This thesis discusses a general approach to hybrid powertrain control based on optimisation and optimal control techniques. A typical strategy comprises a high level non-linear control for optimised energy efficiency, and a lower level Linear Quadratic Regulator (LQR) to track the high-level demand signals and minimise the first torsional vibration mode. The approach is demonstrated in simulation using a model of the Toyota Prius hybrid vehicle, and comparisons are made with a simpler control system which uses proportional integral (PI) control at the lower level. The powertrain of the Toyota Prius has a parallel configuration, comprising a motor, engine and generator connected via an epicyclic gear train. High level control is determined by a Power Efficient Controller (PE C) which dynamically varies the operating demands for the motor, engine and generator. The PEC is an integrated nonlinear controller based on an iterative downhill search strategy for optimising energy efficiency and battery state of charge criteria, and fully accounts for the non-linear nature of the various efficiency maps. The PEC demand signals are passed onto the LQR controller where a cost function balances the importance of deviations from these demands against an additional criterion relating to the amplitude of driveline vibrations. System non-linearity is again accounted for at the lower level through gain scheduling of the LQR controller. Controller performance is assessed. in simulation, the results being compared with a reference system that uses simple PI action to deliver low-level control. Consideration is also given to assessing performance against that of a more general, fully non-linear dynamic optimal controller.

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The thesis introduces the user to programming in Simscape language. A permanent magnet synchronous machine torque control drive system for hybrid electric vehicles has been analyzed, programmed, using Simscape language, and tested in this thesis. The thesis walks the reader through the process of creating custom components in Simscape language explaining details and syntax of the language at every step. Important excerpts of code for all the components designed, created and used in the process are explained in the thesis and the complete code for the same is provided in the Appendix. ix

The Subject of this work is the optimization of the control stratgy for a Plug-In Range Extender in order to decrease CO2 emissions with respect to the regulations. Therefore, the vehicle is equipped with a gasoline combustion engine, a high voltage battery and two electric motors. One electric motor propells the front axle and the other one is connected to the combustion engine to generate electric power. The control is restricted by customer requirements due to the concept of the vehicle. A Model-in-the-Loop is created to simulate the control strategy with support of a battery model. Therefore, the control strategy is optimized in a Matlab/Simulink environment. The simulation results are compared to tests on the dynamometer rig. The optimization highly depends on the specific goal function, which can be a global optimization or a local minimum to balance the State of Charge. Furthermore, customer related drive cycles are taken into account to analyze the control strategy. / Master of Science

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Many engineering problems are naturally posed as optimal control problems. It may involve moving between two points in the fastest possible way, or to put a satellite into orbit with minimum energy consumption. Many optimal control problems are too difficult to be solved analytically and therefore require the use of numerical methods. The numerical methods that are the most widespread are the so-called direct methods. However, there is one major drawback with these. If the problem is non-convex, the solution is not guaranteed globally optimal, that is, the absolute best, instead it is guaranteed locally optimal, that is the best in its vicinity. To compensate for this, the problem should be solved several times, under different conditions, in order to investigate whether the solution is a good candidate for the global optimum. CasADi is a software specifically designed for dynamic optimization. It has gained wide spread in recent years because it provides all the necessary building blocks for dynamic optimization. This has given individual engineers and scientists the ability to independently formulate and solve all sorts of optimal control problems. However, this requires good theoretical knowledge of the necessary numerical methods. The advantage of a toolbox, which solves general optimal control problems, is that the underlying numerical methods have been tested and shown to function on optimal control problems with known solutions. This means that the user does not need exhaustive knowledge of the numerical methods involved, but can focus on formulating and solving optimal control problems. The main contribution of this thesis is an optimal control toolbox for MATLAB based on CasADi. The toolbox does not require expert knowledge of the numerical methods, but provides an alternative lower level abstraction that allows for more complex problem formulations. The toolbox implements two direct methods, direct multiple shooting and direct collocation. This allows a problem formulation with many degrees of freedom. The most important property of the toolbox is that the discretization can be changed, without the problem formulation needing to be altered. This way the user can easily change the conditions for his/her problem. The thesis describes how the two implemented direct methods work, and the design choices made. It also describes what remains to test and evaluate, and the problems that have been used as a reference during the development process.

Master of Science / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Noel Schulz / With increasing costs for fuel there is a growing interest in improving fuel efficiency and performance of military vehicles by employing (1) hybrid drive train architecture; (2) reliable vehicle power system structure, and (3) effective power management strategies of multiple power sources (engine, battery and ultracapacitor) and vehicle electrical loads. However, current ruled-based power management strategies that focus primarily on traction fail to meet the rapidly increasing requirements of military vehicles, including: (1) better fuel economy; (2) the ability to support pulsed power weapon loads; (3) maintaining battery SOC for power offloading applications, and (4) the ability to perform load scheduling of vehicle non-traction electrical loads to save energy. In this thesis, we propose an optimal control based algorithm in conjunction with a rule-based control strategy to optimally manage three power sources (engine, battery and pulsed power supply module) and an effective power management solution for vehicle non-traction electrical loads such that: (1) all traction, non-traction and pulsed power needs are met; (2) power drawn from the engine for specific mission is minimized; (3) a certain desired battery SOC is guaranteed for offloading power, and (4) the ability to perform load scheduling based on different mission requirements. The proposed approach is validated using simulation of a mission specific profile and is compared with two other popular control strategies. The improvements in power efficiency, desired SOC level and ability to perform optimal load scheduling are demonstrated.

The gradual decline in global oil reserves and the presence of ever so stringent emissions rules around the world have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guaranteeing improved fuel economy and reduced emissions. The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. The challenge in developing a real-time HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle. Reviewed literature indicates some research gaps and hence exploitable study areas for which this thesis intends to address. For example, despite the research advances made, HEV energy management is still lacking in several key areas: optimisation of braking energy regeneration; real-time sub-optimal control of HEV for robustness, charge sustenance and fuel reduction; and real-time vehicle speed control. Consequently, this thesis aims to primarily develop novel real-time near-optimal control strategies for a parallel HEV, with a view to achieving robustness, fuel savings and charge sustenance simultaneously, under various levels of obtainable driving information (no route preview information, partial route preview information). Using a validated HEV dynamic simulation model, the following novel formulations are proposed in this thesis and subsequently evaluated in real time: 1. A simple grouping system useful for classifying standard and real-world driving cycles on the basis of aggressivity and road type. 2. A simple and effective near-optimal heuristic control strategy with no access to route preview information. 3. A dynamic programming-inspired real-time near-optimal control strategy with no access to route preview information. 4. An ECMS (Equivalent Consumption Minimisation Strategy) inspired real-time near-optimal control strategy with no access to route preview information. 5. An ECMS-inspired real-time near-optimal control strategy with partial access to route preview information. 6. A dynamic programming based route-optimal vehicle speed control strategy which accounts for real-time dynamic effects like engine braking, while solving an optimisation problem involving the maximisation of fuel savings with little or no penalty to trip time. 7. A real-time vehicle speed control approach, which is based on smoothing the speed trajectory of the lead vehicle, consequently reducing the acceleration and deceleration events that the intelligent vehicle (follower vehicle) will undergo. This smoothing effect translates into reduced fuel consumption, which tends to increase with increasing traffic preview window. Among other studies performed in this thesis, the fuel savings potential of the proposed near-optimal controllers was investigated in real time over standard driving cycles and real-world driving profiles. Results from these analyses show that, over standard driving cycles, properly formulated near-optimal real-time controllers are able to achieve a fuel savings potential within 0.03% to 3.71% of the global optimal performance, without requiring any access to route preview information. It was also shown that as much as 2.44% extra fuel savings could be achieved over a driving route, through the incorporation of route preview information into a real-time controller. Investigations were also made into the real-time fuel savings that could be realised over a driving route, through vehicle speed control. Results from these analyses show that, compared to an HEV technology which comes at a bigger cost, far higher fuel savings, as much as 45.96%, could be achieved through a simple real-time vehicle speed control approach.

<p>The increasing amounts of vehicle emissions and vehicle energy consumption are major problems for the environment and energy conservation. Hybrid vehicles, which have less emissions and energy consumption, play more and more important roles in energy efficiency and sustainable development.</p> <p> </p> <p>The power management strategies of a parallel-through-the-road hybrid architecture vehicle are different from traditional hybrid electric vehicles since one additional dimension is added. To study power management strategies, a simplified model of the vehicle is developed. Four types of power management strategies have been discovered previously based on the simplified model, including dynamic programming model, equivalent consumption minimization strategy, proportional state-of-charge algorithm, and regression model. A new power management strategy, which is artificial neural network model, is developed. All these five power management strategies are compared, and the artificial neural network model is proven to have the best results among the implementable strategies.</p>

With the escalating number of vehicles on the road, great concerns are drawn to the large amount of fossil fuels they use and the detrimental environmental impacts from their emissions. A lot of research and development have been conducted to explore the alternative energy sources. The fuel cell has been widely considered as one of the most promising solutions in automobile applications due to its high energy density, zero emissions and sustainable fuels it employs. However, the cost and low power density of the fuel cell are the major obstacles for its commercialization. This thesis designs a novel converter topology and proposes the control method applied in the Fuel Cell Hybrid Vehicles (FCHVs) to minimize the fuel cell's cost and optimize the system's efficiency. Unlike the previous work, the converters presented in the thesis greatly reduce the costs of hardware and energy losses during switching. They need only three Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) to smoothly accomplish the energy management in the cold start, acceleration, steady state and braking modes. In the converter design, a boost converter connects the fuel cell to the DC bus because the fuel cell's voltage is usually lower than the rating voltage of the motor. In this way, the fuel cell's size can be reduced. So is the cost. With the same reason, the bidirectional converter connected to the ultracapacitor works at the buck pattern when the power is delivered from the DC bus to the ultracapacitor, and the boost converter is selected when the ultracapacitor provides the peaking power to the load. Therefore, the two switches of the bi-directional converter don't work complementarily but in different modes according to the power flow's direction. Due to the converters' simple structure, the switches' duty cycles are mathematically analyzed and the forward control method is described. The fuel cell is designed to work in its most efficient range producing the average power, while the ultracapacitor provides the peaking power and recaptures the braking power. The simulation results are presented to verify the feasibility of the converter design and control algorithm.