Energy management strategies for charge sustaining hybrid electric vehicles reduce fuel power consumption from the engine and electric power consumption from the motor while meeting output power demand. The equivalent consumption minimization strategy is a real time control strategy which uses backward facing models and an equivalence ratio to calculate the lowest total fuel power consumption. The equivalence ratio quantifies the fuel power to battery power conversion process of the hybrid electric vehicle components and therefore quantifies electric power consumption in terms of fuel power consumption. The magnitude of the equivalence ratio determines when the hybrid electric vehicle commands a conventional, electric, or hybrid mode of operation. The equivalence ratio therefore influences the capability of the control strategy to meet charge sustaining performance. Willans line models quantify the input power to output power relationship for powertrain and drivetrain components with a linear relationship and a constant offset. The hybrid electric vehicle model performance is characterized using three Willans line models in the equivalent consumption minimization strategy. The slope of the Willans line models, or marginal efficiency, is used to generate a single equivalence ratio which quantifies the fuel to battery energy conversion process for the hybrid electric vehicle. The implementation of a Willans line based equivalent consumption minimization strategy reduces total fuel power consumption while achieving charge sustaining performance over mild and aggressive drive cycles. / Master of Science / The charge sustaining hybrid electric vehicle in this paper generates output power with an internal combustion engine powered by a fuel tank and an electric traction motor powered by a battery pack. Hybrid electric vehicle energy management strategies generate torque commands to meet output power demand based on the minimum total input power from both the fuel tank and battery pack. Willans line models simplify the energy management strategy by quantifying the output power to input power relationship, or efficiency, of each component with a linear slope and constant offset. The use of Willans line models quantifies the efficiency of the hybrid electric vehicle with three linear relationships. Energy management strategies also ensure the battery pack starts and ends at the same operating condition to maintain charge sustaining performance. Charge sustaining hybrid electric vehicles therefore use the battery pack as an energy buffer and do not need to be charged by an external power supply since all energy comes from fuel. The output to input power relationship of Willans line models quantifies the power conversion of the hybrid electric vehicle and coupled to a term which accounts for changes in the battery pack. The use of Willans line models in hybrid electric vehicles effectively generates torque commands to the engine and motor while improving fuel economy and maintaining charge sustaining performance.

Increased environmental awareness together with new legislative demands on lowered emissions and a rising fuel cost have put focus on increasing the fuel efficiency in new vehicles. Hybridization is a way to increase the efficiency of the powertrain.The Haldex electric Torque Vectoring Device is a rear axle with a built in electric motor, designed to combine all-wheel drive with hybrid functionality. A method is developed for creating a real time control algorithm that minimizes the fuel consumption. First the consumption reduction potential of the system is investigated using Dynamic Programming. A real time control algorithm is then devised that indicates a substantial consumption reduction potential compared to all-wheel drive, under the condition that the assumed and measured efficiencies are accurate. The control algorithm is created using equivalent consumption minimization strategy and is implemented without any knowledge of the future driving mission. Two ways of adapting the control according to the battery state of charge are proposed and investigated. The controller optimizes the torque distribution for the current gear as well as assists the driver by recommending the gear which would give the lowest consumption. The simulations indicate a substantial fuel consumption reduction potential even though the system primarily is an all-wheel drive concept. The results from vehicle tests show that the control system is charge sustaining and the driveability is deemed good by the test-drivers.

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.

This thesis outlines the development of a control system for a series-parallel plugin hybrid electric vehicle. The vehicle, developed at McMaster University for the EcoCAR 3 Advanced Vehicle Technology Competition, was produced in an effort to provide a Chevrolet Camaro with a high-performance, fuel efficient, hybrid powertrain. A rational design methodology was adopted and guided the development of the control system and the implementation of its respective algorithms. A simulation tool was created using MATLAB and Simulink which, in turn, allowed for the effectiveness of the supervisory control logic to be evaluated by approximating the vehicle’s energy consumption, fuel consumption, and emissions. The impact of hybridizing the vehicle’s powertrain was similarly assessed by comparing it against its unelectrified counterpart, the 2016 Chevrolet Camaro LT. A solution to the vehicle’s energy management problem was proposed in the form of an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) which was then evaluated against more common heuristic approaches as well as non-adaptive instantaneous minimization methods. An artificial neural network was selected as the strategy’s adaptation mechanism and it was used to identify specific vehicular driving patterns in real-time. The neural network addresses many issues that arise due to the sensitivity of algorithms that attempt to solve the energy management problem without prior knowledge of the driving cycle. The methods used during the process of the control system’s verification and calibration are also discussed in this thesis and, in addition, encompass the use of software representations of the vehicle’s Electronic Control Units (ECUs), the development of test cases, and the supervisory control software’s evaluation in the Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) environments. / Thesis / Master of Applied Science (MASc) / Compared to conventional combustion vehicles, an automobile with an electrified propulsion system has the potential to reduce fuel consumption and emissions due to the presence of an energy storage system and one or more electric machines. These benefits, however, come at the cost of increased control system complexity. The question of how and when to use alternative energy sources – whether it be electrical or fuel energy – in a hybrid vehicle is at the epicenter of research and development initiatives in the automotive industry. Traditional heuristic methods have proven to be unstable due to their sensitivity to driving conditions and that optimal control policies require prior knowledge of the vehicle’s route and destination, and therefore, are not suitable in most applications. Strategies which attempt to instantaneously minimize a vehicle’s fuel or energy consumption, however, can overcome these aforementioned obstacles. As such, this area of research and development has received much interest. The objective of this research was twofold: the first being to develop a control system for a series-parallel plug-in hybrid electric vehicle in a rational and systematic manner, and, secondarily, to evaluate the benefits of instantaneous minimization methods for energy management.

Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications. The contribution of the thesis can be summarized as follows: • Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust. • Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs. • Developing a one-step prediction control method for improving the gearchanging decision. • Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod. • Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck. / <p>QC 20160128</p>

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In recent years, rules and regulations regarding fuel consumption of vehicles and the amount of emissions produced by them are becoming stricter. This has led the automotive industry to develop more advanced solutions to propel vehicles to meet the legal requirements. The Hybrid Electric Vehicle is one of the solutions that is becoming more popular in the automotive industry. It consists of an electrical driveline combined with a conventional powertrain, propelled by either a diesel or petrol engine. Two power sources create the possibility to choose when and how to use the power sources to propel the vehicle. The strategy that decides how this is done is referred to as an energy management strategy. Today most energy management strategies only try to reduce fuel consumption using models that describe the steady state behaviour of the engine. In other words, no reduction of emissions is achieved and all transient behaviour is considered negligible.  In this thesis, an energy management strategy incorporating engine dynamics to reduce fuel consumption and nitrogen oxide emissions have been designed. First, the models that describe how fuel consumption and nitrogen oxide emissions behave during transient engine operation are developed. Then, an energy management strategy is developed consisting of a model predictive controller that combines the equivalent consumption minimization strategy and convex optimization. Results indicate that by considering engine dynamics in the energy management strategy, both fuel consumption and nitrogen oxide emissions can be reduced. Furthermore, it is also shown that the major reduction in fuel consumption and nitrogen oxide emissions is achieved for short prediction horizons.

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An emerging technology is the hybridization of wheel loaders. Since wheel loaders commonly operate in repetitive cycles it should be possible to use this information to develop an efficient energy management strategy that decreases fuel consumption. The purpose of this thesis is to evaluate if and how this can be done in a real-time online application. The strategy that is developed is based on pattern recognition and Equivalent Consumption Minimization Strategy (ECMS), which together is called Adaptive ECMS (A-ECMS). Pattern recognition uses information about the repetitive cycles and predicts the operating cycle, which can be done with Neural Network or Rule-Based methods. The prediction is then used in ECMS to compute the optimal power distribution of fuel and battery power. For a robust system it is important with stability implementations in ECMS to protect the machine, which can be done by adjusting the cost function that is minimized. The result from these implementations in a quasistatic simulation environment is an improvement in fuel consumption by 7.59 % compared to not utilizing the battery at all.