Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% regardless of starting SOC. Recommendations for modification of the fuzzy logic controller are made to produce additional fuel economy and charge sustaining benefits from the parallel hybrid vehicle model

Power Management Methodologies for Fuel Cell-Battery Hybrid Vehicles

The implementation of fuel cell vehicles requires a supervisory control strategy that manages the power distribution between the fuel cell and the energy storage device. Some of the current problems with power management strategies are: fuel efficiency optimization methods require prior knowledge of the driving cycle before they can be implemented, the impact on the fuel cell and battery life cycle are not considered and finally, there are no standardized measures to evaluate the performance of different control methods. In addition to that, the performances of different control methods for power management have not been directly compared using the same mathematical models. The proposed work will present a different optimization approach that uses fuel mass flow rate instead of fuel mass consumption as the cost function and thus, it can be done instantaneously and does not require knowledge of the driving cycle ahead of time

A study in the use of fuzzy logic in the management of an automotive heat engine / electric hybrid vehicle powertrain

This thesis addresses the problem of the instant-by-instant control of the powertrain of a hybrid heat engine/electric vehicle. In the absence of a prototype vehicle on which the work could be carried out the work has taken the form of computer simulation experiments. In order to develop the powertrain control strategies, a computer model of a conceptual hybrid vehicle is then developed, containing components from real, production and prototype vehicles. The use of this component based modelling approach allows the models to be validated by comparing their predictions with the performance of the real vehicles in which the components are used. The previous work conducted in the field of hybrid vehicle powertrain control is then reviewed. It is found that fuzzy logic could potentially provide a means of controlling the hybrid powertrain in a realistic manner, in which some of the disadvantages of previous hybrid powertrain control strategies could be overcome. The results of initial simulation experiments are then reported, finding that whilst the basic method appears to have the potential to successfully control the powertrain, there is a need for an adaptive fuzzy powertrain controller. A review is then presented of previous work conducted in the field of adaptive fuzzy control, finding that none of the reported adaptive fuzzy control methods are capable of being easily applied in the case of the hybrid powertrain. An adaptive fuzzy controller is then developed, whose rule modification strategy is specifically designed to work in the hybrid powertrain control problem. This initial adaptive powertrain controller is then modified to improve its ability to control the overall performance of a hybrid vehicle, whilst maintaining vehicle driveability. It is found that this controller is able to adapt to the different driving styles of individual vehicle users within the space of a few simulated urban journeys. Experiments are then performed in which improvements in the overall efficiency of the vehicle powertrain are investigated. It is found that significant improvements in the operation of the powertrain are impossible, due to some of the features of the vehicle model and constraints placed upon the control strategy. Conclusions are then drawn, for the work done in the field of hybrid vehicle powertrain control and, also, for the work done in adaptive methods of fuzzy control. The most significant contribution in the field of hybrid powertrain control is the development of a controller that can adapt to the habits of different users. The most significant contribution in the field of fuzzy control is the form of the basic hybrid powertrain controller and the use of small fuzzy controllers in the powertrain controller adaptation strategy

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously