Neural Network regression, to estimate real-time energy slack and monitor battery health. The study also presents a carbon balance optimization application, aiming to minimize carbon emissions and charging costs for EV fleets through grid optimization. The model enables continuous battery health monitoring, opening opportunities for innovative commercial use cases for EV users, fleet managers, and grid operators.

The scope of this study is to demonstrate that on-board electric powertrains with torsional dynamics of the half-shafts have the potential for effective compensation, thanks to the road profile preview. This paper presents a proof-of-concept nonlinear model predictive controller (NMPC) with road preview, which is assessed with a validated simulation model of an all-wheel drive electric vehicle. Three powertrain layouts are considered, with four in-wheel, four on-board, and two on-board electric machines. The control function is evaluated along multiple manoeuvres, through comfort-related key performance indicators (KPIs) that, for the four on-board layout along a road step test at 40 km/h, highlight >80% improvements. Finally, the real-time implementability of the algorithms is demonstrated, and preliminary experiments are conducted on an electric quadricycle prototype, with more than halved oscillations of the relevant variables.

The aim of the present work is to establish which are the main driveline components affecting the filtering behavior of the transmission and how their parameters can be tuned in order to improve the vehicle ability to respond to driver’s different demands without negative impact on his comfort. A complete nonlinear coupled torsional and longitudinal vehicle dynamic model is proposed to this end. The model is validated both in time and frequency domain and allows linearization of its nonlinear components.

The introduction of active safety systems and advanced driver assistance systems has enhanced the control authority over the vehicle dynamics through specialized actuators, enabling, for instance, independent wheel torque control. During emergency situations, these systems step in to aid the driver by limiting vehicle response to a stable and controllable range of low longitudinal tire slips and slip angles. This approach makes vehicle behavior predictable and manageable for the average human driver; however, it is conservative in case of driving automation. In fact, past research has shown that exceeding the operational boundaries of conventional active safety systems enables trajectories that are otherwise unattainable. 

This paper presents a nonlinear model predictive controller (NMPC) for path tracking (PT), which integrates steering, front-to-total longitudinal tire force distribution, and direct yaw moment actuation, and can operate beyond the limit of handling, e.g., to induce drift, if this is beneficial to PT. Simulation results of emergency conditions in an intersection scenario highlight that the proposed solution provides significant safety improvements, when compared to the concurrent operation of PT algorithms and the current generation of vehicle stability controllers.

State-of-the-art antilock braking systems (ABS) are reactive, i.e., they activate after detecting that wheels tend to lock in braking. With vehicle-to-everything (V2X) connectivity becoming a reality, it will be possible to gather information on the tire–road friction conditions ahead, and use these data to enhance wheel slip control performance, especially during abrupt friction level variations. This study presents a nonlinear model predictive controller (NMPC) for ABS with preview of the tire–road friction profile. The potential benefits, optimal prediction horizon, and robustness of the preview algorithm are evaluated for different dynamic characteristics of the brake actuation system, through an experimentally validated simulation model. Proof-of-concept experiments with an electric vehicle prototype highlight the real-time capability of the proposed NMPC ABS, and the associated wheel slip control performance improvements in braking maneuvers with high-to-low friction transitions.

V2X connectivity and powertrain electrification are emerging trends in the automotive sector, which enable the implementation of new control solutions. Most of the production electric vehicles have centralized powertrain architectures consisting of a single central on-board motor, a single-speed transmission, an open differential, half-shafts, and constant velocity joints. The torsional drivetrain dynamics and wheel dynamics are influenced by the open differential, especially in split-μμ scenarios, i.e., with different tire-road friction coefficients on the two wheels of the same axle, and are attenuated by the so-called anti-jerk controllers. Although a rather extensive literature discusses traction control formulations for individual wheel slip control, there is a knowledge gap on: a) model based traction controllers for centralized powertrains; and b) traction controllers using the preview of the expected tire-road friction condition ahead, e.g., obtained through V2X, for enhancing the wheel slip tracking performance. This study presents nonlinear model predictive control formulations for traction control and anti-jerk control in electric powertrains with central motor and open differential, and benefitting from the preview of the tire-road friction level. The simulation results in straight line and cornering conditions, obtained with an experimentally validated vehicle model, as well as the proof-of-concept experiments on an electric quadricycle prototype, highlight the benefits of the novel controllers.