Master of Public Health / Public Health Interdepartmental Program / Manuel Moro / Activities of a state public health veterinarian.

This thesis is set in the context of social science’s interest in the generation of expectations, the news media, and neurotechnologies. It is a qualitative case study that examines the nature and impact of news media reporting of some pioneering research, which used functional magnetic resonance imaging in an attempt to diagnose and communicate with severely brain-injured individuals. Previous news media studies exploring neurotechnologies have been quantitative, or have tended to focus on how or why the news media represents neurotechnologies and/or the impact of the reporting, but rarely all three together. My thesis looks at all three aspects of the news media reporting of my case study. I draw on three sets of empirical data. First, those related to the production of the media - the press releases which reported the research; ten semi-structured interviews with science press officers; and the relevant expert comments posted on the Science Media Centre’s website. Second, 51 newspaper articles reporting the research. Third, five semi-structured interviews with relatives of severely brain-injured patients. I show that the mood of excitement and ‘breakthrough’ present in the press release reporting of this research was closely echoed in the news coverage. This excitement influenced the views and beliefs of only some of the relatives I interviewed. I then examine the nature of hype and by drawing on Haraway’s concept of ‘situated knowledges’ (1988) I argue that individuals view hype differently depending on their profession, industry and/or socio-cultural background. Finally, I show how whilst both the news media and the scholarly literature portrayed this research as ethically contentious, the issues most prominently discussed by scholars and/or journalists do not necessarily equate with relatives’ concerns. My findings aim to contribute to the sociology of expectations, media theory, the sociology of bioethics and the public understanding of science.

This thesis provides an insight into battery modeling in electric vehicles which includes degradation mechanisms as in automotive operation in electric vehicles. As electric vehicles with lithium ion batteries increase in popularity there is an increased need to study and model the capacity losses in such batteries. If there is a good understanding of the phenomena involved and an ability to predict these losses there is also a foundation to take measures to minimize these losses. In this thesis a battery model for lithium ion batteries which includes heat dissipation is used as groundwork. This model is expanded with the addition of capacity losses due to usage as well as storage. By combining this with a simple vehicle model one can use these models to achieve an understanding as to how a battery or pack of several batteries would behave in a specific driving scenario. Much of the focus in the thesis is put into comparing the different factors of degradation to highlight what the major contributors are. The conclusion is drawn that heat is the main cause for degradation for batteries in electric vehicles. This applies for driving usage as well as during storage. As heat is generated when a battery is used, the level of current is also a factor, as well as in which state of charge region the battery is used.

Philosophiae Doctor - PhD / Proton exchange membrane fuel cells (PEMFC) converts chemical energy from the electrochemical reaction of oxygen and hydrogen into electrical while emitting heat, oxygen depleted air (ODA) and water as by-products. The by-products have useful functions in aircrafts, such as heat that can be used for ice prevention, deoxygenated air for fire retardation and drinkable water for use on board. Consequently, the PEMFC is also studied to optimize recovery of the useful products. Despite the progress made, durability and reliability remain key challenges to the fuel cell technology. One of the reasons for this is the limited understanding of PEMFC behaviour in the aeronautic environment. The aim of this thesis was to define a comprehensive non-intrusive diagnostic technique that provides real time diagnostics on the PEMFC State of Health (SoH). The framework of the study involved determining factors that have direct influence on fuel cell life in aeronautic environment through a literature survey, examining the effects of the factors by subjecting the PEMFC to simulated conditions, establishing measurable parameters reflective of the factors and defining the diagnostic tool based on literature review and this thesis finding.

Degradation behavior of commercial lithium-ion pouch cells containing LiCoO2 cathode and graphite anode was investigated for a cycling under continuous overcharge condition. This condition is frequently experienced in electric vehicles in an event of Battery Management System (BMS) failure. Failure of BMS results in an unbalanced module further resulting in overcharging or overdischarging the cells. Commercial cells with 5Ah capacity were continuously cycled at different upper cutoff voltages and 1C-rate to develop a better understanding of the overcharge process. The results show that as the upper cutoff voltage is extended, the cell gains a higher initial capacity. However, the cycle life of the cell diminishes significantly. The extent of overcharge was found to be an important parameter not only for the electrochemical performance but also for cell integrity. Cells overcharged beyond 4.5 V had a significant volume increase and a rapid increase in the capacity fade. The cell starts to swell at this stage and a considerable increase in the temperature and internal resistance of the cells is observed. Thermal imaging of the cell revealed non-uniform temperature distribution and localized degradation sites were identified. Evidence of lithium plating and electrolyte deposits on anode was observed in cells charged beyond 4.4 V, with SEM-EDS verifying their presence. A comparative study of various State of Health (SoH) estimation parameters is presented and the proposed parameter Φ_R based on internal resistance measurement is found to be a good indicator of aggravated degradation in cells.<br>

Operating batteries in parallel is attempted to overcome the problems with conventionally used battery bank, in which batteries are connected in series. The problems and the management with the operation of serial connected batteries are first addressed. The related topics to the parallel configuration are reviewed. Then, the parallel configuration with battery power modules is proposed. The battery power module can be realized with different dc-to-dc converters for different applications. When batteries are charged in parallel, the problem of over-charge can be avoided. With parallel operation, the discharging currents of the batteries are independently controlled but are coordinated to execute a full amount load current. This allows for scheduling the discharging profiles under different operating conditions. As a result, a sophisticated discharging profile can be realized to utilize the available stored energy in batteries. On the other hand, some of the batteries may take rest or be isolated from the system for the detections at a time. This facilitates the estimations of the state of charge (SOC) and the state of health (SOH). Moreover, the completely exhausted or damaged batteries can be isolated from the battery power supply bank without interrupting the system operation. Experiments are carried out on battery power modules with lead-acid batteries incorporating with associated buck-boost converters. The experimental results demonstrate that a more efficient utilization of battery energy can be achieved. On the other hand, a more reasonable management can be done with simple estimation methods of the SOC and the SOH.

This thesis proposes an enhanced ampere-hour counting method based on the depth-of-discharge (DOD) to estimate the state-of-charge (SOC) and state-of-health (SOH) for lead-acid batteries. Not only the losses at different discharging currents, but also the releasable capacity at the exhausted state caused by the larger discharging current are considered and compensated. Furthermore, the SOH is revaluated at the exhausted state by the maximum releasable capacity, consequently leading to more accurate SOC estimation. Through the experiments that emulate practical operations, the experimental results reveal that the maximum error is less than 6 %.

A novel battery power system configured by the battery power modules (BPMs) is proposed. Each BPM consists of a single battery pack or a battery bank equipped with an associated DC/DC converter. The output ports of BPMs can be connected in series for the high voltage applications, or in parallel to cope with a higher power or energy. For a large scale battery power system, a number of BPMs can be arrayed with combination of series and parallel connections to meet the load requirements. These all configurations allow the BPMs be operated individually. Consequently, the discharging currents of the batteries can be independently controlled, but coordinated to provide a full amount of the load current. The performances of BPMs connected in both parallel and series at outputs are analyzed theoretically and discussed from the experimental results. Batteries operating independently do not suffer from charge imbalance, and thus can avoid being over-charged or over-discharged, so that the life cycle can be prolonged. Furthermore, sophisticated discharging profiles such as intermittent currents can be realized to equalize the charges and thus to efficiently utilize the available stored energy in batteries. During the operation period, some of the batteries may take rest or be isolated from the system for the open-circuit measurement, facilitating the estimation of the state-of-charge (SOC) and the evaluation of the state-of-health (SOH). With the benefit of independent operation, the BPMs can be discharged with a scheduled current profile, such as intermittent discharging. The investigation results show that the average current plays the most important role in current discharging. By detecting the battery voltage at the break time, an SOC estimation method based on the dynamically changed open-circuit voltage exhibits an acceptable accuracy in a shorter time with considerations of the previous charging/discharging currents and the depth-of- discharge (DOD). In addition, the coulomb counting method can be enhanced by evaluating the SOH at the exhausted and fully charged states, which can be intended on the independently operated BPMs. Through the experiments that emulate practical operations, the SOC estimation methods are verified on lead-acid batteries and lithium-ion batteries to demonstrate the effectiveness and accuracy.

Environmental and economic reasons have lead automotive companies towards the direction of EVs and HEVs. Stricter emission legislations along with the consumer needs for more cost-efficient and environmental friendly vehicles have increased immensely the amount of hybrid and electric vehicles available in the market. It is essential though for Li-ion batteries, the main propulsion force of EVs and HEVs, to be able to read the battery characteristics in a high accuracy manner, predict life expectancy and behaviour and act accordingly. The following thesis constitutes a concept study of a battery diagnostics method. The method is based on the notion of a pseudo-random binary signal used as the current input and from its voltage response, the impedance is used for the estimation of parameters such as the state of charge and more. The feasibility of the PRBS method at a battery cell has been examined through various tests, both in an experimental manner at the lab but also in a simulation manner. The method is compared for validation against the electrochemical impedance spectroscopy method which is being used as a reference. For both the experimental and the simulation examinations, the PRBS method has been validated and proven to work. No matter the change in the parameters of the system, the method behaves in a similar manner as in the reference EIS method. The level of detail in the research and the performed experiments is what makes the significance of the results of high importance. The method in all ways has been proven to work in the concept study and based on the findings, if implemented on an EV’s or HEV’s electric drive line and the same functionality is observed, be used as a diagnostics method of the battery of the vehicle.

For the broad commercial success of electric vehicles (EVs), it is essential to deeply understand how batteries behave in this challenging application. This thesis has therefore been focused on studying automotive lithium-ion batteries in respect of their performance under EV operation. Particularly, the  need  for  simple  methods  estimating  the  state-of-health  (SOH)  of batteries during EV operation has been addressed in order to ensure safe, reliable, and cost-effective EV operation. Within  the  scope  of  this  thesis,  a  method  has  been  developed  that  can estimate the SOH indicators capacity and internal resistance. The method is solely based on signals that are available on-board during ordinary EV operation  such  as  the  measured  current,  voltage,  temperature,  and  the battery  management  system’s  state-of-charge  estimate.  The  approach  is based on data-driven battery models (support vector machines (SVM) or system  identification)  and  virtual  tests  in  correspondence  to  standard performance  tests  as  established  in  laboratory  testing  for  capacity  and resistance determination. The proposed method has been demonstrated for battery data collected in field tests and has also been verified in laboratory. After a first proof-of-concept of the method idea with battery pack data from a plug-in hybrid electric vehicle (PHEV) field test, the method was improved with the help of a laboratory study where battery electric vehicle (BEV) operation of a battery  cell  was  emulated  under  controlled  conditions  providing  a thorough validation possibility. Precise partial capacity and instantaneous resistance  estimations  could  be  derived  and  an  accurate  diffusion resistance estimation was achieved by including a current history variable in the SVM-based model. The dynamic system identification battery model gave precise total resistance estimates as well. The SOH estimation method was also applied to a data set from emulated hybrid electric vehicle (HEV) operation of a battery cell on board a heavy-duty vehicle, where on-board standard  test  validation  revealed  accurate  dynamic  voltage  estimation performance of the applied model even during high-current situations. In order to exhibit the method’s intended implementation, up-to-date SOH indicators have been estimated from driving data during a one-year time period. / <p>QC 20150914</p>