Axial-flux permanent-magnet (AFPM) machines are becoming an increasingly promising solution for electromechanical systems requiring high power density. In particular, their use is expanding to electric vehicles (EVs), the aerospace industry, and advanced industrial applications, such as renewable energy applications. Their compact design, high torque-to-mass ratio, and relatively high efficiency make AFPM machines an attractive alternative to traditional radial-flux solutions. However, their integration for widespread application remains limited due to challenges in design, manufacturing, thermal management, and control systems, which ultimately also have an economic impact. This article presents a comprehensive and systematic review of AFPM machines, covering key aspects, including topology classification, design methodologies, electromagnetic modelling, optimisation methods, materials and manufacturing processes, and advanced control strategies. A structured, multi-level classification of AFPM machines is presented, incorporating stator and rotor configurations, magnetic circuit structures, winding types, and materials, thereby providing a unified overview of existing designs. Furthermore, the article presents an in-depth analysis of the sizing equations used to calculate and estimate the parameters, approaches to electromagnetic modelling (including the finite element method and magnetic equivalent circuits), and modern optimisation methods based on artificial intelligence. Particular attention is paid to materials science and new manufacturing technologies, such as soft magnetic composites, printed circuit board stators, and additive manufacturing, as well as to thermal management solutions required for high-power-density applications. This work provides a unified reference framework for researchers and engineers and outlines future directions for the development and industrial adoption of AFPM machines.

Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy efficiency and driving comfort. The improvements are evaluated by objective performance indicators. In particular, it was found that the controller recovers more kinetic energy during braking maneuvers and lowers driver stress by up to over 90 % fewer mandatory pedal changes compared to already industrialized approaches.

The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather information about real operation, and to support update in the field. This paper introduces the collaborative development framework introduced in the European research program Collaborative Development Framework for electric-based Software-Defined Vehicles (CODE4EV).

This study investigates the influence of road-induced vertical excitation on air-gap eccentricity in in-wheel permanent magnet synchronous motors. A coupled electromechanical simulation framework is developed by integrating a field-oriented controlled PMSM model, a quarter-vehicle vertical dynamics model, and stochastic road roughness generated according to the ISO 8608. The proposed motor model is validated against experimental data obtained from a dynamometer test bench. Mixed eccentricity conditions are introduced to investigate how road excitation affects air-gap variation and motor current characteristics. A normalized torque-ripple current index is then extracted from the time-domain features of the q-axis current iq, which can be readily calculated from the phase currents and rotor position available in conventional inverter drives without requiring additional sensors. The simulation results reveal that road excitation significantly increases the fluctuation of air-gap eccentricity and amplifies torque-ripple-related current variations compared with no-road conditions. Furthermore, the proposed index increases consistently with eccentricity severity, while rougher road profiles shift the current response toward higher abnormality levels. These findings demonstrate that road–motor coupling has a significant impact on electrical fault signatures and should be considered when developing current-based condition monitoring methods for in-wheel PMSMs. The proposed framework provides a validated basis for evaluating eccentricity faults and supports the development of robust fault diagnosis techniques under realistic vehicle operating conditions.

Brake wear particles are an increasingly relevant source of traffic-related particulate emissions and are addressed by the recently introduced Euro 7 emission regulation. Airborne fractions of brake wear emissions, in particular, have been associated with adverse effects on human health and other organisms. Although several brake particle mitigation strategies have demonstrated promising results under controlled laboratory conditions, their effectiveness under variable open-road driving conditions remains insufficiently understood. This study therefore investigates the transfer of two test-bench-validated mitigation strategies to a fully instrumented passenger vehicle capable of measuring brake particle number (PN) and particulate mass (PM) emissions. The first strategy is a passive approach based on a modified brake pad–disc material pairing, while the second is an active filtration system that extracts particle-laden air directly from the brake friction zone. Both approaches were evaluated during two open-road driving cycles: a real driving emissions (RDE)-compliant cycle and a more dynamic cycle characterized by higher brake stress. Airborne particle emissions were measured over a size range from 300 nm to 10 µm. During the RDE-compliant cycle, the passive approach reduced PN and PM emissions by 44% and 94%, respectively, compared with the reference brake system. Under the higher thermal and mechanical loads of the dynamic cycle, the reductions decreased to 10% for PN and 64% for PM. The active filtration system achieved an increase in PN of 4% in RDE conditions and 11% under high-severity driving. Nevertheless, PM emissions were reduced by 23–97%, depending on its operating mode of the filtration system and the associated airflow and energy demand. For high-severity driving, the PM emissions have been reduced by 40% compared to the reference braking system. These results show that both mitigation approaches hold the potential to reduce brake particle emissions under open-road conditions, although their effectiveness depends strongly on brake load and system operation. The study extends previous laboratory-based investigations by directly comparing passive and active mitigation strategies on the same vehicle under real-world driving conditions.

This paper presents a novel approach to slip ratio control on roads with differing friction coefficients on either side. The proposed methodology uses super-twisting control with an extended sliding variable that considers lateral acceleration in order to manage lateral vehicle movement effectively. Through simulation studies we demonstrate the importance of this extended sliding variable in scenarios without alternative lateral control mechanisms. Our results show that including lateral acceleration in the control strategy improves vehicle performance.

While tyre abrasion is about to be regulated as well as particle emissions emission of friction brakes, the tyre and road wear particles gain more attention in research. In the past, emission behaviour of heavy duty vehicles was estimated on extrapolation out of abrasion measurements. In order to close this knowledge gap, this study aims to make a first comparison of the TRWP emission behaviour of light duty vehicles and heavy duty vehicles under real driving conditions. The results of this study cannot confirm the big difference in emission factors between LDV´s and HDV´s. However this study also highlights the limits of the conducted measurements, suggesting, that under current conditions, emission factors should not be estimated because of sampling and measurement method limitations, which need further refinement.

The automotive industry is evolving toward Software-Defined Vehicles (SDVs) enabled by centralized computing, cloud integration, and Over-the-Air (OTA) updates. Yet, prevailing SDV and Internet of Vehicles (IoV) simulators often treat each vehicle as a single monolithic node, obscuring the interplay between internal vehicle modules and the surrounding infrastructure in dense urban scenarios. This work proposes a modular SDV reference architecture embedded in a Software-Defined Internet of Vehicles (SD-IoV) framework together with a Software-in-the-Loop (SiL) co-simulation testbed built on Objective Modular Network Testbed in C++ (OMNeT++), Simulation of Urban MObility (SUMO), and Vehicles in Network Simulation (Veins). The architecture decouples perception, communication, decision, and actuation into typed replaceable modules and instantiates them across six co-existing agent types: an SDV; two human-driver vehicle classes with cognition modelled as a multi-stage Eye–Ear–Brain–Hand–Foot pipeline with reaction-delay sampling; a public transport bus; a Roadside Unit (RSU); and a Traffic Light (TL). Three platform-level mechanisms connect the agents to the infrastructure: a single shared world model with a three-layer line-of-sight funnel that serves visual-sensor queries and reuses the building polygons of the wireless shadowing model; a dual-CPU mobile-fog node implementing a cycles-per-frequency workload model with explicit end-to-end latency decomposition; and a three-plane intersection coordination fabric that combines 802.11p wireless with a wired RSU-to-TL star and a wired peer mesh between adjacent TLs. The initial results confirm that the implemented message paths and module interactions behave as specified, including directional Signal Phase and Timing (SPaT) reception, cross-junction handover, bus-side fog-offload latency accounting, and passive identification of Vehicle-to-Everything (V2X)-silent vehicles. Several architecture elements are specified but deliberately not exercised in the present evaluation and remain design targets for future work: the Roadside Unit (RSU) route planning and fog computing companion (and any multi-tier offloading comparison), non-line-of-sight SPaT reception, and a safety violation detection layer. Within the above scope, the testbed is positioned as a reusable foundation for module-level SDV research and as a basis for future extensions such as Joint Communication and Sensing (JCAS), energy-aware driving, and Hardware-in-the-Loop (HiL) integration.

The rolling resistance coefficient is a crucial parameter in vehicle dynamics. It directly affects the resistance to motion and, consequently, energy consumption and emissions in any vehicle, i.e. for combustion engine vehicles, hybrid or electric vehicles. It varies with factors such as vehicle speed, temperature, road surface type, tire pressure, and weather conditions. This variability significantly influences the vehicle performance, particularly in terms of fuel consumption and pollutant emissions, for engine vehicles. Besides energy consumption, in case of electric vehicles it also affects the recovery through regenerative braking. Therefore its estimation is an important area of research.In this work, based on a typical vehicle motion model, we compare three algorithms for estimating this coefficient: Recursive Least Squares (RLS), Super Twisting Algorithm (STA), and Extended Kalman Filter (EKF). To assess their effectiveness in a realistic scenario, we use experimental data from three vehicle tests under different maneuvers.

Due to changed requirements compared to conventional propulsion concepts, electromobility demands new and innovative strategies for energy-efficient vehicle motion control. For example, the challenge in purely rear-wheel drive (RWD) electric vehicles (EVs) is to achieve a maximum of regenerative braking power in order to increase energy recovery and to ensure, that this does not impair the braking stability. Within this conflict between energy efficiency and braking dynamics, it is necessary to design an intelligent strategy to optimise recuperation. This paper presents such a strategy, which improves an existing approach formerly presented by the authors, but specifically optimised to overcome weaknesses. The previous approach had two major limitations: First, the efficiency map of the in-wheel machines (IWMs) was not considered. Second, there was no possibility of switching flexibly between different brake force distributions to guarantee both, maximized recovery potential and high braking stability, in fulfilment of legislative requirements. The new strategy addresses these shortcomings by introducing a speed-dependent torque limit for the electric drive motors to avoid inefficient operating and uses two independent factors to manipulate the brake force distribution along the axles and vary the distribution between the actuators. In addition, various scenarios were analysed and incorporated into the new strategy in order to achieve optimal torque distribution in every driving situation. The developed approach was implemented into a real vehicle and extensively tested in driving trials on closed-off terrain and on public roads. The results of the investigation demonstrate the ability to ensure stable vehicle control and a 45.3 % increase in energy recovery in comparison to the established benchmark.

Traction control plays a key role in improving vehicle safety, especially for driving scenarios involving low levels of tire-road friction. Over the past 30 years, academic and industrial research in traction controllers has mainly favored deterministic approaches. This paper introduces a traction control strategy based on a deep reinforcement learning agent tailored for straight-line acceleration maneuvers from standstill in low-friction conditions. The proposed agent is trained on two different electric vehicles, a front-wheel drive city car (from EU vehicle segment A), and a rear-wheel drive sedan (from EU vehicle segment D). The paper presents a deep reinforcement learning agent formulation suitable for training on different vehicles, assesses the performance of the resulting controllers in comparison with a benchmarking integral sliding mode controller, and evaluates their response to changes in vehicle mass, powertrain parameters and tire-road friction conditions. The assessment uses a high-fidelity co-simulation model, combining AVL VSM and Simulink, developed as part of the Horizon Europe project EM-TECH. Results highlight the capability of the deep reinforcement learning agent to create traction controllers for the different vehicle configurations by only changing the weights of a single term of the reward function.

This study investigates the effects of active roll control on tyre wear in electric vehicles. The increased mass due to the installed batteries and the resulting higher vertical and lateral forces during dynamic manoeuvres contribute significantly to tyre wear in electric vehicles. In this study, advanced mathematical modelling is used to investigate these effects in a high-fidelity simulation environment. A series of open-loop and closed-loop tests were used to analyse the distribution of tyre forces and the resulting wear patterns. The results suggest that active roll control can significantly increase load-induced tyre wear, particularly on the outer wheel where the forces are most pronounced. This work provides insights into the challenges that engineers will have to solve when developing new platforms for electric vehicles.

Brake-by-wire systems have received more and more attention in the recent years, but a close look on the available systems shows, that they have not reached full by-wire level yet. Most systems are still using hydraulic connections between main cylinder and the brake calipers on at least one axle to ensure functional safety. Mostly, this is the front axle, since the front brakes have to convert more kinetic energy during braking manoeuvers. Electromechanical actuators are currently used for rear brakes in hybrid brake-by-wire applications solely, since a loss of the front brake calipers can lead to severe conditions and control loss of the vehicle during braking. Further, the higher mass of battery electric vehicles (BEVs) leads to much higher braking forces on both axles and to increased sizes of the electromechanical calipers. This article presents a concept for a brake-by-wire system for battery electric vehicles, which features electromechanical brake actuators on all corners and a redundant system architecture. Theoretically, the proposed system is capable to generate a braking intensity up to 70% for vehicles with a total mass of approximately 2.8 tons. Besides mechanical design of the actuator, the power electronics are taken into account too and their behavior is investigated through dedicated simulation.

Modern vehicles are characterized by a high degree of environmental perception and automation. This is made possible by the development of new motion control and driver assistance systems as well as new methods for sensor-based detection of driving parameters. However, the functionality of commonly used visual and wave-based sensor systems, such as cameras, radars, etc., can be limited when installed in a vehicle as some obstacles fall into the blind zone due to the road profile or other road users. It is also difficult to detect road-specific parameters such as gradient, slope, or surface conditions with onboard vehicle sensors. A method to improve the vehicle’s detection capabilities can be proposed by extending the information channels with additional sensors by mounting them on an assistant drone. This article presents a framework for drone-to-vehicle collaboration that enables the integration of data from drone sensors into the vehicle motion control. The article gives an overview of the system, the technology created for the implementation, and the challenges to be overcome by integrating the different components. The hardware implementation of the drone and the integration of the data into the vehicle control unit (VCU) are presented. The proposed approach allows the vehicle to view the road from different angles and detect changes in road conditions from a distance. It extends the capabilities of the vehicle motion control. The effectiveness of the developed framework is experimentally validated with an electric vehicle’s velocity control and traction control on low-friction surfaces.

In the automotive industry, sensors represent an enabling technology for autonomous vehicles. Physical sensors are often too expensive or impractical. Therefore, virtual sensors constitute a feasible and economical alternative in which quantities of interest can be estimated using other available measurements coupled with analytical models of road vehicles. A virtual sensor for measuring the vertical displacement of the unsprung masses of a road vehicle, functional for the control of vehicle suspension systems, is presented in this work. The virtual sensor consists of a 7-degree-of-freedom vehicle model, in which the measured vehicle longitudinal and lateral accelerations are employed as inputs. The developed sensor has been tested by comparing experimental data obtained by a Range Rover vehicle with the estimated data. The obtained results show the suitability of the developed sensor for measuring the vertical displacement of unsprung masses, as confirmed by values related to the adopted performance indexes composed of the average RMSE and the NRMSE.

The Horizon Europe projects EM-TECH and HighScape propose innovative solutions for electric traction machines and their WBG-based drives and components, to achieve higher energy efficiency, reduced volume and mass, as well as reduced cost. This paper outlines the main innovations of EM-TECH and HighScape, targeting a wide range of vehicle applications, including passenger cars and commercial vehicles. Specifically, EM-TECH deals with: i) modular designs of on-board axial flux machines (AFMs) for reducing the implementation costs of scalable centralised powertrains for electric axle (e-Axle) solutions; ii) in-wheel motors (IWMs) integrated with electric gearing, for expanding the high efficiency region of electric corner (e-Corner) powertrains; and iii) the use of permanent magnets deriving from recycling processes to improve sustainability. In parallel, HighScape targets the physical and functional integration of the power electronics of WBG-based traction inverters, onboard chargers, DC/DC converters, and electric drives for auxiliaries and actuators.

This paper presents a case study on adaptive one pedal driving for a battery electric sport utility vehicle with in wheel based rear-wheel drive that is able to adjust the drive pedal curve automatically. In addition, a fuzzy logic observes the state of the electric machines to adapt the motor torque to the most efficient region. The control method was tested in a co-simulation using a validated vehicle model and a driver-in-the-loop strategy. Within the experiments, an improvement in energy recovery of up to 4.8 % was achieved compared to the classic brake blending. In addition, the total number of brake pedal applications is ten times lower, which translates into less driving stress and greater driving comfort, especially on long distance trips, proving the effectiveness of the controller.

This paper explores reinforcement learning for automated control derivation within design space exploration with focus on a functional safety concept for safety-critical automotive applications. A multi-task reinforcement learning framework is proposed to handle optimal control for various system topologies, component dimensioning, failures and scenarios. The timing analysis reveals that increasing the number of design variants significantly reduces per-topology training time, demonstrating the scalability of the proposed multi-task reinforcement learning approach for exploring large design spaces. This enables the derivation of optimal control across the entire design space, including both normal and failure conditions, while accounting for non linear plant dynamics with non-ideal actuator dynamics. The proposed methodology reduces manual engineering effort, supports derivation of fault tolerant control and offers a practical path toward automation in large-scale design space explorations.

Electric Vehicles (EVs) are still facing prejudices about limited range, making them unattractive for many customers. However, their locally emission-free operation and the ability to recover kinetic energy during braking manoeuvres are significant advances against conventional drivetrains. Especially the function of one-pedal driving (OPD) can further reduce the energy consumption of EVs if properly realized and tuned. In this research, the optimisation and evaluation of an adaptive OPD strategy for a battery electric vehicle (BEV) with the aim of improving energy efficiency and driving comfort, which was previously introduced by the authors, is presented. Therefore, an adaptive pedal curve was designed first and extended through the integration of a fuzzy controller that considers the trade-off between efficient operation and driver intention based on vehicle speed and the drive pedal position signals. The strategy was extended by the incorporation of another input to represent the traffic area. The efficiency was evaluated in a proband study using virtual driving tests in a static simulator, in which different configurations were analysed and rated. It was found that the optimised strategy achieved the best overall result. Compared to pure regenerative braking as the benchmark, energy consumption as well as the amount of pedal changes were reduced by 8.45% as well as 62.27%, respectively, and the rate of energy recovery was increased by 67.8%.