High Performance Cooling of Traction Brushless Machines

The work presented in this thesis covers several aspects of traction electric drive system design. Particular attention is given to the traction electrical machine with focus on the cooling solution, thermal modelling and testing. A 60 kW peak power traction machine is designed to achieve high power density and high efficiency thanks to direct oil cooling. The machine selected has a tooth coil winding, also defined as non-overlapping fractional slot concentrated winding. This winding concept is state of the art for many applications with high volumes and powers below 10 kW. Also, these have been proven successful in high power applications such as wind power generators. In this thesis, it is shown that this technology is promising also for traction machines and, with some suggested design solutions, can present certain unique advantages when it comes to manufacturing and cooling.The traction machine in this work is designed for a small two-seater electric vehicle but could as well be used in a parallel hybrid. The proposed solution has the advantage of having a simple winding design and of integrating the cooling within the stator slot and core. A prototype of the machine has been built and tested, showing that the machine can operate with current densities of up to 35 A/mm^2 for 30 seconds and 25 A/mm^2 continuously. This results in a net power density of the built prototype of 24 kW/l and a gross power density of 8 kW/l with a peak efficiency above 94%. It is shown that a version of the same design optimized for mass manufacturing has the potential of having a gross power density of 15.5 kW/l which would be comparable with the best in class traction machines found on the automotive industry. The cooling solution proposed is resulting in significantly lower winding temperature and an efficiency gain between 1.5% and 3.5% points, depending on the drivecycle, compared to an external jacket cooling, which is a common solution for traction motors

High Performance Cooling Traction Brushless Machine Design for Mass Production

In the last few years electric vehicles (EVs) are coming on the market. The shift from niche market to main stream is challenging. It took many years to reach the current internal combustion engine quality in the manufacturing process. However, a much faster process is needed for the electric drive-train if sustainability goals about CO2 emissions are to be met. It is extremely important to think about the manufacturing process during the design of a piece of hardware if this is meant for mass production. In a few years from now, traction electric motors for EVs will be produced with the rate of millions per year and ensuring a simple, effective and reliable manufacturing process is key in the success of the electric vehicle industry. This thesis presents an innovative brushless machine design meant for mass production. The machine is designed to achieve high power density and high reliability thanks to a novel cooling concept. The machine selected has a tooth coil winding, also defined as non-overlapping fractional slot concentrated winding. This winding concept is state of the art for many applications with high volumes and powers below 10 kW. Also, these have been proven successful in high power applications such as wind power generators. In this thesis the ambition is to show that this technology is promising as well for traction machines and it presents certain unique advantages when it comes to manufacturing and cooling. The traction machine in this work is designed for a small two-seater electric vehicle but could as well be used in a parallel hybrid

Electromagnetic and Calorimetric Validation of a Direct Oil Cooled Tooth Coil Winding PM Machine for Traction Application

Tooth coil winding machines offer a low cost manufacturing process, high efficiency and high power density, making these attractive for traction applications. Using direct oil cooling in combination with tooth coil windings is an effective way of reaching higher power densities compared to an external cooling jacket. In this paper, the validation of the electromagnetic design for an automotive 600 V, 50 kW tooth coil winding traction machine is presented. The design process is a combination of an analytical sizing process and FEA optimization. It is shown that removing iron in the stator yoke for cooling channels does not affect electromagnetic performance significantly. In a previous publication, the machine is shown to be thermally capable of 25 A/mm2 (105 Nm) continuously, and 35 A/mm2 (140 Nm) during a 10 s peak with 6 l/min oil cooling. In this paper, inductance, torque and back EMF are measured and compared with FEA results showing very good agreement with the numerical design. Furthermore, the efficiency of the machine is validated by direct loss measurements, using a custom built calorimetric set-up in six operating points with an agreement within 0.9 units of percent between FEA and measured results

Data Driven Patient-Specialized Neural Networks for Blood Glucose Prediction

Diabetes is an autoimmune disease characterized by glucose levels dysfunctions. It involves continuous monitoring combined with insulin treatment. Nowadays, continuous glucose monitoring systems (CGMs) have led to a greater availability of data. These can be effectively used by machine learning techniques to infer future values of the glycaemic concentration, allowing the early prevention of dangerous states and a better optimisation of the diabetic treatment. In this work, we investigate a patient-specialized prediction model. Thus, we designed a specialized solution based on Long Short-Term Memory (LSTM) neural network. Our solution was experimentally compared with two literature approaches, respectively based on Feed-Forward (FNN) and Recurrent (RNN) neural networks. The experimental results have highlighted that our LSTM solution obtained good performance both for short- and long-term glucose level inference (60 min.), overcoming the other methods both in terms of correlation between measured and predicted glucose signal and in terms of clinical outcome

A Participatory Design Approach for Energy-Aware Mobile App for Smart Home Monitoring

It is generally recognized that our behaviours affect the environment. However, it is difficult to correlate behaviour of an individual person to large-scale problems. This is usually due to insufficient ergonomy of available tools. The main cause is that most of user-awareness tools available are technology-centered instead of user-centered. In this paper, we present a participatory design approach we followed to design and develop an energy-aware mobile application for user-awareness on energy consumption for Smart Home monitoring. To engage end-users from the early design stages, we conduct two on-line surveys and a focus group involving about 630 people. Results allowed on identifying functional requirements and guidelines for mobile app design. The purpose of this research is to increase user-awareness on energy consumption using tools and methods required by users themselves. Furthermore in this paper, we present the technological choices that drove our implementation of an energy-aware application based on prosumers’ requirements

Analytical Conduction Loss Calculation of a MOSFET Three-Phase Inverter Accounting for the Reverse Conduction and the Blanking Time

The reverse conduction capability of MOSFETs is beneficial for the efficiency of a three-phase inverter. In this paper analytical expressions in closed form are presented which allow to quickly evaluate the conduction losses, considering the effect of the reverse conduction and blanking time for both sinusoidal PWM operation with and without third harmonic injection. The losses of a three-phase SiC MOSFET inverter suitable for traction applications are estimated with the proposed method and show good agreement of about 98.5 % with measurements, performed with a calorimetric setup

Comparative analysis of neural networks techniques to forecast Global Horizontal Irradiance

Due to the continuous increasing importance of renewable energy sources as an alternative to fossil fuels, to contrast air pollution and global warming, the prediction of Global Horizontal Irradiation (GHI), one of the main parameters determining solar energy production of photovoltaic systems, represents an attractive topic nowadays. Solar irradiance is determined by deterministic factors (i.e. the position of the sun) and stochastic factors (i.e. the presence of clouds). Since the stochastic element is difficult to model, this problem can benefit from machine learning techniques, like artificial neural networks. This work proposes a methodology to forecast GHI in short- (i.e. from 15 min to 60 min) and mid-term (i.e. from 60 to 120 min) time horizons. For this purpose, we designed, optimised and compared four neural network architectures for time-series forecasting, respectively based on: i) Non-Linear Autoregressive, ii) Feed-Forward, iii) Long Short-Term Memory and iv) Echo State Network. The original data-set, consisting of GHI values sampled every 15min, has been pre-processed by applying different filtering techniques. Our results analysis compares the performance of the proposed neural networks identifying the best in terms of error rate and forecast horizon. This analysis highlights that the clear-sky index results the preferred filtering technique by giving greatly improvements in data-set pre-processing, and Echo State Network gives best accuracy results

Forecasting short-term solar radiation for photovoltaic energy predictions

In the world, energy demand continues to grow incessantly. At the same time, there is a growing need to reduce CO2 emissions, greenhouse effects and pollution in our cities. A viable solution consists in producing energy by exploiting renewable sources, such as solar energy. However, for the efficient use of this energy, accurate estimation methods are needed. Indeed, applications like Demand/Response require prediction tools to estimate the generation profiles of renewable energy sources. This paper presents an innovative methodology for short-term (e.g. 15 minutes) forecasting of Global Horizontal Solar Irradiance (GHI). The proposed methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of solar radiation samples collected for four years by a real weather station. Then GHI forecast, the output of the neural network, is given as input to our Photovoltaic simulator to predict energy production in short-term time periods. Finally, experimental results for both GHI forecast and Photovoltaic energy prediction are presented and discussed

A Serious Game Approach for the Electro-Mobility Sector

Serious Games (SGs) represent a new approach to improve learning processes more effectively and economically than traditional methods. This paper aims to present a SG approach for the electro-mobility context, in order to encourage the use of electric light vehicles. The design of the SG is based on the typical elements of the classic "game" with a real gameplay with different purposes. In this work, the proposed SG aims to raise awareness on environmental issues caused by mobility and actively involve users, on improving livability in the city and on real savings using alternative means to traditional vehicles. The objective of the designed tool is to propose elements of fun and entertainment for tourists or users of electric vehicles in the cities, while giving useful information about the benefits of using such vehicles, discovering touristic and interesting places in the city to discover. In this way, the user is stimulated to explore the artistic and historical aspects of the city through an effective learning process: he/she is encouraged to search the origins and the peculiarities of the monuments.Comment: This paper has been presented at 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC

A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models