Online power management with embedded optimization for a multi-source hybrid with real time applications

The focus of this thesis is to develop a suitable power management optimization strategy for a three-source hybrid vehicle powertrain. This strategy takes into account the integration of optimized parameters that limit the battery and fuel cell current by utilizing a third power source, namely supercapacitor. The goal is to develop a modular structure with decoupled online and offline parts such that implementation in case of real driving conditions is feasible. Based on the literature review it can be concluded that providing optimal solutions in terms of multiple objectives online is an issue. Adaption of optimized control strategy to real driving data is another concern. The developed strategy employs an online rule-based control with embedded offline-optimized parameters. The parameters are optimized with respect to multiple and conflicting objectives such as fuel consumption and state-of-charge deviation minimization. By a suitable selection of parameters, operation of all three sources within desired working ranges is possible, keeping in mind the load demand. By varying the weights between the objectives, one or more objectives can be given more priority than others. The application of this concept to fuel cell-battery-supercapacitor hybrid is discussed in this thesis. Detailed modeling of all components along with verification and plausibility assessment is done. For the purpose of experimental validation, the real powertrain components are replaced by controllable power sources and sinks that emulate the dynamics of real components. Finally, a brief concept is presented to integrate the developed power management optimization in real driving scenarios. For validation/verification purposes, a driving simulator environment is connected to the experimental hybrid electric vehicle set-up and with the help of an illustrative example, the desired predicted optimal values are calculated online and displayed to the human driver by a suitable interface. The absence of online tuning of controller parameters in this example is counteracted by developing a concept based on literature. With the help of this concept, the adaption of the power management control concept, developed in this thesis, can be realized

Combined Design and Control Optimization: Application to Optimal PHEV Design and Control for Multiple Objectives.

This dissertation develops algorithms for optimal design and control solutions of dynamic systems in a computationally efficient manner. These methods are demonstrated by applying them to a Plug-in Hybrid Electric Vehicle (PHEV) powertrain’s optimal design and control. Since a PHEV draws energy from the grid it is important to consider these interactions in its optimal design and control decisions. The battery size also affects the amount of grid energy transferred to propulsion and consequently the on-road power management decisions. Thus, we develop algorithms to determine the optimal PHEV battery size and control decisions considering conditions on the electric grid. First, we develop a Dynamic Programming (DP) based algorithm for optimal on-road power management of a series PHEV. A backward looking implementation of the PHEV powertrain’s dynamic model with the DP algorithm avoided the need to interpolate the value function or enforce constraints through penalty functions, thereby alleviating computational concerns. This algorithm is extended to consider optimal charging on the electric grid by utilizing conditions at the boundaries of the optimal charging and driving problems. The results exposed tradeoffs between the two problems. This algorithm was further applied to determine the optimal CO2 reduction benefits in propulsion depending on wind penetration on the grid. Since PHEVs are expected to address emissions, cost, and participate in grid services, we develop a multi-objective dynamic programming (MODP) algorithm. This algorithm utilizes the idea of crowding distance from Non-Dominated Sort Genetic Algorithms (NSGA) literature to represent the Pareto front with fewer points, easing the computational time and memory requirements. Tradeoffs in achieving minimum CO2 vs. minimum operational costs are discussed. Finally, we utilize the theory on combined optimization of a system’s design and control for PHEV battery sizing considering its optimal charging and power management. Salient features of the algorithm such as calculation and use of a sensitivity term and the reduction of computational effort are demonstrated by solving a beam mass reduction and vibration attenuation problem. Then, this algorithm is applied for optimal battery sizing of a PHEV. The sensitivity values provided insights for optimal PHEV battery sizing while considering the optimal control.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/93962/1/rakeshmp_1.pd

Journey predictive energy management strategy for a plug-in hybrid electric vehicle

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonisation of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialisation. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Further, recent studies suggest the use of \intelligent transport" infrastructure to include a predictive element to the energy management strategy to achieve reductions in emissions. The thesis addresses the problem of determining the links between component-sizing, real-world usage and energy management strategies for a PHEV. The objective is to develop an integrated framework in which the advantages of predictive energy management can be realised by component downsizing for a PHEV. The study is spilt into three sections. The first part presents the framework by which the predictive element can be included into the PHEV's energy management strategy. Second part describes the development of the PHEV component models and the various energy management strategies which control the split in energy used between the engine and the battery. In this section a new control strategy is presented which integrates the predictive element proposed in the first part. Finally, in the third section an optimisation framework is presented by which the size of the components within the PHEV are reduced due to the lower energy demands of the new proposed energy management strategy. The first part of the study presents a framework by which the energy consumption of a vehicle may be predicted over a route. The proposed energy prediction framework employs a neural network and was used o_-line for estimating the real-world energy consumption of the vehicle so that it can be later integrated within the vehicles energy management control system. Experimental results show an accuracy within 20%-30% when comparing predicted and measured energy consumptions for over 800 different real-world EV journeys … [cont.]

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

Design and energy management of aircraft hybrid electric propulsion system.

This thesis investigates the design and development of a Hybrid Electric Propulsion System (HEPS) for aircraft. The main contributions of the study are the multi-objective system sizing and the two energy optimization algorithms. First, the system sizing method is employed to design the hybrid electric propulsion system for a prototype aircraft. The sized hybrid propulsion system can ensure that no significant performance is sacrificed and the fuel economy is improved. The novel approach in this work is a new non-dominated sorting algorithm for the Non-dominated Sorting Genetic Algorithm (NSGA). The new algorithm can improve the time complexity of non-dominated sorting process. The optimized hybrid aircraft can save up to 17% fuel, achieve higher cruising speed and rate of climb. It is concluded that the optimal results are more sensitive to the variation of battery energy density than other parameters. Next, the main components of the HEPS are modelled for example. The engine model provides an insight into the inherent relationship between the throttle command and the output torque. Regarding the d-q model of motor/generator, the estimation of torque loss at steady state is achieved using the efficiency map from experiments. The application of Shepherd model leads to the straightforward parameter identification. In this research, both non-causal and causal energy management strategies for HEPS are investigated. The main novelty when studying convex optimization is the proposal of a new lossless convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and convexified problem. The introduced variable—battery internal energy, is proposed to convexify the battery model. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with much less optimization time. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application. In causal control, the main focus is to extend the original Equivalent Consumption Minimization Strategy (ECMS) with the fuzzy control. The proposed algorithm can maintain the battery State of Charge (SoC) in a desirable range, without the requirement of off-line estimation of equivalence factor. By comparing with non-causal control—dynamic programming, the test cases validates that the fuzzy based ECMS succeeds in converting the non-causal optimization, with little sacrifice of the optimality of the solution. In other words, the prior-knowledge of flight mission is not a pre- requisite, and the fuzzy based ECMS can achieve the sub-optimal control for on-line implementation. The fuzzy based ECMS is also validated to outperform the adaptive ECMS, since it can reduce the computation time of optimization and save more fuel usage. The theoretical relationship between the equivalence factor of ECMS and the co-state variable of Hamiltonian function is also demonstrated in this thesis. The convex optimization and fuzzy based ECMS are combined to complete a flight mission with several sub-tasks. Each task has different power and SoC requirements. The test case demonstrates that only the combination of non-causal and causal optimization can satisfy the various constraints and requests of the test scenario. Compared with the engine-only powered aircraft, the hybrid powered aircraft saves 18.7% on fuel consumption. Furthermore, the hybrid propulsion system has better efficiency since it integrates the high efficient electric powertrain.PhD in Aerospac

Developing and Evaluating the driving and powertrain systems of automated and electrified vehicles (AEVs) for sustainable transport

In the transition towards sustainable transport, automated and electrified vehicles (AEVs) play a key role in overcoming challenges such as fuel consumption, emissions, safety, and congestion. The development and assessment of AEVs require bringing together insights from multiple disciplines such as vehicle studies to design and control AEVs and traffic flow studies to describe and evaluate their driving behaviours. This thesis, therefore, addresses the needs of automotive and civil engineers, and investigates three classes of problems: optimizing the driving and powertrain systems of AEVs, modelling their driving behaviours in microscopic traffic simulation, and evaluating their performance in real-world driving conditions. The first part of this thesis proposes Pareto-based multi-objective optimization (MOO) frameworks for the optimal sizing of powertrain components, e.g., battery and ultracapacitor, and for the integrated calibration of control systems including adaptive cruise control (ACC) and energy management strategy (EMS). We demonstrate that these frameworks can bring collective improvements in energy efficiency, greenhouse gas (GHG) emissions, ride comfort, safety, and cost-effectiveness. The second part of this thesis develops microscopic free-flow or car-following models for reproducing longitudinal driving behaviours of AEVs in traffic simulation, which can support the needs to predict the impact of AEVs on traffic flow and maximize their benefits to the road network. The proposed models can account for electrified vehicle dynamics, road geometric characteristics, and sensing/perception delay, which have significant effects on driving behaviours of AEVs but are largely ignored in traffic flow studies. Finally, we systematically evaluate the energy and safety performances of AEVs in real-world driving conditions. A series of vehicle platoon experiments are carried out on public roads and test tracks, to identify the difference in driving behaviours between ACC-equipped vehicles and human-driven vehicles (HDVs) and to examine the impact of ACC time-gap settings on energy consumption

Optimal control of a motor-integrated hybrid powertrain for a two-wheeled vehicle suitable for personal transportation

The present research aims to propose an optimized configuration of the motor integrated power-train with an optimal controller suitable for small power-train based two wheeler automobile which can increase the system level efficiency without affecting drivability. This work will be the foundation for realizing the system in a production ready vehicle for the two wheeler OEM TVS Motor Company in India. A detailed power-train model is developed (from first principles) for the scooter vehicle, which is powered by a 110 cc spark ignition (SI) engine and coupled with two types of transmission, a continuous variable transmission (CVT) and a 4-speed manual transmission (MT). Both models are capable of simulating torque and NOx emission output of the SI engine and dynamic response of the full power-train. The torque production and emission outputs of the model are compared with experimental results available from TVS Motor Company. The CVT gear ratio model is developed using an indirect method and an analytical model. Both types of powertrain models are applied to perform a simulated study of fuel consumption, NOx emission and drivability study for a particular vehicle platform. In the next stage of work, the mathematical model for a brush-less direct current machine (BLDC) with the drive system and Li-Ion battery are developed. The models are verified and calibrated with the experimental results from TVS Motor Company. The BLDC machine is integrated with both the CVT and MT powertrain models in parallel hybrid configurations and a drive cycle simulation is conducted for different static assist levels by the electrical machines. The initial test confirms the need of optimal sizing of the powertrain components as well as an optimal control system. The detailed model of the powertrain is converted to a control-oriented model which is suitable for optimal control. This is followed by multi-objective optimization of different components of the motor-integrated powertrain using a single function as well as Pareto-Optimal methods. The objective function for the multi-objective optimization is proposed to reduce the fuel consumption with battery charge sustainability with least impact on the increase of financial cost and weight of the vehicle. The optimization is conducted by a nested methodology that involves Particle Swarm Optimization and a Non-dominated sorting genetic algorithm where, concurrently, a global optimal control is developed corresponding to the multi-objective design. The global optimal controller is designed using dynamic programming. The research is concluded with an optimal controller developed using the hp-collocation method. The objective function of the dynamic programming method and hp-collocation method is proposed to reduce fuel consumption with battery charge sustainability.Open Acces