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Energy-efficient powertrain components and advanced vehicle control strategies are two effective methods to promote the potential of hybrid electric vehicles (HEVs). Aiming at hybrid system efficiency improvement, this thesis presents a comprehensive review of energy-efficient hybrid powertrain specific engines and proposes three improved energy management strategies (EMSs), from a basic non-adaptive real-time approach to a state-of-the-art learning-based intelligent approach. To evaluate the potential of energy-efficient powertrain components in HEV efficiency improvement, a detailed discussion of hybrid powertrain specific engines is presented. Four technological solutions, i.e., over-expansion cycle, low temperature combustion mode, alternative fuels, and waste heat recovery techniques, are reviewed thoroughly and explicitly. Benefits and challenges of each application are identified, followed by specific recommendations for future work. Opportunities to simplify hybrid-optimized engines based on cost-effective trade-offs are also investigated. To improve the practicality of HEV EMS, a real-time equivalent consumption minimization strategy (ECMS)-based HEV control scheme is proposed by incorporating powertrain inertial dynamics. Compared to the baseline ECMS without such considerations, the proposed control strategy improves the vehicle drivability and provides a more accurate prediction of fuel economy. As an improvement of the baseline ECMS, the proposed dynamic ECMS offers a more convincing and better optimal solution for practical HEV control. To address the online implementation difficulty faced by ECMS due to the equivalence factor (EF) tuning, a predictive adaptive ECMS (A-ECMS) with online EF calculation and instantaneous power distribution is proposed. With a real-time self-updating EF profile, control dependency on drive cycles is reduced, and the requirement for manual tuning is also eliminated. The proposed A-ECMS exhibits great charge sustaining capabilities on all studied drive cycles with only slight increases in fuel consumption compared to the basic non-adaptive ECMS, presenting great improvement in real-time applicability and adaptability. To take advantage of machine learning techniques for HEV EMS improvement, a deep reinforcement learning (DRL)-based intelligent EMS featuring the state-of-the-art asynchronous advantage actor-critic (A3C) algorithm is proposed. After introducing the fundamentals of reinforcement learning, formulation of the A3C-based EMS is explained in detail. The proposed algorithm is trained successfully with reasonable convergence. Training results indicate the great learning ability of the proposed strategy with excellent charge sustenance and good fuel optimality. A generalization test is also conducted to test its adaptability, and results are compared with an A-ECMS. By showing better charge sustaining performance and fuel economy, the proposed A3C-based EMS proves its potential in real-time HEV control. / Thesis / Doctor of Philosophy (PhD)

La propulsion des véhicules électriques hybrides parallèles est généralement assurée par un moteur à combustion interne combiné à une machine électrique réversible. Les flux de puissance entre ces deux organes de traction sont déterminés par un algorithme de supervision, qui vise à réduire la consommation de carburant et éventuellement les émissions de certains polluants. Dans la littérature, la théorie de la commande optimale est maintenant reconnue comme étant un cadre puissant pour l’élaboration de lois de commande pour la gestion énergétique des véhicules full-hybrides. Ces stratégies, dénommée « Stratégies de Minimisation de la Consommation Equivalente » (ECMS) sont basée sur le principe du Maximum de Pontryagin. Pour démontrer l’optimalité de l’ECMS, on doit supposer que les limites du système de stockage ne sont pas atteintes durant le cycle de conduite. Il n’est plus possible de faire cette hypothèse lorsque l’on considère les véhicules micro et mild hybrides étudiés dans cette thèse car la variable d’état atteint généralement plusieurs fois les bornes. Des outils mathématiques adaptés à l’étude des problèmes de commande avec contraintes sur l’état sont présentés et appliqués à deux problèmes en lien avec la gestion énergétique. Le premier problème consiste à déterminer le profil optimal de la tension aux bornes d’un pack d’ultra-capacités. Le second problème se concentre sur un système électrique intégrant deux stockeurs. L’accent est mis sur l’étude des conditions d’optimalités valables lorsque les contraintes sur l’état sont actives. Les conséquences de ces conditions pour la commande en ligne sont mises en avant et exploitées afin de concevoir une commande en temps réel. Les performances sont évaluées à l’aide d’un prototype. Une comparaison avec une approche de type ECMS plus classique est également présentée. / Parallel hybrid electric vehicles are generally propelled by an internal combustion engine, which is combined to a reversible electric machine. The power flows between these two traction devices are determined by a supervisory control algorithm, which aims at reducing the fuel consumption and possibly some polluting emissions. In the literature, optimal control theory is now recognized as a powerful framework for the synthesis of energy management strategies for full hybrid vehicles. These strategies are referred to as “Equivalent Consumption Minimization Strategies” (ECMS) and are based on the Pontryagin Maximum Principle. To demonstrate the optimality of ECMS, it must be assumed that the storage system limits are not reached during the drive cycle. This hypothesis cannot be made anymore when considering the micro and mild hybrid vehicles studied in this thesis because the state variable generally reaches several times the boundaries. Some mathematical tools suitable for the study of state constrained optimal control problems are introduced and applied to two energy management problems. The first problem consists in determining the optimal profile of the voltage across a pack of ultra-capacitors. The second problem focuses on a dual storage system. The stress is put on the study of the optimality conditions holding in case of active state constraints. Some consequences of these conditions for the online control are pointed out are exploited for the design of a real-time controller. Its performances are assessed using a demonstrator vehicle. A comparison with a classical ECMS-based approach is also provided.

Les travaux de cette thèse portent sur l’investigation d’approches de commande et de supervision permettant d’aborder la problématique de gestion d’énergie des réseaux électriques multi-sources. l’objectif souhaité était de proposer une démarche de conception de lois de commande pour ce type de système en vue de réguler la tension de sortie et de gérer d’une manière optimale les flux d’énergie entre les différentes sources et les consommateurs et au vu de minimiser la consommation d’hydrogène.A cette fin, deux configurations ont été envisagées :l’application d’approches à base d’un modèle statique et des stratégies à base d’un modèle dynamique de la PàC. Dans un premier temps, trois approches de gestion énergétique ont été appliquées au système visant à minimiser la consommation de masse d’hydrogène tout en respectant les contraintes physiques du système.Tout d’abord, l’optimisation est réalisée en utilisant une méthode d’optimisation hors ligne appelée la programmation dynamique. Deuxièmement, deux approches d’optimisation en ligne sont utilisées : stratégies ECMS et MPC. Une comparaison en termes de consommation d’hydrogène et de temps de calcul est réalisée.Dans un deuxième temps, une approche décentralisée de commande a été envisagée afin de tenir compte du modèle dynamique de la PàC dans la conception du superviseur. L’avantage de cette architecture réside dans sa capacité a aborder séparément chacune des problématiques dans l’optique de répondre aux différents objectifs de commande. Dans ce cadre, la régulation du système PàC et de l’état de charge de l’ESS est réalisée séparément avec deux contrôleurs différents, tous deux conçus en utilisant l’approche (MPC-LTV). Les troisième et quatrième niveaux de la structure de contrôle décentralisée consistent en des boucles de locale des courants de la PàC et de SC et un contrôle de tension du bus continu, conçu à l’aide de contrôleurs PI. La validation de la structure de contrôle est réalisée en simulation en utilisant un modèle non linéaire du système PàC.A la fin, les approches de commande conçues à base du modèle statique de la PàC (DP, ECMS et MPC) seront appliquées sur le modèle dynamique de cette dernière afin d’évaluer les performances de ces approches et d’en déduire des conclusions sur l’apport de l’intégration de la dynamique de la PàC dans la synthèse des lois de commande. / This thesis focuses on the investigation of control approaches to treat the issue of energy management of multi-source electrical networks. The considered electric motor supply system consists on a fuel cell as a main energy source and an additional element that supplies peak power and charges by regenerative braking. At first, three energy management strategies have been applied to the sypply system aiming to minimize the fuel cell hydrogen mass consumption while satisfying the system physical constraints. First, the optimization is realized using dynamic programming,an off-line optimization method that requires the knowledge of the entire power load profile. Secondly, twoon-line optimization approaches are used : ECMS and MPC strategies, for which only the current power demand is demanded.The second part of this thesis presents a decentralized control strategy applied to the power system. The dedicated control structure aims to assure an optimal operation of the FC system while respecting the compressor physical limits and to control the converter current sand network output voltage. To attain these objectives, a dynamic model of the FC system is used,in addition to the SSE and electric network dynamics. The FC system regulation and the control of the SSE state of energy are performed separately with two different controllers, both designed using (MPC-LTV) approach. The third and fourth levels of the decentralized control structure consists on inner control loops for fuel cell/supercapacitor currents and a DC bus voltage control loop, designed using PI controllers. The validation of the control structure is performed in simulation using a nonlinear models of the FC system and the SSE. To validate and compare the performance of different control methods based on a fuel cell static model, these approaches have been applied to the dynamic model of the FC and compared to the results obtained by applying the approched designed and based on an FC dynamic model. A comparison in terms of network efficiency and hydrogen consumption has been done.

abstract: For years the automotive industry has been shifting towards hybridization and electrification of conventional powertrains due to increase in fossil fuel cost and environmental impact due heavy emission of Green House Gases (GHG) and various pollutants into atmosphere by combustion engine powered vehicles. Hybrid Electric Vehicles (HEV) have proved to achieve superior fuel economy and reduced emissions. Supervisory control strategies determining the power split among various onboard power sources are evolving with time, providing better fuel economies. With increasing complexity of control systems driving HEV’s, mathematical modeling and simulation tools have become extremely advanced and have derived whole industry into adopting Model Based Design (MBD) and Hardware-in-the-loop (HIL) techniques to validate the performance of HEV systems in real world. This report will present a systematic mythology where MBD techniques are used to develop hybrid powertrain, supervisory control strategies and control systems. To validate the effectiveness of various energy management strategies for HEV energy management in a real world scenario, Conventional rule-based power split strategies are compared against advanced Equivalent Consumption Minimization Strategy (ECMS), in software and HIL environment. Since effective utilization of the fuel reduction potential of a HEV powertrain requires a careful design of the energy management control methodology, an advanced ECMS strategy involving implementation with Fuzzy Logic to reduce computational overload has been proposed. Conventional real-time implementation of ECMS based strategy is difficult due to the involvement of heavy computation. Methods like Fuzzy Logic based estimation can be used to reduce this computational overload. Real-time energy management is obtained by adding a Fuzzy Logic based on-the-fly algorithm for the estimation of driving profile and adaptive equivalent consumption minimization strategy (A-ECMS) framework. The control strategy is implemented to function without any prior knowledge of the future driving conditions. The idea is to periodically refresh the energy management strategy according to the estimated driving pattern, so that the Battery State of Charge (SOC) is maintained within the boundaries and the equivalent fuel consumption is minimized. The performance of the presented Fuzzy Logic based adaptive control strategy utilizing driving pattern recognition is benchmarked using a Dynamic Programming based global optimization approach. / Dissertation/Thesis / Masters Thesis Engineering 2015

Les progrès dans les domaines des véhicules autonomes, l'hybridation du groupe motopropulseur et les systèmes de transport intelligents (STI) signifient que l'automobile en tant que machine est sur le point d'être réinventée. Les trois domaines technologiques sus-cités ont ouvert des portes sur des avancées possibles au niveau de l'amélioration de la sécurité routière et de l'efficacité énergétique des véhicules qui étaient auparavant limitées en raison de plusieurs facteurs, comme les capacités de détection et de puissance de calcul. Dans ce contexte, un contrôleur de la dynamique longitudinale du véhicule électrique est mis au point et étudié de façon à réaliser un compromis entre sécurité et efficacité du véhicule. Ce système est appelé Smart And Green Adaptive Cruise Control (SAGA).Le développement de cette fonction est basée sur l'optimisation de l'énergie ainsi que sur des stratégies de régénération d'énergie en respectant les contraintes des composants du groupe motopropulseur comme la charge de la batterie, la capacité de freinage du moteur et de la situation courante dans le trafic routier. Dans ce processus, des techniques d'optimisation comme la programmation dynamique et la stratégie de minimisation de la consommation d'énergie équivalente (ECMS) sont utilisés. Utilisant des modèles d'énergie du véhicule et des modèles cinématiques intégrés sur Matlab-Simulink, ce travail de thèse évalue les avantages et les limites de l'utilisation de la fonction SAGA pour diverses topologies de véhicules pour différents scénarios de trafic. / The progress in the fields of autonomously driven vehicles, powertrain hybridization and Intelligent transportation systems (ITS) means that the automobile as a machine, is on the verge of reinvention. The aforementioned three fields of technologies have opened doors to advanced opportunities in improvement of safety and efficiency of vehicles which were earlier limited due to several factors like sensing capacities and computational power.In this context, a vehicle longitudinal motion controller is developed and investigated which will actively balance vehicle safety and efficiency. It is named as the Smart and Green Adaptive Cruise Control System (SAGA). Development of this function is based on optimization of energy supply as well as energy regeneration strategies with respect to powertrain component constraints like battery charge acceptance, motor braking capacity and traffic situation. In this process, optimization techniques like Dynamic programming and Equivalent Consumption Minimization Strategy (ECMS)are used. Using vehicle energy and kinematic models built in Matlab-Simulink platform, this dissertation evaluates the advantages and limitations of using SAGA function for various vehicles topologies and in different traffic scenarios.

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With increasing demands on lowering carbon emissions, fuel cell hybrid electric vehicles (FCHEV) have been seen as an alternative to the fossil-fuel driven trucks of today. These would have less emissions and strive to have the same range as any diesel driven transport vehicle. A lot of effort and resources have been put into fuel cell research for incorporation in new powertrains. There are however many different fuel cell types, so the aim of the thesis was to explore two different fuel cell types for use in a FCHEV model.The thesis sets up a model consisting of various subsystems of a high temperature proton exchange membrane fuel cell (HT-PEMFC). Components for the power electronics and a cooling system are also incorporated. The system was then combined with a vehicle model, where a power split between the fuel cell and battery was investigated. The performance of the HT-PEMFC was compared to a low temperature proton exchange membrane fuel cell (LT-PEMFC) on three levels with increasing complexity. These were on a single cell level, stack level and on a vehicle level.The results showed that the HT-PEMFC had worse performance than the LT-PEMFC on both a cell and vehicle level. The power output of an HT-PEMFC was lower for all current densities, meaning more cells were needed in order for the HT-PEMFC to have the same power output as an LT-PEMFC. It did however have a better cooling ability and was a simpler system, which therefore does warrant further investigation on its future use in transport applications. If heat recuperation was investigated further, the HT-PEMFC performance would have been increased to a higher degree than the LT-PEMFC.