This thesis proposes a novel framework for solving the energy management problem of Hybrid Electric Vehicles (HEVs). We aim to establish a practical and effective approach targeting an optimal Energy Management Strategy (EMS). A situation-specific Equivalent Consumption Minimization Strategy (ECMS) is developed to minimize fuel consumption and improve battery charge sustainability while maintaining an acceptable drive quality. The investigated methodology will be broadly applicable to all HEV applications; however, it will be well-suited for hybrid electric delivery applications. / Thesis / Master of Applied Science (MASc)

Personal vehicles have made great contributions to our life and satisfy our daily mobility needs. However, they have also caused societal issues, such as air pollution and global warming. Further to the recent attention to low-carbon energy technologies and environmentally friendly mobility, hybrid electric vehicles play an important role in the current automotive industry. As a leading center and an educational institution in Canada, McMaster University wants to build a Hybrid Electric Vehicle Powertrain Laboratory for introducing undergraduate students to hybrid powertrain architectures, instrumentation and control. A phased development of the hybrid powertrain teaching laboratory is being pursued. The first phase is to design a electric motor laboratory, as a platform for demonstrating motor characteristics. A LabVIEW based interface is designed to enable electric motor characterization tests. This laboratory set-up is still under construction. Real experiments would be implemented, once finishing the utility connections. For the hybrid powertrain laboratory, an innovative design architecture is proposed to enable different hybrid architectures, such as series, parallel, and power-split modes to be investigated. Instead of a planetary gearbox, bevel gearboxes with a continuous variable transmission (CVT) are used for making the laboratory more compact and flexible for demonstrating hybrid functionalities. The additional generator provides the ability of input power-split for allowing the engine to operate at a narrow high efficiency region. After designing the hybrid laboratory, a novel rule-based energy management strategy is applied to a simplified simulation model. / Thesis / Master of Applied Science (MASc)

Στόχος της παρούσας διπλωματικής εργασίας ήταν η μελέτη και η υλοποίηση στρατηγικής διαχείρισης ενέργειας για τη βελτιωμένη οικονομική λειτουργία υβριδικού οχήματος με χρήση ψηφιακού μικροελεγκτή. Η προτεινόμενη στρατηγική, η οποία βασίζει τη λειτουργία της στην αρχή της ασαφούς λογικής αναπτύχθηκε και αρχικά δοκιμάστηκε, χρησιμοποιώντας το πρόγραμμα Matlab/Simulink. Για την επιβεβαίωση της ορθής λειτουργίας του συνολικού συστήματος αποτελούμενου από το ηλεκτρικό κινητήριο σύστημα, τη μηχανή εσωτερικής καύσης και το σύστημα διαχείρισης της ενέργειας, δημιουργήθηκε ένα μαθηματικό μοντέλο ενός υβριδικού οχήματος παράλληλης διάταξης, επίσης στο περιβάλλον Simulink,στο οποίο η διαχείριση της ενέργειας γίνεται μέσω του ανεπτυχθέντος ασαφούς ελεγκτή. Στόχος ήταν να ελεγχθεί η συμπεριφορά του ελεγκτή αυτού. Τέλος, κατασκευάστηκε το κύκλωμα υλοποίησης του ασαφούς ελεγκτή, χρησιμοποιώντας τον ψηφιακό μικροελεγκτή dsPIC30f4011 της εταιρίας Microchip. Για την υλοποίηση της λειτουργίας του ελεγκτή, αναπτύχθηκε ο αντίστοιχος κώδικας σε γλώσσα C, η λειτουργία του οποίου επιβεβαιώθηκε πειραματικά. / The objective of this master thesis was the study and the implementation of an energy management strategy, aiming for improved economic operation of hybrid vehicle using a digital microcontroller. The proposed strategy, based on the principles of fuzzy logic, was developed and initially tested using the environment Matlab/Simulink. To ascertain the correct operation of the system constituted by the electric motive system, the internal combustion machine and the energy management system, a mathematic model of hybrid vehicle of parallel provision was also created in Simulink. The energy management is implemented by the fuzzy controller. The main objective was to test the behavior of this controller. Finally, the electronic circuit of the fuzzy controller was manufactured. The digital microcontroller dsPIC30f4011 (Microchip company) was used. For the implementation of the operation of the controller, the corresponding code was developed in C language, the operation of which was experimentally confirmed.

The Energy Management Strategy (EMS) has a huge effect on the performance of any hybrid vehicle because it determines the operating point of almost every component associated with the powertrain. This means that its optimisation is an incredibly complex task which must consider a number of objectives including the fuel consumption, drive-ability, component degradation and straight-line performance. The EMS is of particular importance for Fuel Cell Hybrid Electric Vehicles (FCHEVs), not only to minimise the fuel consumption, but also to reduce the electrical stress on the fuel cell and maximise its useful lifetime. This is because the durability and cost of the fuel cell stack is one of the major obstacles preventing FCHEVs from being competitive with conventional vehicles. In this work, a novel EMS is developed, specifcally for Fuel Cell Hybrid Electric Vehicles (FCHEVs), which considers not only the fuel consumption, but also the degradation of the fuel cell in order to optimise the overall running cost of the vehicle. This work is believed to be the first of its kind to quantify effect of decisions made by the EMS on the fuel cell degradation, inclusive of multiple causes of voltage degradation. The performance of this new strategy is compared in simulation to a recent strategy from the literature designed solely to optimise the fuel consumption. It is found that the inclusion of the degradation metrics results in a 20% increase in fuel cell lifetime for only a 3.7% increase in the fuel consumption, meaning that the overall running cost is reduced by 9%. In addition to direct implementation on board a vehicle, this technique for optimising the degradation alongside the fuel consumption also allows alternative vehicle designs to be compared in an unbiased way. In order to demonstrate this, the novel optimisation technique is subsequently used to compare alternative system designs in order to identify the optimal economic sizing of the fuel cell and battery pack. It is found that the overall running cost can be minimised by using the smallest possible fuel cell stack that will satisfy the average power requirement of the duty cycle, and by using an oversized battery pack to maximise the fuel cell effciency and minimise the transient loading on the stack. This research was undertaken at Loughborough University as part of the Doctoral Training Centre (DTC) in Hydrogen, Fuel Cells and Their Applications in collaboration with the University of Birmingham and Nottingham University and with sponsorship from HORIBA-MIRA (Nuneaton, UK). A Microcab H4 test vehicle has been made available for use in testing for this research which was previously used for approximately 2 years at the University of Birmingham. The Microcab H4 is a small campus based vehicle designed for passenger transport and mail delivery at low speeds as seen on a university campus. It has a top speed of approximately 30mph, and is fitted with a 1.2kW fuel cell and a 2kWh battery pack.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

This thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world drivecycles and its performance is compared against global optimal results evaluated using DP. / Thesis / Master of Applied Science (MASc)

I samband med den globala uppvärmningen ökar efterfrågan för rena och förnybara bränslen alltmer i dagens samhälle. Eftersom flygindustrin idag är ansvarig för samma mängd växthusgaser som all motortrafik i Sverige, skulle ett byte till en avgasfri energikälla för flygfarkoster vara ett stort framsteg. Därför har projektet genom modellering framtagit ett hybridsystem av ett batteri och en bränslecell och undersökt hur kombinationen av olika storlekar på dem presterar i en driftcykel. Då batterier har hög specifik effekt men är tunga, kompletteras de med fördel av bränsleceller, som är lättviktiga och bidrar med uthållig strömförsörjning. På så sätt blir hybriden optimal för flygfarkoster. Kandidatarbetet är en del av projektet Green Raven, ett tvärvetenskapligt samarbete mellan instutitionerna Tillämpad Elektrokemi, Mekatronik och Teknisk Mekanik på Kungliga Tekniska Högskolan. Driftcykelmodelleringen gjordes i Simulink, och flera antaganden gjordes beträffande effektprofilen, samt bränslecellens mätvärden och effekt. Tre olika energihushållningsscheman skapades, vilka bestämde bränslecellseffekten beroende på vätgasnivån och batteriets laddningstillstånd. Skillnaden på systemen var vilka intervall av laddningstillstånd hos batteriet som genererade olika effekt hos bränslecellen.  Det bästa alternativet visade sig vara 0/100-systemet, eftersom det var det enda som inte orsakede någon degradering av bränslecellens kapacitet. / In today’s society, with several environmental challenges such as global warming, the demand for cleanand renewable fuels is ever increasing. Since the aviation industry in Sweden is responsible for the sameamount of greenhouse gas emissions as the motor traffic, a change to a non-polluting energy source forflying vehicles would be considerable progress. Therefore, this project has designed a hybrid system of abattery and a fuel cell and investigated how different combinations of battery and fuel cell sizes perform ina drive cycle, through computer modelling. As batteries possess a high specific power but are heavy, thefuel cells with high specific energy complement them with a sustained and lightweight power supply,which makes the hybrid perfect for aviation. The bachelor thesis is a part of Project Green Raven, aninterdisciplinary collaboration with the institutions of Applied Electrochemistry, Mechatronics andEngineering Mechanics at KTH Royal Institute of Techology. The drive cycle simulations were done inSimulink, and several assumptions regarding the power profile, fuel cell measurements and power weremade. Three different energy management strategies were set up, determining the fuel cell powerdepending on hydrogen availability and state of charge of the battery. The strategies were called 35/65,20/80 and 0/100, and the difference between them was at which state of charge intervals the fuel cellchanged its power output. The best strategy proved to be 0/100, since it was the only option which causedno degradation of the fuel cell whatsoever.

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)

The automotive industry is inevitably experiencing a paradigm shift from fossil fuels to electric powertrain with significant technological breakthroughs in vehicle electrification. Emerging hybrid electric vehicles were one of the first steps towards cleaner and greener vehicles with a higher fuel economy and lower emission levels. The energy management strategy in hybrid electric vehicles determines the power flow pattern and significantly affects vehicle performance. Therefore, in this thesis, a learning-based strategy is proposed to address the energy management problem of a hybrid electric vehicle in various driving conditions. The idea of a deep recurrent neural network-based energy management strategy is proposed, developed, and evaluated. Initially, a hybrid electric vehicle model with a rule-based supervisory controller is constructed for this case study to obtain training data for the deep recurrent neural network and to evaluate the performance of the proposed energy management strategy. Secondly, due to its capabilities to remember historical data, a long short-term memory recurrent neural network is designed and trained to estimate the powertrain control variables from vehicle parameters. Extensive simulations are conducted to improve the model accuracy and ensure its generalization capability. Also, several hyper-parameters and structures are specifically tuned and debugged for this purpose. The novel proposed energy management strategy takes sequential data as input to capture the characteristics of both driver and controller behaviors and improve the estimation/prediction accuracy. The energy management controller is defined as a time-series problem, and a network predictor module is implemented in the system-level controller of the hybrid electric vehicle model. According to the simulation results, the proposed strategy and prediction model demonstrated lower fuel consumption and higher accuracy compared to other learning-based energy management strategies. / Thesis / Master of Applied Science (MASc)