Vehicle Propulsion Research Articles

Diagnosis of Power Switch Faults in Three-Phase Permanent Magnet Synchronous Motors via Current-Signature Technique

The paper deals with the development of a model-based current-signature algorithm for the detection and isolation of power switch faults in three-phase Permanent Magnet Synchronous Motors (PMSMs). The algorithm, by elaborating the motor currents feedbacks, reconstructs the current phasor trajectories in the Clarke plane through elliptical fittings, up to detecting and isolating the fault depending on the characteristics of the signature deviation from the nominal one. As a rough approximation, as typically proposed in the literature, the fault of one out of six power switches implies that, at constant speed operation, the phasor trajectory deviates from the nominal circular path up to a semi-circular “D-shape” signature, the inclination of which depends on the failed converter leg. However, this evolution can significantly deviate in practical cases, due to the dynamics related to the transition of motor phase connections from failed to active switches. The study demonstrates that an online ellipse fitting of the current signature can be effective for diagnosis, through correlating the ellipse centre to the location of the failed switch. The performances of the proposed monitoring technique are here assessed via the nonlinear simulation of a PMSM employed for the propulsion of a lightweight fixed-wing Unmanned Aerial Vehicle (UAV), by quantifying the fault latencies and the related transients.

Theoretical model for high-altitude gas exchange process in multi-fuel poppet valves two-stroke aircraft engine

Under the demand of global aviation carbon reduction, the multi-fuel poppet valves two-stroke (MF-PV2S) aircraft engine exhibits advantages such as lower lubricant consumption and the flexibility to high-altitude valve timing adjustment, which position it to play a more significant role in general aviation aircraft and unmanned aerial vehicle propulsion systems. High-altitude gas exchange performance is a critical factor in the thermal efficiency and power characteristics of two-stroke aircraft engines. However, the applicability and accuracy of existing models in predicting the gas exchange process for PV2S engines remain insufficiently discussed. Recognizing discrepancies in the predictions of PV2S gas exchange processes by existing models, a theoretical model applicable to high-altitude gas exchange in an MF-PV2S aircraft engine is established. The modified model adjusts the exhaust composition during the second and third phases of the scavenging process, and a novel model characteristic coefficient, designated as b3, is incorporated to signify the proportion of mixed gas in the exhaust gases at a given moment. Moreover, the model characteristic coefficients b1 and b2, are determined with nonlinearity, based on appropriate relationships that account for the characteristics of the PV2S gas exchange process. The model calculates gas exchange characteristics under different operating conditions and fuels, comparing these results with simulations and tracer gas-based experiments. The findings demonstrate that the model successfully captures the characteristics of early appearance and prolonged duration of fresh charge loss during PV2S gas exchange, significantly enhancing the predictive accuracy. Through adjustments of model coefficients based on specific conditions, the model predicts and describes the variation process of PV2S gas exchange performance parameters under different conditions, including various speeds, different intake-exhaust pressure differences at different altitudes, valve overlaps, and fuel types. The maximum relative errors between model predictions and experimental trapping efficiency, delivery ratio and charging efficiency are 2.58 %, 4.33 %, and 3.89 % respectively. The model can be used for rapid and accurate prediction of gas exchange characteristics under different conditions for the MF-PV2S aircraft engine. Also, it serves as an alternative model of three-dimensional simulation models that can be coupled into the one-dimensional thermodynamic cycle simulation.

Real-time Adaptive Control of Electric Vehicle Drives using Artificial Neural Networks

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.

Artificial Neural Networks-Based Torque Distribution for Riding Comfort Improvement of Hybrid Electric Vehicles

In an age characterized by a focus on environmental sustainability and technological advancement, the creation and integration of hybrid electric vehicles (HEVs) have become a significant solution in the realm of transportation and clean energy. This study introduces a method for optimizing the distribution of torque in HEVs through the utilization of artificial neural networks (ANN). Furthermore, it introduces an innovative design for the vehicle’s drivetrain, enabling it to function in both rear-wheel and four-wheel drive configurations. The HEV is propelled by a permanent magnet synchronous machine (PMSM) and is controlled using direct torque control (DTC) due to its capability to provide rapid and precise responses. The results of simulations conducted using MATLAB/Simulink confirm the effectiveness of the proposed intelligent torque distribution strategy, demonstrating its capacity to enhance vehicle performance, driving comfort, and propulsion power.

Adaptive control of electric vehicle drives through neural network ensembles

This study examines the use of neural network ensembles in adaptive control for electric vehicle (EV) propulsion systems, using simulated data to evaluate their efficacy. The research aims to evaluate the collective performance of a group, analyze the characteristics of electric vehicle drives, examine the feedback from adaptive control systems, and analyze the data used to train neural networks in order to get a thorough understanding of the subject. The results demonstrate the resilience of neural network ensembles in predictive modeling, with mean squared error values ranging from 0.0028 to 0.0042 and R-squared scores between 0.979 and 0.992. An examination of electric vehicle (EV) driving characteristics reveals differences in battery capacity (ranging from 60 to 85 kWh) and motor efficiency (ranging from 85% to 95%). Notably, there are correlations that demonstrate the influence of weight on the needs for battery capacity. An analysis of the feedback parameters in adaptive control reveals speed inaccuracies ranging from -1.8 to -3.2 km/h, battery voltage errors between 1.5 and 2.8 V, temperature mistakes ranging from 1.2 to 2.5°C, and variations in the control signal. This highlights the significant impact these factors have on the adjustments made by the control system. Moreover, examination of the training data for neural networks emphasizes the significance of having a wide range of inputs (0.3-0.9) and the intricate connections between inputs and outputs (0.6-0.95). In summary, these findings highlight the ability of neural network ensembles to improve predictive accuracy, comprehend the dynamics of EV systems, and emphasize the importance of accurate feedback and high-quality training data for effective adaptive control strategies in electric vehicles. These insights are valuable for advancing EV technology and control methodologies.

Optimization of Permanent Magnet Synchronous Machines for Electric Vehicle Propulsion with Multiple Objectives

Optimization of Permanent Magnet Synchronous Machines for Electric Vehicle Propulsion with Multiple Objectives

Investigation of a Double-Stator Consequent-Pole Dual-PM Machine for Electric Vehicle Propulsion

Investigation of a Double-Stator Consequent-Pole Dual-PM Machine for Electric Vehicle Propulsion

Model-Based Sensor Fault Detection and Diagnosis in Closed-Loop Power Converters for Electric Vehicles

Electric Vehicle (EV) involves electric motors equipped with closed loop powered conversion systems which use the energy from storage batteries for vehicle propulsion and rely on sensor measurements as feedback to the controller. Therefore, sensor fault detection and diagnosis are an essential process that ensures performance and safety of EVs. From the National Renewable Energy Laboratory (NREL) study it is confirmed that electrical vehicle’s fault percentages vary, however, sensor faults constitute approximately 20-30% of total faults in them. Thus, sensor fault detection is critical for effective EV operation and requires the utmost focus from EV manufacturers and operators. Model-based techniques and Kalman Filter (KF) make it possible to detect faults on sensors in closed-loop EV power converter. It involves the use of mathematical model, sensor data and KF to identify fault before it becomes a threat and guarantees proper function of the vehicle. In this article, a state-space model of a CUK converter is developed, and an LQR closed-loop control system is designed for the power converter. A sensor fault detection method was developed for the system which is then simulated and tested under sensor bias fault conditions for both open and closed-loops control for determining the difference between them. The results showed a distinctive feature extracted between these two systems in the presence of system disturbances and noise applied to the sensor readings. The second section of simulation results in a closed-loop configuration validated the mathematical equations used for sensor fault detection, demonstrating their resistance to false alarms caused by system or component faults in addition to its ability to diagnose faults.

Comparative analysis of electric motor drives employed for propulsion purpose of Battery Electric Vehicle (BEV) systems

This paper presents an analysis of electric motor drives for the propulsion system of a battery electric vehicle (BEV). It offers a comprehensive review and mathematical analysis of both AC and DC motor drives commonly used in electric vehicle (EV) applications. Various types of electric motor drives have been utilized for EV propulsion, and among them, the Permanent Magnet Synchronous Motor (PMSM) drive stands out as the optimal choice. The PMSM drive demonstrates superior performance and numerous advantages, including a robust structure, high efficiency, compact size, reduced maintenance costs, and minimal torque ripple. These characteristics make it a more suitable option for EV propulsion compared to other motors. This study investigates the performance of the PMSM drive in comparison to other competitive electric motor drives used in EV propulsion systems, namely the Brushless DC Motor (BLDCM), the Induction Motor (IM), and the Switched Reluctance Motor (SRM). The evaluation focuses on key criteria for electric motors—output power and torque densities, essential for effective application in EV propulsion systems. The paper introduces novel mathematical and analytical relationships between two prominent PM motor families: the PMSM and the BLDCM. Both motors are highly competitive in terms of power and torque output. The mathematical analysis and graphical plot simulation results demonstrate that the PMSM drive offers the highest power and torque densities among the three motor drives. Specifically, the PMSM drive exhibits 29.90% greater power and torque densities than the BLDCM drive, 88.68% greater than the SRM drive, and an impressive 200% greater than the IM drive, all under the same operating parameters such as power factor, size, rating, and efficiency. These findings highlight the significant advantages of the PMSM drive, positioning it as a superior choice for electric vehicle propulsion systems.

Assessing driving behavior influence on fuel efficiency using machine-learning and drive-cycle simulations

Consumption of fossil fuel-based energy for vehicle propulsion and associated emissions are a global concern.One pathway to energy reduction is to examine situations where high-energy consumption occurs on roadways, e.g., speed volatility at work zones, or on sharp curves, which has been understudied. Harnessing second-by-second data from the naturalistic driving study and using the concept of driving volatility, this paper explores driving styles in work-zones and curves using machine learning approaches (k-medoids, hierarchical clustering) and drive-cycle simulations from Autonomie®. Results show that aggressive driving account for 12.2 % and 15.4 % of events that occurred in work zones and on curves and leads to 23 % increase in fuel consumption as opposed to normal driving.These results have implications for transportation agencies to improve work-zone configurations and provide signage or technology on curves to reduce fuel consumption and emissions. Moreover, automated and connected vehicles can smooth out traffic flow with advanced advisories and warnings.

Hydrogen Fuel Cell Aiming at Vehicle Propulsion: Onboard or Off-Board Electricity Generation?

In response to the global energy crisis and climate change, hydrogen has been pointed out as promising fuel to be part of the green energy transition. Thus, countries worldwide have released policies to support and increase the hydrogen industry. Therefore, investigations regarding how hydrogen can be utilized best as a fuel should be addressed to evaluate its potential applications. Since transportation is responsible for 37% of the carbon emissions from the end-use sectors, it is of interest to consider hydrogen fuel for vehicle propulsion. There are two main alternatives that might be considered for the electricity generation from hydrogen: 1) a hydrogen fuel cell onboard the electric vehicle or 2) a hydrogen fuel cell off-board powering the electric vehicle.Therefore, in this work, we propose the modeling and simulation from hydrogen fuel to wheels of the two different systems using the WLTP as the driving cycle reference. For the onboard system, the following components were considered: the vehicle dynamics, the gear, the motor, the inverter, the battery, and the fuel cell system on board (fuel cell vehicle powertrain). For the off-board system, the following components were considered: the vehicle dynamics, the gear, the motor, and the inverter for the battery electric vehicle powertrain, followed by the charger, the grid, and the fuel cell system off-board. The components were modeled as further described.The vehicle dynamics were modeled mathematically and the weight difference between the vehicles was taken into account. The gear was a single-step gear. The motor was an eight-pole PMSM and the inverter three-phase IGBT. The battery for both was modeled as a lithium-ion battery with different sizes, with the battery for the fuel cell vehicle being much smaller. Capacity values similar to commercialized vehicles were used (4 and 300 Ah). The fuel cell was modeled as a commercialized fuel cell with 63% peak efficiency. For the fuel cell vehicle, a control strategy was implemented to split the power demand between the fuel cell and the battery. The control strategy was based on the state of charge of the battery and the power demand for each point of the cycle.The simulation results have shown a slightly higher efficiency and lower hydrogen consumption for the onboard system compared to the off-board system. Even though there were more losses associated with the battery and the fuel cell in the onboard system, the losses associated with vehicle weight and the off-board power electronics related to the off-board system were more significant to the overall energy consumption. Thus, the results suggest that if hydrogen is available as a fuel aimed at vehicle propulsion using it onboard is more efficient than using it off-board.

Understanding Charge, Mass, and Heat Transfer in Fuel Cells for Transport Applications – Insights from the Camelot Project

Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising solution for zero-emission vehicle propulsion owing to their high energy density, low operating temperature, and high efficiency. The overall aim of the CAMELOT project is to improve the power density of fuel cells by understanding the limitations on the performance of state-of-the-art (SoA) and beyond SoA PEMFC membrane electrode assemblies (MEAs). To achieve this, the CAMELOT project has diagnosed the fundamental transport properties that limit performance of these MEAs and materials. Not only has the CAMELOT project produced MEAs with features that have the potential to enable disruptive performance increases but have further developed leading open-source models to enable accurate simulations validated through experimental work. In-depth parametrisation of fuel cell components through extensive ex-situ and in-situ characterisation has been performed to provide input to the modelling activities. The two major modelling advances achieved by the CAMELOT project are i) the improvement of water uptake and transport models, especially concerning ultra-thin PEMs, and ii) extending a leading open-source model (FAST-FC) to enable accurate simulation of beyond SoA fuel cell performance and voltage loss breakdown. CAMELOT has developed a series of beyond-SoA fuel cell components to ensure that the modelling inputs are representative of SoA component properties. A series of membranes with varying thicknesses were prepared and a range of ex-situ experiments were carried out to gather water uptake and permeability properties for the water transport models. Additional in-situ electrochemical characterisation was carried out on a series of catalyst coated membranes (CCMs) to extract important properties of the catalyst and membrane components as inputs to improve the physics-based fuel cell performance model.Significant results from the project to be highlighted in this presentation include the development of a water transport model with improved description of water uptake and transport in thin ionomeric layers, the development of a predictive modelling framework from which we can identify the most important material parameters to tune to reach our MEA performance targets of 2.7 A/cm2 at >0.67 V with total catalyst loadings <0.08 mgPt/cm2, the development of ultrathin (< 10 µm) proton exchange membranes and ultra-low loading cathode catalyst loadings (0.08 mgPt/cm2), the development of catalyst coated membranes (CCMs) with in-plane catalyst and ionomer gradients, and extensive in-situ fuel cell characterization of the SoA CAMELOT CCMs/MEAs.The CAMELOT project has received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under grant agreement No 875155. This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation program, Hydrogen Europe and Hydrogen Europe Research.

Stirling engines: Advancements, applications, and environmental benefits

The increasing adoption of Stirling engines in contemporary industries is driven by their potential to address critical issues of energy efficiency and emissions reduction. Stirling engines offer higher energy efficiency, lower noise and emissions, and versatility in utilizing various heat sources, making them a promising solution for enhancing environmental preservation and energy diversity. This paper examines the growing adoption of Stirling engines in diverse industries, driven by the urgent need to address energy efficiency and emissions challenges. The paper outlines the core principles and advantages of Stirling engines, highlighting their potential to enhance energy efficiency, preserve the environment, and diversify energy sources. Furthermore, the paper underscores significant industrial and aviation applications of Stirling engines, particularly in vehicle propulsion, heat pumps, and electricity generation. Notably, the automotive Stirling engine excels in fuel efficiency and emissions reduction, offering a viable alternative to traditional internal combustion engines. Stirling engines also prove effective in heat pump systems, contributing to efficient building heating and cooling. In conclusion, despite challenges such as material requirements and heat loss, Stirling engines hold great promise as sustainable and eco-friendly thermal engines. As global concerns regarding energy and the environment intensify, Stirling engines offer a compelling pathway to improved energy efficiency, reduced emissions, and a more diversified energy landscape, driving innovation across sectors.

Algorithm based Calibration Strategies in an Electric Powertrain

The latest trend in the automobile industry is electric energy usage for vehicle propulsion. The electrical energy conversion to mechanical energy via motor is the currently accepted and safe technology. The major challenge for this is the component protection strategies. Components such as the motor, battery etc. need to be protected to enhance the life cycle of the vehicle. Also, vehicle safety is of much importance to avoid any fatalities arising with regards to the mishandling of the components. This study suffices the component protection by developing algorithmbased strategies. The algorithms are pre-fed to the controller. Development of smart motor and battery is proposed via Battery Thermal management system. The energy used is of both AC as well DC form. Energy sources for the electric energy generation are different namely, Batteries, Fuel cell etc. Energy generated through chemical reaction is stored in the battery and is eventually utilized.

Application of Accelerated H-Matrix Boundary Element Methods to Predict Exterior eVTOL Acoustics

The development of Advanced Air Mobility vehicles such as eVTOLs presents unique challenges - both community noise and vehicle interior noise must meet targets for level and character of sound for a vehicle to be commercially viable. Community noise entirely depends on, and interior noise strongly depends on the exterior noise sources generated by the vehicle propulsion. To predict community and interior noise with models which can be used to explore improvements to meet noise targets, the exterior noise field from the vehicle propulsion must be captured with sufficient accuracy. Boundary Element Methods (BEM) have good capabilities for predicting vehicle exterior acoustic propagation, but standard BEM is challenged to predict necessary high frequencies due to small mesh size and computational limitations. Accelerated or reduced methods such as H-Matrix BEM approach can be used to increase the prediction upper frequency limit, improve calculation time, or reduce memory consumption with minimal loss of accuracy. This allows community noise to be predicted and for generation of exterior field loads on the vehicle structure to predict interior noise and design treatments. The processes and improvements offered by H-Matrix BEM in conjunction with and as an alternative to other acoustic simulation methods are discussed and presented.

Electrification of Motorway Network: A Methodological Approach to Define Location of Charging Infrastructure for EV

Environmental issues have reached global attention from both political and social perspectives. Many countries and companies around the world are adopting measures to help change current trends. Awareness of decarbonization in the transportation sector has led to an increasing development of energy storage systems in recent years, especially for ground vehicles. Batteries, due to their high efficiency, are one of the most attractive energy storage systems for vehicle propulsion. As for road vehicles, the growing interest in Electric Vehicles (EVs) is motivated by the fact that they reduce local emissions compared to traditional Internal Combustion Engine (ICE) vehicles. The purpose of the paper is to present a study on how to plan and implement vehicle charging infrastructure on motorways. In particular, a specific road in Italy is analyzed: the motorway A1 from Milan to Naples with a length of about 800 km. This motorway can be considered representative because it passes through some of Italy’s most important cities and regions and may represent the backbone of Italy. A useful model for defining the optimal location of electric vehicle charging stations is presented within the paper. Starting with the data on the average daily traffic flows passing through the main nodes of the motorways section, the demand for the potential vehicles needed to define the number and dimension of charging stations and provide an adequate supply is estimated. The analysis was performed considering five-time horizons (year 2022 to year 2025) and four Scenarios involving the installation of 4, 8, 16, and 32 Charging Stations (CSs) in each service area, respectively.

Investigation of self-adjusting cooling system for the autonomous underwater vehicle propulsion motor

Permanent magnet (PM) motors are widely employed in the autonomous underwater vehicle (AUV) propulsion because of their high efficiency and torque density. Since the AUV housings are sealed and narrow, motors are vulnerable to the risk of overheating, leading to dissolution of the winding insulation and demagnetization of the permanent magnets. In this study, a self-adjusting cooling scheme was proposed, which uses the hydrodynamic pressure to pump seawater into the cooling channel automatically. Firstly, the mathematical model of the flow field and heat transfer was established using equation derivations. Subsequently, to clarify the characteristics of the self-adjusting cooling system, the effect of the AUV speed on the thermal and fluid characteristics was investigated using computational fluid dynamics. Results show that the flow rate in the channel increases from 2 to 8.1 L/min as the AUV speed increases from 15 to 50 kn, resulting in the improvement of the heat transfer coefficient from 41 to 764 W/(m2∙K). Subsequently, the influences of inlet and outlet on the thermal characteristics were studied and found to be beneficial for improving the cooling performance. To investigate the spiral flow channels, the distribution coefficient kC and shape factor Rf were defined. Compared to the closed-cycle cooling system for the AUV propulsion motor, the proposed cooling system eliminated auxiliary devices such as pumps and heat exchangers, which makes it more compact and efficient for AUVs. However, introducing seawater into the AUV housing poses a risk of liquid leakage.

Towards More Efficient Electric Propulsion UAV Systems Using Boundary Layer Ingestion

The implementation of distributed propulsion and boundary layer ingestion for unmanned aerial vehicles represents various challenges for the design of embedded ducts in blended wing body configurations. This work explores the conceptual design and evaluation of DP configurations with BLI. The aerodynamic integration of each configuration is evaluated following a proposed framework, including simulation analysis. Power saving coefficient and propulsive efficiency were compared against a baseline podded case. The results show the optimal propulsion configuration for the BWB UAV obtaining 3.95% of power benefit and propulsive efficiency (ηp&gt;80%). Indeed, the aerodynamic integration effects for the proposed design maintain the BWB’s aerodynamic efficiency, which will contribute to longer endurance and better performance.

Using inerter-integrated absorbers to improve the performance of an in-wheel motor system

The in-wheel motor (IWM) is a promising advancement in electric vehicle propulsion since it offers greater packaging efficiency and torque vectoring capabilities than a chassis-mounted motor. However, the increased unsprung mass degrades the vehicle dynamics by increasing body vertical acceleration (BVA) and tyre dynamic load (TDL), worsening ride comfort and road-holding respectively. Another important consideration is magnetic gap deformation (MGD), which results from the deflection between the motor stator and rotor. Reducing MGD improves the motor reliability and longevity. This paper addresses these issues by incorporating inerters into an existing IWM model. They are incorporated into the strut and bushes to control BVA, TDL and MGD. The system optimised for MGD resulted in a deflection reduction of over 98% without compromising performance of the other indices. Optimising the strut and bushes for BVA and TDL reduced them by 20% and 9% respectively without degrading MGD. A sensitivity analysis of the results is presented. Here the damping and inertance values are varied by 20% from the optimum, and the resulting performance still improves upon the default. It is concluded that there are significant potential improvements where inerters are included in an in-wheel motor system.

Hybridisation Concept of Light Vehicles Utilising an Electrified Planetary Gear Set

Climate change and air pollution are two significant challenges facing our society and represent a major driver for new developments in the transport sector. As a consequence, automotive manufacturers have focused on the electrification of vehicle propulsion systems and offer a wide range of hybrid and full-electric vehicles in different classes. However, in the world’s most densely populated metropolitan areas, small and lightweight vehicles are key for the mobility of millions. Traditionally these vehicles have provided cost-effective transportation which is difficult to preserve with vehicle electrification. Many of these light vehicles, such as scooters and all-terrain vehicles, use internal combustion engines in combination with a continuously variable rubber belt transmission which provides a simple, comfortable and cost-effective transmission technology but with poor efficiency and high maintenance costs. In this contribution, a novel full hybrid powertrain concept is proposed that offers a similar driving experience to conventional continuously variable transmissions while providing significantly improved performance and fuel economy combined with low system complexity. In its basic configuration, the hybrid powertrain can operate without active actuators and even with mechanical throttle control of the internal combustion engine. This minimalist approach reduces system costs and helps to create a competitive solution for price-sensitive markets. The hybrid system is based on a planetary gear set that combines the internal combustion engine and an electric motor. It is complemented by a centrifugal clutch and one-way clutch, resulting in different operating modes for low and high speeds as well as for electric driving. This paper describes the mechanical design and control approach of the proposed hybrid powertrain layout. In order to evaluate the basic functionalities, a prototype vehicle was built and tested. This contribution shows the integration of the hybrid powertrain concept in a prototype vehicle and proves the fulfilment of all required full hybrid functionalities.