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

Development of a mathematical model to enable optimal efficiency of the indabuko lithium-ion battery.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Cathode materials are the foremost primary challenge for the vast scale application of lithium-ion batteries in electric vehicles and the stockpiles of power. Foreseeing the properties of cathode materials is one of the central issues in energy storage. In the recent past, density functional theory (DFT) calculations aimed at materials property predictions offered the best trade-off between computational cost and accuracy compared to experiments. However, these calculations are still excessive and costly, limiting the acceleration of new materials discovery. Now the results from different computational materials science codes are made available in databases, which permit quick inquiry and screening of various materials by their properties. Such gigantic materials databases allow a dominant data-driven methodology in materials discovery, which should quicken advancements in the field. This study was aimed at applying machine learning algorithms on existing computations to make precise predictions of physical properties. Thus, the dissertation primary goal was build best ML models that are capable of predicting DFT calculated properties such as, formation energy, energy band-gap and classify materials as stable or unstable based on their thermodynamic stability. It was established that the algorithms only require the chemical formula as input when predicting materials properties. The theoretical aspect of this work describes the current machine learning algorithms and presents "descriptors"-representations of materials in a dataset that plays a significant role in prediction accuracy. Also, the dissertation examined how various descriptors and algorithms influence learning model. The Catboost Regressor was found to be the best algorithm for determining all the properties that were selected in this study. Results indicated that with appropriate descriptors and ML algorithms it is feasible to foresee formation energy with coefficient of determination (R2) of 0.95, mean absolute error (MAE) of 0.11 eV and classify materials into stable and unstable with 86% of accuracy and area under the ROC Curve (AUC) of 89%. Lastly, we build a web application that allow users to predict material properties easily

Advanced data-driven methods for prognostics and life extension of assets using condition monitoring and sensor data.

A considerable number of engineering assets are fast reaching and operating beyond their orignal design lives. This is the case across various industrial sectors, including oil and gas, wind energy, nuclear energy, etc. Another interesting evolution is the on-going advancement in cyber-physical systems (CPS), where assets within an industrial plant are now interconnected. Consequently, conventional ways of progressing engineering assets beyond their original design lives would need to change. This is the fundamental research gap that this PhD sets out to address. Due to the complexity of CPS assets, modelling their failure cannot be simplistically or analytically achieved as was the case with older assets. This research is a completely novel attempt at using advanced analytics techniques to address the core aspects of asset life extension (LE). The obvious challenge in a system with several pieces of disparate equipment under condition monitoring is how to identify those that need attention and prioritise them. To address this gap, a technique which combined machine learning algorithms and practices from reliability-centered maintenance was developed, along with the use of a novel health condition index called the potential failure interval factor (PFIF). The PFIF was shown to be a good indicator of asset health states, thus enabling the categorisation of equipment as “healthy”, “good ” or “soon-to-fail”. LE strategies were then devoted to the vulnerable group labelled “good – monitor” and “soon-to-fail”. Furthermore, a class of artificial intelligence (AI) algorithms known as Bayesian Neural Networks (BNNs) were used in predicting the remaining useful life (RUL) for the vulnerable assets. The novelty in this was the implicit modelling of the aleatoric and epistemic uncertainties in the RUL prediction, thus yielding interpretable predictions that were useful for LE decision-making. An advanced analytics approach to LE decision-making was then proposed, with the novelty of implementing LE as an on-going series of activities, similar to operation and maintenance (O&M). LE strategies would therefore be implemented at the system, sub-system or component level, meshing seamlessly with O&M, albeit with the clear goal of extending the useful life of the overall asset. The research findings buttress the need for a paradigm shift, from conventional ways of implementing LE in the form of a project at the end of design life, to a more systematic approach based on advanced analytics.Shafiee, Mahmood (Associate)PhD in Energy and Powe