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V2X technologies will become widespread in the next generation of passenger cars, and enable the development of novel vehicle control functionalities. Although a wide literature describes the energy efficiency benefits of V2X connectivity, e.g., in terms of vehicle speed profiling and platooning, there is a gap in the analysis of the potential of vehicle connectivity in enhancing the performance of active safety control systems. To highlight the impact vehicle connectivity could have on future active safety systems, this paper presents two novel control functions for connected vehicles, benefitting from the precise knowledge of the expected path and tire-road friction conditions ahead, as well as the current position of the ego vehicle. These functions, developed within recent and ongoing European projects, are: i) pre-emptive traction control; and ii) pre-emptive braking control. 

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.

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.

Traction inverters play a crucial role in the growing industry of electric vehicles. On the one hand, the traction inverter design is quite challenging and needs to pursue key design goals including high efficiency, high reliability, high power density, and low weight and cost. On the other hand, a modular and scalable design methodology to cover a wide range of vehicles is highly desirable. This article explores the optimal or near-optimal design configuration of the multilevel neutral-point-clamped legs of traction inverters for three use cases: an electric motorcycle, an electric passenger car, and an electric truck. The design is based on the use of an array of switching cells. The optimal configuration and operation of the switching cells are obtained through a weighted objective function in terms of efficiency, reliability, and simplicity. The design optimization results illustrate the modularity, scalability, and suitability of the used design approach, where a single module fits all applications, and the available degrees of freedom enable the adaptation of the design to the application and operating conditions, to maximize its efficiency and reliability.

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 paper presents an overview of different DT platforms that can be used in EV applications. A deductive comparison between model-based and data-driven DT was performed. EV main systems have been discussed regarding the usable DT platform. DT platforms used in the EV industry were addressed. Finally, the review showed the superiority of data-driven DTs over model-based DTs due to their ability to handle systems with high complexity.

The design of high-frequency transformers plays a pivotal role in electric vehicle charging systems, acting as the core component in isolated DC-DC converters. Achieving a transformer that not only operates well within the converter but also achieves high efficiency and compact size involves the balancing of numerous trade-offs. Managing the magnetic losses is essential as they dictate the efficiency but also govern the required transformer size for effective heat dissipation. This paper presents a novel transformer design method based on an exhaustive search algorithm that identifies the Pareto optimal transformers with minimised loss and weight. By introducing a Steinmetz equation filter, rapid computational speed and accuracy are combined. Loss estimations are based on experimentally validated analytical models, and the proposed methodology is experimentally validated by multiple transformer prototypes. Their operation in a dual active bridge is demonstrated by experimental measurements and circuit simulation. The results prove the effectiveness of the proposed design approach in achieving efficient and compact transformers.

This article addresses a common issue in the design of battery electric vehicles (BEVs) by introducing a comprehensive methodology for the modeling and simulation of BEVs, referred to as the “PerfECT Design Tool”. The primary objective of this study is to provide engineers and researchers with a robust and streamlined approach for the early stages of electric vehicle (EV) design, offering valuable insights into the performance, energy consumption, current flow, and thermal behavior of these advanced automotive systems.

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.

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