published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

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A vehicular battery must consist of a large number of cells to provide the necessary energy and power. Management only at the level of the battery pack causes out-of-investigation cells and lack of cell equalization ability. Therefore, in the smart module concept, cells are first grouped into modules, which are then connected to the battery pack. Each module is an independent unit with a controller to investigate and control cells. Based on this concept, the work in this thesis redistributes tasks among module controllers and a central controller, applies a self-power design to enhance module independence, and selects the newly developed automotive ICs and sensors. Finally, a prototype of the BMS has been developed and successfully applied in a series of HEVs. / Cell equalization is a crucial technique to balance the cells inside a battery pack, with the ability to maximize pack capacity and protect cells from damage. For the bi-directional Cuk equalizing circuit, we propose a SoC based, instead of voltage based, fuzzy controller to intelligently determine the equalizing current, with the aim of reducing equalizing duration, enhancing equalizing efficiency, and protecting cells. The inputs to the controller are specially designed as the difference in SoC, the average SoC, and the total internal resistance. Because of the lack of theoretical analysis on equalizing current in the electrochemistry field, we utilize a fuzzy controller to incorporate the experience and knowledge of experts. Simulations and experiments verify its availability and efficacy. Especially for a LiFePO4 battery, a large SoC difference may lead to only a small difference in voltage and cause the failure of a traditional voltage based equalizer. The SoC based method successfully avoids this problem and obtains good performance in equalizing LiFePO4 cells. / Fast charge is intended to charge a battery as fast as possible, without any damage and with high energy efficiency, thus helping to reduce vehicle out-of-service time and promote the commercialization of EVs. Battery safety and charging efficiency are partially reflected by the increase in temperature during the charging process. Therefore, the aims of this thesis were to accelerate charging speed and reduce the temperature increase. We introduce a model predictive control framework to control the charging process. An RC model and the modified enhanced self-correcting model are employed to predict the future SoC in simulations and experiments respectively. A single-node lumped-parameter thermal model and a neural network trained by real experimental data are also applied respectively. In addition, a genetic algorithm is applied to optimize the charging current under multiple objectives and constraints. Simulation and experimental results strongly demonstrate that the Pareto front of the proposed algorithm dominates that of the popular constant current constant voltage charge method. / State of charge (SoC) is a battery state indicating its residual capacity. It is the fundamental state of the battery and is the basis for other battery operations. However, SoC is not a directly measurable state and has to be obtained by estimation techniques. Aiming to enhance the anti-noise ability of SoC estimation in a real vehicle environment, we propose a SoC estimation framework consisting of an adaptive nonlinear diffusion filter to reduce the noise of current measurement, a self-learning mechanism to remove its zero-drift, an open loop coulomb counting estimator and a model based closed loop filter to estimate SoC, and a data fusion unit to reach the final estimation result. In a simulation study, the closed loop filter is implemented based on an RC model and Hinfinity filter. In experiments and application, we modify the enhanced self-correcting model to model a type of LiFePO4 battery and apply an extended Kalman filter to estimate SoC. The framework has been demonstrated to improve accuracy and anti-noise ability, and achieves the technique upgrading goal recently published by the Chinese government. / The automotive industry has experienced a significant boom in recent years, accelerating the problems of energy shortage and environmental disruption around the world. To solve the two problems, electric vehicles (EVs), including battery electric vehicles (BEV), hybrid electric vehicles (HEV), and fuel-cell electric vehicles (FEV), have been proposed and studied in recent years. Despite the efforts devoted to the development of EVs by both the scientific research and industrial communities, there are still many obstacles hindering the mass commercialization of EVs. Among these obstacles, the battery system, the new energy storage component in EVs, is one of the most important yet most difficult parts of EV design, and the battery management system (BMS) is recognized as the single most important technical issue in the successful commercialization of EVs. / Yan Jingyu. / Adviser: Xu Yangsheng. / Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 166-182). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

The purpose of the study is to analyze the energy use of battery electric vehicles (BEVs), to compare their energy usage with other different vehicle technologies, and ultimately to determine their suitability for recreational use. The possibility of applying such vehicles into South Africa’s game reserves is researched in terms of energy costs and evaluated. Calculations were made based on actual existing routes found in the Kruger National Park, and are presently used by tourists for sightseeing and to access the different camps within the park. Calculations were made on the forces acting on a vehicle driving through the different routes and terrains. These forces were then translated into fuel or energy consumption and subsequently into fuel and energy prices. The entire exercise was performed on alternative vehicle technologies in a hypothetical scenario. The calculations investigated the energy consumption and efficiency of a battery electric vehicle (BEV) and other vehicle technologies such as fuel cell electric vehicle (FCEV), hybrid electric vehicle (HEV), and lastly the internal combustion engine (ICEV) vehicle. It was found that the energy consumption of each vehicle technology revealed similar trends and ranking on most routes. However on certain routes, the energy usage difference amongst the different vehicle technologies became more pronounced. This can be attributed to the continuous demand of energy by the vehicle to maintain forward motion. It was found that in general, irrespective of the route profile, the route surface or its total distance, the highest energy efficiency is achieved by the battery electric vehicle (BEV), followed by the fuel cell electric vehicle (FCEV) and then by the combined hybrid electric vehicle (HEV) and lastly by the internal combustion engine (ICEV) vehicle. / Electrical Engineering / M. Tech. (Electrical Engineering)

The world faces the unprecedented challenge of the need change to a new energy era. The introduction of distributed renewable energy and storage together with transportation electrification and deployment of electric and hybrid vehicles, allows traditional consumers to not only consume, but also to produce, or store energy. The active participation of these so called "prosumers", and their interactions may have a significant impact on the operations of the emerging smart grid. However, how these capabilities should be integrated with the overall system operation is unclear. Intelligent energy management systems give users the insight they need to make informed decisions about energy consumption. Properly implemented, intelligent energy management systems can help cut energy use, spending, and emissions. This thesis aims to develop a consumer point of view, user-friendly, intelligent energy management system that enables vehicle drivers to plan their trips, manage their battery pack and under specific circumstances, inject electricity from their plug-in vehicles to power the grid, contributing to frequency regulation.