A Modular Multi-level Converter for Energy Management of Hybrid Energy-Storage Systems in Electric Vehicles

Electric vehicles (EVs) are substantial applications of clean energy. Their effectiveness for mainstream transportation is predicated on the efficient use of stored energy within the vehicles’ power pack. Among rechargeable storage solutions, lithium-ion (Li-ion) battery cells have high energy density making them suitable to supply the EVs’ average power. However, the peak power requirements of the vehicles exert stress on the Li-ion cells due to their low pulsating power capabilities. Ultracapacitors can be used instead as the power-pulsating storage elements given their superior power density. Incorporating the two cell types for energy storage signifies a hybrid configuration that leads to challenging tasks in managing the energy between cells due to varying cell dynamics. Therefore, this study investigated the design of an end-to-end hybrid energy-storage and management system. The limitations of existing power electronics and control schemes were identified based on comparative analysis, both on a cell level and on a system level. Subsequently, an energy system was developed that utilized modular multi-level converters to manage the energy between the different cell types. The formulated control strategy accounted for various power modes and added immense flexibility in charge sharing through diverse switching states. Furthermore, the proposed configuration eliminated the conventional need for a system level drive inverter feeding the EV motor. Electro-mechanical modeling results and physical design merits verified the proposed configuration’s effectiveness in improving EV efficiency

Big Data and Electric Mobility

Nowadays Electric Vehicles are getting more and more important to address modern issues like pollution, economical transportation needs and more efficient and flexible ways of moving. In this thesis we focus on the assessment of an electrification rate of the major urban areas of Tuscany, by simulating the consumption of a real EV on millions of real users trajectories. We propose different usage scenarios, all regarding a different level of sophistication, this to make more reliable evaluations in different environmental conditions.Then, we generate the algorithms used for the simulations, and address the challenges met on the path, such as GPS data sampling and elevation extraction issues. Gli odierni veicoli elettrici stanno assumendo sempre più importanza come risposta a problemi quali l’inquinamento dell’aria, il bisogno di un mezzo di trasporto più economico e modalità di spostamento più efficienti e flessibili. In questa tesi focalizziamo l’attenzione all’individuazione di un tasso di elettrificabilità delle maggiori aree cittadine della Toscana, attraverso una simulazione del consumo di un reale veicolo elettrico su milioni di traiettorie di utenti reali. Proponiamo quindi differenti scenari di uso, tutti riguardanti differenti livelli di sofisticazione, in modo da generare valutazioni più precise al variare di specifiche condizioni.In seguito generiamo gli algoritmi utilizzati per la simulazione, risolvendo tutte le sfide incontrate sul cammino, come il sampling dei dati GPS e i problemi relativi all’estrazione dell’elevazione

Low cost vertical axis wind energy harvesting system using supercapacitors for rural Malaysia

Numerous countries worldwide are conscious about the fact that the past and current trends of energy system are not sustainable and a solution needs to be drawn to protect the world energy from a drastic falling. One of the sources that can replace the current trend is surely wind energy that momentously depends on the availability of the wind resource. For a typical horizontal axis wind turbine to run and generate power, a wind speed of at least 5 m/s is required. Countries like Malaysia have less than 5m/s average wind speed. Another predicament is that these regions face unsteady multi-directional winds making HAWT totally incompatible in such areas. The vertical axis wind turbine on the other hand is appropriate for such regions due to its ability to capture wind energy at any direction. Also, the use of Neodymium magnets for suspension at the bottom surface assist attaining nearly zero friction (Maglev), could be of help improving the output efficiency. Conventional generators now-a-days have been replaced with Permanent Magnet Synchronous Generator (PMSG). Although a number of researches in the area of VAWT and PMSG are carried through separately, few attempts were taken to build a system that work efficiently at low wind speed. Moreover, there is another gap in research for an off-grid standalone energy harvesting device incorporated with low wind Maglev VAWT. This thesis provides a platform for a novel innovative approach towards an off-grid energy harvesting system (EHS) for Maglev VAWT. This EHS basically a Supercapacitor based hybrid battery charging energy harvesting device. Rural areas in countries like Malaysia where grid connection is not always available, this standalone system there can make a difference for small scale electronic devices. In this thesis, a complete simulation analysis is done for all 3 types of PMSG connected to VAWT and result was compared. This novel comparison showed that 5-phase is a better performer both in high and low speed comparing with 3-phase and dual stator. Moreover, although at high speed dual stator provides better power efficiency than 3-phase, at low wind, output power performance in 3-phase surpasses that of dual stator. At low wind, even though 5-phase PMSG shows better performance in low wind speed, 3-Phase PMSG was chosen for low maintenance cost, light weight and less complicated design. With the variation of design parameters under low wind speeds, two configurations were optimized for rural Malaysia in terms of low speed, high power and output torque. First configuration was a 1.5KW 220V 20 Pole AFPMSG adopted to a Maglev based VAWT having radius and height of 1m and 2.6m respectively. The other configuration presents a 200W 12V 16 Pole AFPMSG attached to Maglev VAWT of 14.5cm radius and 60cm of height. Later weight to Power Ratio is applied subsequently and the second configuration has been proved to be more cost-effective. The proposed system was also compared with existing models in rural Malaysia for cost-efficiency. A prototype version of the low cost optimized system is built up in lab for open circuit performance and with satisfactory findings, design is sent for fabrication. Upon arrival, the optimized system is implemented into the energy harvesting circuit and field testing is carried to observe the performance. The energy harvesting circuit shows better efficiency in charging battery in all aspects comparing to direct charging of battery regardless of with or without converter. Sufficient groundwork and results have been laid out in this thesis to deliver the necessary development and framework for further improvements. Based on analysis and results carried out in this thesis, all feasibility studies and information are provided for the next barrier

Generating Synthetic Automotive Data and Detecting Abnormal Vehicle Behavior Using Unsupervised Machine Learning

The amount of data generated, processed, and stored by the modern vehicle is increasing and this is creating the potential to detect abnormal and potentially dangerous situations occurring. The purpose of this thesis is to portray a lack of information in the area of intrusion detection using automotive data and to lay the foundations of research in intrusion detection using unsupervised machine learning. As vehicles continue to become more connected, there is an increased possibility of them being exploitable through a successful cyberattack. An example of a hacked Jeep Cherokee (Miller, Valasek, (2011)) and a remote exploitation strategy using multiple attack vectors (Checkoway et al, (2011)) was the prime exhibition of a situation where the vehicle can be remotely compromised. These examples demonstrate the potential to exploit aspects of the vehicle’s communication and control systems, resulting in expected behavior. This thesis is focused on detecting attacks targeting a vehicle by identifying abnormal vehicle behavior, exhibited through control data. To achieve this, synthetic vehicle data containing detectable abnormalities is generated and used for analysis and detection to help detect cyberattacks. Unsupervised machine learning techniques are used as a way to detect abnormal entries in-vehicle data. the synthetic data is generated based on datasets comparable with those generated during normal vehicle operations, before being used to insert manually insert skewness to generate abnormalities, before using and evaluating various unsupervised learning algorithms

Low cost vertical axis wind energy harvesting system using supercapacitors for rural Malaysia

Numerous countries worldwide are conscious about the fact that the past and current trends of energy system are not sustainable and a solution needs to be drawn to protect the world energy from a drastic falling. One of the sources that can replace the current trend is surely wind energy that momentously depends on the availability of the wind resource. For a typical horizontal axis wind turbine to run and generate power, a wind speed of at least 5 m/s is required. Countries like Malaysia have less than 5m/s average wind speed. Another predicament is that these regions face unsteady multi-directional winds making HAWT totally incompatible in such areas. The vertical axis wind turbine on the other hand is appropriate for such regions due to its ability to capture wind energy at any direction. Also, the use of Neodymium magnets for suspension at the bottom surface assist attaining nearly zero friction (Maglev), could be of help improving the output efficiency. Conventional generators now-a-days have been replaced with Permanent Magnet Synchronous Generator (PMSG). Although a number of researches in the area of VAWT and PMSG are carried through separately, few attempts were taken to build a system that work efficiently at low wind speed. Moreover, there is another gap in research for an off-grid standalone energy harvesting device incorporated with low wind Maglev VAWT. This thesis provides a platform for a novel innovative approach towards an off-grid energy harvesting system (EHS) for Maglev VAWT. This EHS basically a Supercapacitor based hybrid battery charging energy harvesting device. Rural areas in countries like Malaysia where grid connection is not always available, this standalone system there can make a difference for small scale electronic devices. In this thesis, a complete simulation analysis is done for all 3 types of PMSG connected to VAWT and result was compared. This novel comparison showed that 5-phase is a better performer both in high and low speed comparing with 3-phase and dual stator. Moreover, although at high speed dual stator provides better power efficiency than 3-phase, at low wind, output power performance in 3-phase surpasses that of dual stator. At low wind, even though 5-phase PMSG shows better performance in low wind speed, 3-Phase PMSG was chosen for low maintenance cost, light weight and less complicated design. With the variation of design parameters under low wind speeds, two configurations were optimized for rural Malaysia in terms of low speed, high power and output torque. First configuration was a 1.5KW 220V 20 Pole AFPMSG adopted to a Maglev based VAWT having radius and height of 1m and 2.6m respectively. The other configuration presents a 200W 12V 16 Pole AFPMSG attached to Maglev VAWT of 14.5cm radius and 60cm of height. Later weight to Power Ratio is applied subsequently and the second configuration has been proved to be more cost-effective. The proposed system was also compared with existing models in rural Malaysia for cost-efficiency. A prototype version of the low cost optimized system is built up in lab for open circuit performance and with satisfactory findings, design is sent for fabrication. Upon arrival, the optimized system is implemented into the energy harvesting circuit and field testing is carried to observe the performance. The energy harvesting circuit shows better efficiency in charging battery in all aspects comparing to direct charging of battery regardless of with or without converter. Sufficient groundwork and results have been laid out in this thesis to deliver the necessary development and framework for further improvements. Based on analysis and results carried out in this thesis, all feasibility studies and information are provided for the next barrier

Holistic Management of Energy Storage System for Electric Vehicles

While electric vehicles (EVs) have recently gained popularity owing to their economic and environmental benefits, they have not yet dominated conventional combustion-engine vehicles in the market. This is due mainly to their short driving range, high cost and/or quick battery performance degradation. One way to mitigate these shortcomings is to optimize the driving range and the degradation rate with a more efficient battery management system (BMS). This dissertation explores how a more efficient BMS can extend EVs' driving range during their warranty periods. Without changing the battery capacity/size, the driving range and the degradation rate can be optimized by adaptively regulating main operational conditions: battery ambient temperature (T), the amount of transferred battery energy, discharge/charge current (I), and the range of operating voltage (min/max V). To this end, we build a real-time adaptive BMS from a cyber-physical system (CPS) perspective. This adaptive BMS calculates target operation conditions (T, I, min/max V) based on: (a) a battery performance model that captures the effects of operational conditions on the degradation rate and the driving range; (b) a real-time battery power predictor; and (c) a temperature and discharge/charge current scheduler to determine target battery operation conditions that guarantee the warranty period and maximize the driving range. Physical components of the CPS actuate battery control knobs to achieve the target operational conditions scheduled by the batteries cyber components of CPS. There are two subcomponents for each condition (T, I): (d) a battery thermal management system and (e) a battery discharge/charge current management system that consists of algorithms and hardware platforms for each sub-system. This dissertation demonstrates that a more efficient real-time BMS can provide EVs with necessary energy for the specified period of time while slowing down performance degradation. Our proposed BMS adjusts temperature and discharge/charge current in real time, considering battery power requirements and behavior patterns, so as to maximize the battery performance for all battery types and drivers. It offers valuable insight into both current and future energy storage systems, providing more adaptability and practicality for various mobile applications such as unmanned aerial vehicles (UAV) and cellular phones with new types of energy storages.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143920/1/kimsun_1.pd