A Li-ion battery charge protocol with optimal aging-quality of service trade-off

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging. In this paper we propose a charge protocol that, using an accurate estimate of the charging time of a battery and the statistical properties of the charge/discharge patterns, yields an optimal trade-off between aging and quality of service. The latter is measured in terms of the distance of the actual SOC from 100% at the end of the charge phase. Results show that the present method improves significantly over other similar protocols proposed in the literature

A Li-ion battery charge protocol with optimal aging-quality of service trade-off

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging. In this paper we propose a charge protocol that, using an accurate estimate of the charging time of a battery and the statistical properties of the charge/discharge patterns, yields an optimal trade-off between aging and quality of service. The latter is measured in terms of the distance of the actual SOC from 100% at the end of the charge phase. Results show that the present method improves significantly over other similar protocols proposed in the literature

Electric Vehicles Plug-In Duration Forecasting Using Machine Learning for Battery Optimization

The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware charging protocols. Since one of the main factors affecting battery aging is its average state of charge (SOC), these protocols try to minimize the standby time, i.e., the time interval between the end of the actual charge and the moment when the EV is unplugged from the charging station. Doing so while still ensuring that the EV is fully charged when needed (in order to achieve a satisfying user experience) requires a “just-in-time” charging protocol, which completes exactly at the plug-out time. This type of protocol can only be achieved if an estimate of the expected plug-in duration is available. While many previous works have stressed the importance of having this estimate, they have either used straightforward forecasting methods, or assumed that the plug-in duration was directly indicated by the user, which could lead to sub-optimal results. In this paper, we evaluate the effectiveness of a more advanced forecasting based on machine learning (ML). With experiments on a public dataset containing data from domestic EV charge points, we show that a simple tree-based ML model, trained on each charge station based on its users’ behaviour, can reduce the forecasting error by up to 4× compared to the simple predictors used in previous works. This, in turn, leads to an improvement of up to 50% in a combined aging-quality of service metric

Wearable, low-power CMOS ISFETs and compensation circuits for on-body sweat analysis

Complementary metal-oxide-semiconductor (CMOS) technology has been a key driver behind the trend of reduced power consumption and increased integration of electronics in consumer devices and sensors. In the late 1990s, the integration of ion-sensitive field-effect transistors (ISFETs) into unmodified CMOS helped to create advancements in lab-on-chip technology through highly parallelised and low-cost designs. Using CMOS techniques to reduce power and size in chemical sensing applications has already aided the realisation of portable, battery-powered analysis platforms, however the possibility of integrating these sensors into wearable devices has until recently remained unexplored. This thesis investigates the use of CMOS ISFETs as wearable electrochemical sensors, specifically for on-body sweat analysis. The investigation begins by evaluating the ISFET sensor for wearable applications, identifying the key advantages and challenges that arise in this pursuit. A key requirement for wearable devices is a low power consumption, to enable a suitable operational life and small form factor. From this perspective, ISFETs are investigated for low power operation, to determine the limitations when trying to push down the consumption of individual sensors. Batteryless ISFET operation is explored through the design and implementation of a 0.35 \si{\micro\metre} CMOS ISFET sensing array, operating in weak-inversion and consuming 6 \si{\micro\watt}. Using this application-specific integrated circuit (ASIC), the first ISFET array powered by body heat is demonstrated and the feasibility of using near-field communication (NFC) for wireless powering and data transfer is shown. The thesis also presents circuits and systems for combatting three key non-ideal effects experienced by CMOS ISFETs, namely temperature variation, threshold voltage offset and drift. An improvement in temperature sensitivity by a factor of three compared to an uncompensated design is shown through measured results, while adding less than 70 \si{\nano\watt} to the design. A method of automatically biasing the sensors is presented and an approach to using spatial separation of sensors in arrays in applications with flowing fluids is proposed for distinguishing between signal and sensor drift. A wearable device using the ISFET-based system is designed and tested with both artificial and natural sweat, identifying the remaining challenges that exist with both the sensors themselves and accompanying components such as microfluidics and reference electrode. A new ASIC is designed based on the discoveries of this work and aimed at detecting multiple analytes on a single chip. %Removed In the latter half of the thesis, Finally, the future directions of wearable electrochemical sensors is discussed with a look towards embedded machine learning to aid the interpretation of complex fluid with time-domain sensor arrays. The contributions of this thesis aim to form a foundation for the use of ISFETs in wearable devices to enable non-invasive physiological monitoring.Open Acces

Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

SUSTAINABLE ENERGY HARVESTING TECHNOLOGIES – PAST, PRESENT AND FUTURE

Chapter 8: Energy Harvesting Technologies: Thick-Film Piezoelectric Microgenerato

Electronic system modelling of UT pulser-receiver and the electron beam welding power source

This thesis was submitted for the degree of Doctor of Engineering and awarded by Brunel University.Continuous improvements to industrial equipment used in essential industrial applications are a key for the commercial success to the equipment manufacturers. Industrial applications always demand optimum performance and reliability and almost all equipment used in industrial applications is complex and are very expensive to replace. Often modifications to hardware and retrofitting additional hardware are encouraged by most equipment manufacturers and operators. The complexity of these systems however, makes assessment of modifications and design change difficult. This research implemented system modelling techniques to overcome this issue, by developing virtual test platforms of two distinctive industrial systems for enhancement assessment. The two distinctive systems were the electronic equipment called pulser-receiver used in ultrasonic non-destructive testing of safety critical oil & gas pipelines and a high voltage power supply used in high energy electron beam welding. Optimisation with emphasis on portability of the pulser-receiver and rapid weld recovery after a flashover fault condition in the electron beam welding application required assessment before design changes were made to hardware. SPICE based simulators LTSpice and PSpice were used to model and simulate the pulser-receiver and the welding power supply respectively. All the models were evaluated appropriately against theoretical data and published datasheets. However, validation of low level component models developed in the research against measurement data at a component level suffered due to system complexity and resource constraints. Close mapping of simulation results to measurement data at a system level were obtained. The research helped build up a wealth of knowledge in the development of circuit simulation models that can be analysed in the time domain with no non-convergent issues. Simulation settings were relaxed without compromising accuracy of model performance.The Engineering and Physical Sciences Research Board (EPSRC) and TWI Ltd

Index to 1986 NASA Tech Briefs, volume 11, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1986 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Graphene inspired sensing devices

Graphene’s exciting characteristics such as high mechanical strength, tuneable electrical prop- erties, high thermal conductivity, elasticity, large surface-to-volume ratio, make it unique and attractive for a plethora of applications including gas and liquid sensing. Adsorption, the phys- ical bonding of molecules on solid surfaces, has huge impact on the electronic properties of graphene. We use this to develop gas sensing devices with faster response time by suspending graphene over large area (cm^2) on silicon nanowire arrays (SiNWAs). These are fabricated by two-step metal-assisted chemical etching (MACE) and using a home-developed polymer-assisted graphene transfer (PAGT) process. The advantage of suspending graphene is the removal of diffusion-limited access to the adsorption sites at the interface between graphene and its support. By modifying the Langmuir adsorption model and fitting the experimental response curves, we find faster response times for both ammonia and acetone vapours. The use of suspended graphene improved the overall response, based on speed and amplitude of response, by up to 750% on average. This device could find applications in biomedical breath analysis for diseases such lung cancer, asthma, kidney failure and more. Taking advantage of the mechanical strength of graphene and using the developed PAGT process, we transfer it on commercial (CMOS) Ion-Sensitive Field-Effect Transistor (ISFET) arrays. The deposition of graphene on the top sensing layer reduces drift that results from the surface modification during exposure to electrolyte while improving the overall performance by up to about 10^13 % and indicates that the ISFET can operate with metallic sensing membrane and not only with insulating materials as confirmed by depositing Au on the gate surface. Post- processing of the ISFET top surface by reactive ion plasma etching, proved that the physical location of trapped charge lies within the device structure. The process improved its overall performance by about 105 %. The post-processing of the ISFET could be applied for sensor performance in any of its applications including pH sensing for DNA sequencing and glucose monitoring.Open Acces