Design and implementation of machine learning techniques for modeling and managing battery energy storage systems

The fast technological evolution and industrialization that have interested the humankind since the fifties has caused a progressive and exponential increase of CO2 emissions and Earth temperature. Therefore, the research community and the political authorities have recognized the need of a deep technological revolution in both the transportation and the energy distribution systems to hinder climate changes. Thus, pure and hybrid electric powertrains, smart grids, and microgrids are key technologies for achieving the expected goals. Nevertheless, the development of the above mentioned technologies require very effective and performing Battery Energy Storage Systems (BESSs), and even more effective Battery Management Systems (BMSs). Considering the above background, this Ph.D. thesis has focused on the development of an innovative and advanced BMS that involves the use of machine learning techniques for improving the BESS effectiveness and efficiency. Great attention has been paid to the State of Charge (SoC) estimation problem, aiming at investigating solutions for achieving more accurate and reliable estimations. To this aim, the main contribution has concerned the development of accurate and flexible models of electrochemical cells. Three main modeling requirements have been pursued for ensuring accurate SoC estimations: insight on the cell physics, nonlinear approximation capability, and flexible system identification procedures. Thus, the research activity has aimed at fulfilling these requirements by developing and investigating three different modeling approaches, namely black, white, and gray box techniques. Extreme Learning Machines, Radial Basis Function Neural Networks, and Wavelet Neural Networks were considered among the black box models, but none of them were able to achieve satisfactory SoC estimation performances. The white box Equivalent Circuit Models (ECMs) have achieved better results, proving the benefit that the insight on the cell physics provides to the SoC estimation task. Nevertheless, it has appeared clear that the linearity of ECMs has reduced their effectiveness in the SoC task. Thus, the gray box Neural Networks Ensemble (NNE) and the white box Equivalent Neural Networks Circuit (ENNC) models have been developed aiming at exploiting the neural networks theory in order to achieve accurate models, ensuring at the same time very flexible system identification procedures together with nonlinear approximation capabilities. The performances of NNE and ENNC have been compelling. In particular, the white box ENNC has reached the most effective performances, achieving accurate SoC estimations, together with a simple architecture and a flexible system identification procedure. The outcome of this thesis makes it possible the development of an interesting scenario in which a suitable cloud framework provides remote assistance to several BMSs in order to adapt the managing algorithms to the aging of BESSs, even considering different and distinct applications

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

Water stress and in particular drought are some of the most significant factors affecting plant growth, food production, and thus food security. Furthermore, the possibility to predict and shape irrigation on real plant demands is priceless. The objective of this study is to characterize, classify, and forecast water stress in tomato plants by means of in vivo real time data obtained through a novel sensor, named bioristor, and of different artificial intelligence models. First of all, we have applied classification models, namely Decision Trees and Random Forest, to try to distinguish four different stress statuses of tomato plants. Then, we have predicted, through the help of recurrent neural networks, the future status of a plant when considering both a binary (water stressed and not water stressed) and a four-status scenario. The obtained results are very good in terms of accuracy, precision, recall, F-measure, and of the resulting confusion matrices, and they suggest that the considered novel data and features coming from the bioristor, together with the used machine and deep learning models, can be successfully applied to real-world on-the-field smart irrigation scenarios in the future

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

Model-free non-invasive health assessment for battery energy storage assets

Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health.Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health

Paper Session I-B - Reverse Engineering of Biological Gravity-Sensing Organs: Neurocomputational and Biomedical Implications

As humans began to project themselves into the environment of interplanetary space during the early 1960s, it was clear that the opening of this new frontier would require a comprehensive understanding of the effects of near-weightlessness (microgravity) on biological organisms. After all, life on planet Earth has evolved under the stable and pervasive influence of gravity. In terrestrial ecosystems, a force of one gravitational unit represents a continuous epigenetic agent that affects living systems at levels ranging from the morphogenetic to the behavioral2. However, an unexpected, beneficial outcome of research in gravitational biology and medicine is that it not only improves the conditions and prospects for space travelers, but it also results in enhanced knowledge that could contribute to the solution of physiological and biomedical problems for humans here on Earth3. Several Space Shuttle missions over the past decade have included experiments aimed at improving our understanding of the effect of microgravity on living organisms. For instance, the recent orbiter Columbia mission Neurolab (STS-90), proposed at the beginning of this ÒDecade of the BrainÓ, focused on basic neuroscience questions which will not only expand our understanding of how the nervous system develops and functions in space, but also increase our knowledge about how it develops and functions on Earth, thus contributing to the study and treatment of neurological diseases and disorders

Mammalian Brain As a Network of Networks

Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD