Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Next Generation HEV Powertrain Design Tools: Roadmap and Challenges

Hybrid electric vehicles (HEVs) represent a fundamental step in the global evolution towards transportation electrification. Nevertheless, they exhibit a remarkably complex design environment with respect to both traditional internal combustion engine vehicles and battery electric vehicles. Innovative and advanced design tools are therefore crucially required to effectively handle the increased complexity of HEV development processes. This paper aims at providing a comprehensive overview of past and current advancements in HEV powertrain design methodologies. Subsequently, major simplifications and limits of current HEV design methodologies are detailed. The final part of this paper defines research challenges that need accomplishment to develop the next generation HEV architecture design tools. These particularly include the application of multi-fidelity modeling approaches, the embedded design of powertrain architecture and on-board control logic and the endorsement of multi-disciplinary optimization procedures. Resolving these issues may indeed remarkably foster the widespread adoption of HEVs in the global vehicle market

Cyber-physical system based optimization framework for intelligent powertrain control

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented. Simulation-based parameter optimizations are carried out according to the objective functions. Simulation results show that an electric powertrain with intelligent controller can perform its tasks well under sport, eco, and normal driving modes. The vehicle further improves overall performance in vehicle dynamics, ride comfort, and energy efficiency. The results validate the feasibility and effectiveness of the proposed CPS-based optimization framework, and demonstrate its advantages over a baseline benchmark

Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies

This Special Issue was intended to contribute to the sustainable mobility agenda through enhanced scientific and multi-disciplinary knowledge to investigate concerns and real possibilities in the achievement of a greener mobility and to support the debate between industry and academic researchers, providing an interesting overview on new needs and investigation topics required for future developments

A Study on the Integration of a High-Speed Flywheel as an Energy Storage Device in Hybrid Vehicles

The last couple of decades have seen the rise of the hybrid electric vehicle as a compromise between the outstanding specific energy of petrol fuels and its low-cost technology, and the zero tail-gate emissions of the electric vehicle. Despite this, considerable reductions in cost and further increases in fuel economy are needed for their widespread adoption. An alternative low-cost energy storage technology for vehicles is the high-speed flywheel. The flywheel has important limitations that exclude it from being used as a primary energy source for vehicles, but its power characteristics and low-cost materials make it a powerful complement to a vehicle's primary propulsion system. This thesis presents an analysis on the integration of a high-speed flywheel for use as a secondary energy storage device in hybrid vehicles. Unlike other energy storage technologies, the energy content of the flywheel has a direct impact on the velocity of transmission. This presents an important challenge, as it means that the flywheel must be able to rotate at a speed independent of the vehicle's velocity and therefore it must be coupled via a variable speed transmission. This thesis presents some practical ways in which to accomplish this in conventional road vehicles, namely with the use of a variator, a planetary gear set or with the use of a power-split continuously variable transmission. Fundamental analyses on the kinematic behaviour of these transmissions particularly as they pertain to flywheel powertrains are presented. Computer simulations were carried out to compare the performance of various transmissions, and the models developed are presented as well. Finally the thesis also contains an investigation on the driving and road conditions that have the most beneficial effect on hybrid vehicle performance, with a particular emphasis on the effect that the road topography has on fuel economy and the significance of this

Combined design and control optimization of hybrid vehicles

Hybrid vehicles play an important role in reducing energy consumption and pollutant emissions of ground transportation. The increased mechatronic system complexity, however, results in a heavy challenge for efficient component sizing and power coordination among multiple power sources. This chapter presents a convex programming framework for the combined design and control optimization of hybrid vehicles. An instructive and straightforward case study of design and energy control optimization for a fuel cell/supercapacitor hybrid bus is delineated to demonstrate the effectiveness and the computational advantage of the convex programming methodology. Convex modeling of key components in the fuel cell/supercapactior hybrid powertrain is introduced, while a pseudo code in CVX is also provided to elucidate how to practically implement the convex optimization. The generalization, applicability, and validity of the convex optimization framework are also discussed for various powertrain configurations (i.e., series, parallel, and series-parallel), different energy storage systems (e.g., battery, supercapacitor, and dual buffer), and advanced vehicular design and controller synthesis accounting for the battery thermal and aging conditions. The proposed methodology is an efficient tool that is valuable for researchers and engineers in the area of hybrid vehicles to address realistic optimal control problems

Sim-heuristics low-carbon technologies’ selection framework for reducing costs and carbon emissions of heavy goods vehicles

UK logistics fleets face increasing competitive pressures due to volatile fuel prices and the small profit margins in the industry. By reducing fuel consumption, operational costs and carbon emissions can be reduced. While there are a number of technologies that can reduce fuel consumption, it is often difficult for logistics companies to identify which would be the most beneficial to adopt over the medium and long terms. With a myriad of possible technology combinations, optimising the vehicle specification for specific duty cycles requires a robust decision-making framework. This paper combines simulated truck and delivery routes with a metaheuristic evolutionary algorithm to select the optimal combination of low-carbon technologies that minimise the greenhouse gas emissions of long-haul heavy goods vehicles during their lifetime cost. The framework presented is applicable to other vehicles, including road haulage, waste collection fleets and buses by using tailored parameters in the heuristics model

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Study of the Potential of Electrified Powertrains with Dual-Fuel Combustion to Achieve the 2025 Emissions Targets in Heavy-Duty Applications

[ES] El transporte de personas, así como de carga ha evolucionado y crecido tremendamente en los últimos años. El desarrollo tecnológico debió ser adaptado a las diferentes medidas gubernamentales en términos de control de emisiones contaminantes. Desde el acuerdo de Paris en 2015 para mantener el crecimiento de la temperatura global por debajo de 1.5oC, se han impuesto también límites para las emisiones de CO2 por parte de vehículos de carretera. Para el sector del transporte pesado, se han impuesto límites de flota de 15% para 2025 y 30% para 2030 de reducción del CO2 con respecto a 2019. Por lo tanto, esta doble restricción de muy bajos niveles de emisiones contaminantes, así como de gases de efecto invernadero hacen que el sector del transporte este ante un gran desafío tecnológico. En 2022, el transporte de carga tiene un 99% de vehículos propulsados a motor de combustión interna con Diesel como combustible y sin ningún tipo de ayuda eléctrica en el sistema de propulsión. Los límites de emisiones contaminantes como Euro 6 son alcanzados con complejos sistemas de postratamiento que además agregan el consumo de Urea. Trabajos previos en la bibliografía, así como sistemas prototipo han demostrado que es posible alcanzar los objetivos de emisiones contaminantes con métodos avanzados de control de la combustión y así disminuyendo la complejidad del post tratamiento en la salida de gases. Con mayor éxito, el concepto de Reactivity Controlled Combustion Ignition puede alcanzar valores por debajo de Euro 6 con eficiencia similar a la combustión de Diesel. Sin embargo, no soluciona los problemas de emisiones de CO2. Por otro lado, en vehículos de pasajeros fue demostrado con suceso la aplicación de motores eléctricos en el sistema de propulsión para mejorar la eficiencia global del vehículo. El caso extremo son los vehículos puramente electicos donde se alcanza eficiencias por arriba del 70% contra 35% de los vehículos no electrificados. Sin embargo, limitaciones de autonomía, tiempo de carga y la no clara reducción global de la contaminación debido a las emisiones de la energía de la red eléctrica y la contaminación de las baterías de ion-litio hacen que este sistema de propulsión este bajo discusión. Para los vehículos con algún grado de electrificación, las emisiones de gases contaminantes siguen siendo un problema como para el caso no electrificado. Por lo tanto, esta tesis doctoral aborda el problema de emisiones contaminantes, así como de CO2 combinado modos avanzados de combustión con sistemas de propulsión electrificado. La aplicación de estas tecnologías se centra en el sector del transporte de carretera pesado. En particular, un camión de 18 toneladas de carga máxima que originalmente en 2022 equipa un motor seis cilindros de 8 litros con combustión convencional Diesel. El presente trabajo utiliza herramientas experimentales como son medidas en banco motor, así como en carretera para alimentar y validar modelos numéricos de motor, sistema de postratamiento, así como de vehículo. Este último es el punto central del trabajo ya que permite abordar sistemas como el mild hybrid, full hybrid y plug-in hybrid. Calibración de motor experimental dedicada a sistemas de propulsión hibrido es presentada con combustibles sintéticos y/o para llegar a los límites de Euro 7.[CA] El transport de persones, així com de càrrega ha evolucionat i crescut tremendament en els últims anys. El desenvolupament tecnològic degué ser adaptat a les diferents mesures governamentals en termes de control d'emissions contaminants. Des de l'acord de Paris en 2015 per a mantindre el creixement de la temperatura global per davall de 1.5oC, s'han imposat també límits per a les emissions de CO¿ per part de vehicles de carretera. Per al sector del transport pesat, s'han imposat limites de flota de 15% per a 2025 i 30% per a 2030 de reducció del CO¿ respecte a 2019. Per tant, aquesta doble restricció de molt baixos nivells d'emissions contaminants, així com de gasos d'efecte d'hivernacle fan que el sector del transport aquest davant un gran desafiament tecnològic. En 2022, el transport de càrrega té un 99% de vehicles propulsats a motor de combustió interna amb Dièsel com a combustible i sense cap mena d'ajuda elèctrica en el sistema de propulsió. Els limites d'emissions contaminants com a Euro 6 són aconseguits amb complexos sistemes de posttractament que a més agreguen el consum d'Urea. Treballs previs en la bibliografia, així com sistemes prototip han demostrat que és possible aconseguir els objectius d'emissions contaminants amb mètodes avançats de control de la combustió i així disminuint la complexitat del post tractament en l'eixida de gasos. Amb major èxit, el concepte de Reactivity Controlled Combustion Ignition pot aconseguir valors per davall d'Euro 6 amb eficiència similar a la combustió de Dièsel. No obstant això, no soluciona els problemes d'emissions de CO¿. D'altra banda, en vehicles de passatgers va ser demostrat amb succés l'aplicació de motors elèctrics en el sistema de propulsió per a millorar l'eficiència global del vehicle. El cas extrem són els vehicles purament electicos on s'aconsegueix eficiències per dalt del 70% contra 35% dels vehicles no electrificats. No obstant això, limitacions d'autonomia, temps de càrrega i la no clara reducció global de la contaminació a causa de les emissions de l'energia de la xarxa elèctrica i la contaminació de les bateries d'ió-liti fan que aquest sistema de propulsió aquest baix discussió. Per als vehicles amb algun grau d'electrificació, les emissions de gasos contaminants continuen sent un problema com per al cas no electrificat. Per tant, aquesta tesi doctoral aborda el problema d'emissions contaminants, així com de CO¿ combinat maneres avançades de combustió amb sistemes de propulsió electrificat. L'aplicació d'aquestes tecnologies se centra en el sector del transport de carretera pesat. En particular, un camió de 18 tones de càrrega màxima que originalment en 2022 equipa un motor sis cilindres de 8 litres amb combustió convencional Dièsel. El present treball utilitza eines experimentals com són mesures en banc motor, així com en carretera per a alimentar i validar models numèrics de motor, sistema de posttractament, així com de vehicle. Est ultime és el punt central del treball ja que permet abordar sistemes com el mild hybrid, full *hybrid i plug-in hybrid. Calibratge de motor experimental dedicada a sistemes de propulsió hibride és presentada amb combustibles sintètics i/o per a arribar als límits d'Euro 7.[EN] The transport of people, as well as cargo, has evolved and grown tremendously over the recent years. Technological development had to be adapted to the different government measures for controlling polluting emissions. Since the Paris agreement in 2015 limits have also been imposed on the CO2 emissions from road vehicles to keep global temperature growth below 1.5oC. For the heavy transport sector, fleet limits of 15% for 2025 and 30% for 2030 CO2 reduction have been introduced with respect to the limits of 2019. Therefore, the current restriction of very low levels of polluting emissions, as well as greenhouse gases, makes the transport sector face a great technological challenge. In 2021, 99% of freight transport was powered by an internal combustion engine with Diesel as fuel and without any type of electrical assistance in the propulsion system. Moreover, polluting emission limits such as the Euro 6 are achieved with complex post-treatment systems that also add to the consumption of Urea. Previous research and prototype systems have shown that it is possible to achieve polluting emission targets with advanced combustion control methods, thus reducing the complexity of post-treatment in the exhaust gas. With greater success, the concept of Reactivity Controlled Combustion Ignition can reach values below the Euro 6 with similar efficiency to Diesel combustion. Unfortunately, it does not solve the CO2 emission problems. On the other hand, in passenger vehicles, the application of electric motors in the propulsion system has been shown to successfully improve the overall efficiency of the vehicle. The extreme case is the purely electric vehicles, where efficiencies above 70% are achieved against 35% of the non-electrified vehicles. However, limitations of vehicle range, charging time, payload reduction and an unclear overall reduction in greenhouse emissions bring this propulsion system under discussion. For vehicles with some degree of electrification, polluting gas emissions continue to be a problem as for the non-electrified case. Therefore, this doctoral Thesis addresses the problem of polluting emissions and CO2 combined with advanced modes of combustion with electrified propulsion systems. The application of these technologies focuses on the heavy road transport sector. In particular, an 18-ton maximum load truck that originally was equipped with an 8-liter six-cylinder engine with conventional Diesel combustion. The present work uses experimental tools such as measurements on the engine bench as well as on the road to feed and validate numerical models of the engine, after-treatment system, and the vehicle. The latter is the central point of the work since it allows addressing systems such as mild hybrid, full hybrid, and plug-in hybrid. Experimental engine calibration dedicated to hybrid propulsion systems is presented with synthetic fuels in order to reach the limits of the Euro 7.This Doctoral Thesis has been partially supported by the Universitat Politècnica de València through the predoctoral contract of the author (Subprograma 2), which is included within the framework of Programa de Apoyo para la Investigación y Desarrollo (PAID)Martínez Boggio, SD. (2022). Study of the Potential of Electrified Powertrains with Dual-Fuel Combustion to Achieve the 2025 Emissions Targets in Heavy-Duty Applications [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18883