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This thesis provides an in-depth exploration of the state of the art in hybrid electric vehicle energy management. A broad literature review was conducted which revealed that most research has focused on strategies that can perform real-time optimisation, however, the real-world implementation of these systems is generally neglected. For vehicle applications, the control methods must be computationally efficient due to the limited processing power and memory available. In light of these findings, a control system optimised for real-world implementation on a hybrid electric bus was investigated. Detailed component models were generated using a combination of first principles and system identification. A novel artificial neural network-based fuel consumption model was created, enabling highly accurate energy consumption estimations. Using real-world test data, individual component models were validated and later incorporated into a complete model of the vehicle. A fuzzy system that controlled the electric motor torque based on the engine and drivetrain efficiency was developed and optimised using the vehicle model and a genetic algorithm. The optimised fuzzy system demonstrated an energy consumption reduction of approximately 10 % relative to a traditional deterministic rule-based strategy. Real-world tests were conducted that demonstrated a real-world energy consumption reduction of up to 20 % relative to the existing strategy present on the target vehicle. These results demonstrate that the proposed system is suitable for real-world implementation while also achieving improved performance relative to conventional methods.

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.

This dissertation examines the benefit of energy optimization in the operation of a vehicle system at an individual vehicle level and the fleet level. For energy optimization in an individual vehicle, a hybridized Class 6 Pickup and Delivery truck with a Range Extended Electric Vehicle configuration is considered. The truck's components were chosen for minimal energy consumption while meeting all the performance requirements of a conventional, diesel-powered vehicle of that class and application. Dynamic Programming is used to determine the best possible energy consumption performance over the course of a working day for the hybrid truck. Energy consumption is then determined using a causal energy management controller on a forward simulator that is compatible with implementation in real-time, where this dissertation introduces the use of a distance-based driver that accurately matches the distance traveled by the vehicle from every start-to-stop in the drive cycle even if the performance constraints of the components prevent the exact matching of the drive cycle speed. The energy consumption results with the forward simulator demonstrate that with increasing levels of information of the expected duty cycle of the day, the onboard energy management can be easily adapted to obtain better fuel consumption performance. For energy optimization in a vehicle fleet, a delivery vehicle fleet is considered that consists of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs) and conventional Internal Combustion Engine Vehicles (ICEVs) operating over the same service area, from a shared depot. This dissertation develops a methodology for route optimization of such a heterogeneous delivery vehicle fleet while taking into account information related to static parameters of the service area (such as topography, payload and driving distance) and dynamic driving conditions (such as traffic incidents and traffic lights). The benefit of route optimization of the fleet for energy consumption and time is then demonstrated for multiple sets of customer locations and depot locations on a real-world road map.

The performance of a powertrain is dependent on how its controller parameters are tuned for the given duty cycle. In real world, the duty cycle is hard to predict, however, if the historical performance is evaluated and used to fine tune the controller parameters, the performance of the powertrain can be improved. This thesis presents a retrospective information-based powertrain performance improvement. The performance is evaluated using the fuel economy and charge sustaining operation of an electrified powertrain. A Retrospective Cost Adaptive Controller (RCAC) has been designed for parallel HEV, which improves it performance based on the former performance. The simulation results demonstrate that the controller parameters are re-calibrated to show improved performance for standard test driving cycles as well as cycles with different driving styles. A dynamic programming (DP) solution is also given as a benchmark to evaluate RCAC results.

One of the main goals of hybrid electric vehicle technology is to improve the energy efficiency. In industry and most of academic research, the powertrain control is designed and evaluated under standard driving cycles. However, the situations that a vehicle may encounter in the real world could be quite different from the standard cycles. Studies show that the human drivers have a great influence on the vehicle energy consumptions and emissions. The actual operating conditions that a vehicle faces are not only dependent on the roads and traffic, but also dependent on the drivers. A standard driving cycle can only represent the typical and averaged driving style under the typical driving scenarios, therefore the control strategies designed based on a standard driving cycle may not perform well for all different driving styles. This motivates the idea to design optimally-personalized hybrid electric vehicle control methods that can be adaptive to individual human driving styles and their driving routes. Human-subject experiments are conducted on a driving simulator to study the driving behaviors. A stochastic driver pedal model that can learn individual driver’s driving style is developed first. Then a theoretic investigation on worst-case relative cost optimal control problems, which is closely related to vehicle powertrain optimal control under real-world uncertain driving scenarios, is presented. A two-level control structure for plug-in hybrid electric vehicles is proposed, where the parameters in the lower-level controller can be on-line adjusted via optimization using historical driving data. The methods to optimize these parameters are designed for fixed-route driving first, and then extended to multi-routes driving using the idea similar to the worst-case relative cost optimal control. The performances of the two proposed methods are shown through simulations using human driving data and stochastic driver model data respectively. The energy consumption results in both situations are close to the posteriori optimal result and outperform other existing methods, which show the effectiveness of applying optimally-personalized energy management strategy on hybrid electric vehicles. Finally, a route-based global energy-optimal speed planning method is also proposed. This off-line method provides a useful tool to evaluate the potential of other speed planning methods, for either eco-driving guidance applications or future automated vehicle controls. The contributions of this dissertation include 1) a novel stochastic driver pedal behavior model which can learn independent drivers’ driving styles is created, 2) a new worst-case relative cost optimal control method is proposed, 3) a real-time implementable stochastic optimal energy management strategy for hybrid electric vehicles running on fixed routes is designed using the statistics of history driving data, 4) the fix-route strategy is extended to the multi-route situation, and 5) an off-line global energy-optimal speed planning solution for road vehicles on a given route is presented.

In today's world, automotive manufacturers face the difficult challenge of building vehicles that are capable of meeting the increasingly stringent fuel economy and emissions standards, while also maintaining the performance and drive quality that consumers have come to expect. The automotive industry's response to this has been to make increasingly advanced vehicles that require more complex control systems, often resulting in longer development times and higher costs. One way to help reduce the development time and cost associated with these advanced vehicles is to use a model-based design approach. This approach allows engineers to design more of the vehicle's control system in a virtual environment, before hardware is available to test the control software. While model-based design techniques have helped reduce the amount of development time and cost that is needed to design the control system for a vehicle, these model-based techniques may not fully account for a vehicle's drive quality characteristics. Many of the energy management optimal control algorithms for hybrid vehicles designed in virtual environments today are capable of achieving high fuel economy numbers, but may result in poor drive quality characteristics when implemented on a vehicle. Therefore, a new methodology is needed to account for a vehicle's drive quality during the initial stages of a vehicle's control development.The research presented here describes a new methodology where drive quality metrics are added to the optimal control algorithm's cost function, in order to allow the algorithm to find a good balance between fuel economy and drive quality. Although some research has been previously published in this area, the majority of research does not specifically link the criteria used to improve drive quality to the physical behavior of the vehicle. Other research solves the optimal energy management problem to minimize fuel consumption, but then filters the results to prevent drive quality problems. This filtering can potentially result in a non-optimal final solution. The research presented in this dissertation formally links the drive quality behavior of the vehicle to the criteria used in the formulation of the optimal energy management problem. This allows the final solution to the optimal energy management problem to be directly applicable to the vehicle, without the need to filter the results.

Abstract: Data collection is difficult in remote locations due to limited access and scarce resources. To power environmental monitoring equipment, and allow it to operate independently of maintenance and infrastructure, energy can be harvested from the environment; however, this makes the system dependent upon its environment and requires energy management to maintain performance. The objective of energy management in environmental monitoring is to produce the highest quality data set possible. These devices require simple, robust control, so a fuzzy inference system is used to produce a controller that maps device states to actions. To ensure that the fuzzy controller selects the best actions for each state, it is tuned by expert knowledge and genetic optimization. The simulation results from the Arctic and Boreal regions both show that these controllers allow energy consumption to be matched to the local energy profile, which improves performance over operating at a fixed level of energy consumption. By reducing the rate of data capture when energy is scarce, the monitor prevents failure and conserves energy. When energy is plentiful, the rate of data capture was increased to acquire high quality data. Managing energy and data storage allows the control system to delay energy-intensive operations, like wireless transmission, until environmental conditions became favourable. By combining energy management with suitable storage technologies, the vulnerability of the monitoring system is reduced to the point where storage can compensate for environmental energy scarcity and a suitable level of performance can be guaranteed for a deployment period of a few years.

This thesis presents the optimal control problem of energy management in a plug-in hybrid electric vehicle. Review of the literature suggests the need for a methodology which follows a blended strategy unlike the traditional charge depleting - charge sustaining (CD-CS) strategy for state of charge of the battery. Many present blended strategies require a-priori knowledge of the driving mission which is obtained by prediction. The performance of these strategies again depends on the prediction algorithms and often end up being sub-optimal in implementation. There is a need for an energy management strategy that provides near optimal results with minimal information about the driving mission. This thesis proposes one such controller. Knowledge of the optimal trajectories under various driving conditions is obtained by implementing a Pontryagin's Minimum Principle (PMP) based energy management scheme. With this knowledge, a practical implementable controller is proposed which performs with near optimal results under different driving missions. A comparison of the optimal PMP solution, the practical controller solution and the traditional CD-CS solution is done to conclude this work.