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.

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.

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.

Electric vehicles (EVs) are vital in the transition toward a sustainable and carbon-neutral future. However, the widespread adoption of EVs currently depends on the convenience of the charging process and the availability of their charging infrastructure. Consequently, onboard chargers (OBCs), offering an ac-charging solution built into most EVs, have gained significant attention. Furthermore, bidirectional OBCs enable reverse power flow, whereby the EV battery can be used to power various devices, homes, or even the electric grid. However, as the trend towards bidirectional OBCs becomes evident, new power converter design challenges arise, intensifying the need for high-efficiency, compact and cost-competitive solutions. This article extensively reviews the state-of-the-art bidirectional on-board chargers by analyzing over 500 publications, identifying the key trends, challenges, and research opportunities that will influence the development of next-generation bidirectional OBCs. Hence, various strategies to achieve cutting-edge performance are deducted. This includes the rise of high-voltage batteries, the integration of powertrains, the growing adoption of wide-bandgap semiconductors, and the use of integrated planar magnetic components, all aiming to enhance efficiency and power density. This article is accompanied by a CSV file recording all pertinent references to support future research, statistical analysis, and other contributions.