Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves

The accurate estimation of the retained capacity in a lithium-ion battery is an essential requirement for the electric vehicles. The aging of the batteries depends on parameters and factors that are not easily monitored by the battery management system. This paper analyzes the ability of various machine learning algorithms to deal with the data generated by the battery management system during the partial charging/discharging process to instantly diagnose and estimate the retained capacity of the battery. Experimental data from an online dataset containing thousands of battery cycles are used for training and validation of the different models. Results demonstrate that the developed convolutional neural network outperforms the rest of the machine learning algorithms implemented, regardless of the portion of the cycle registered by the battery management system. The estimates obtained outperform most previous references. However, the estimation error values registered when analyzing partial cycles with depths lower than 50 % (above 1.5 %) remain too high to validate any of the analyzed algorithms as a solution for commercial systems.Funding for open access charge: CRUE-Universitat Jaume

Data-driven reliability analysis of Boeing 787 Dreamliner

The Boeing 787 Dreamliner, launched in 2011, was presented as a game changer in air travel. With the aim of producing an efficient, mid-size, wide-body plane, Boeing initiated innovations in product and process design, supply chain operation, and risk management. Nevertheless, there were reliability issues from the start, and the plane was grounded by the U.S. Federal Aviation Administration (FAA) in 2013, due to safety problems associated with Li-ion battery fires. This paper chronicles events associated with the aircraft's initial reliability challenges. The manufacturing, supply chain, and organizational factors that contributed to these problems are assessed based on FAA data. Recommendations and lessons learned are provided for the benefit of engineers and managers who will be engaged in future complex systems development

Remote Computing Cluster for the Optimization of Preventive Maintenance Strategies: Models and Algorithms

The chapter describes a mathematical model of the early prognosis of the state of high-complexity mechanisms. Based on the model, systems of recognizing automata are constructed, which are a set of interacting modified Turing machines. The purposes of the recognizing automata system are to calculate the predictors of the sensor signals (such as vibration sensors) and predict the evolution of hidden predictors of dysfunction in the work of the mechanism, leading in the future to the development of faults of mechanism. Hidden predictors are determined from the analysis of the internal states of the recognizing automata obtained from wavelet decompositions of time series of sensor signals. The results obtained are the basis for optimizing the maintenance strategies. Such strategies are chosen from the classes of solutions to management problems. Models and algorithms for self-maintenance and self-recovery systems are discussed

Assessment of the calendar aging of lithium-ion batteries for a long-term—Space missions

Energy availability is a critical challenge for space missions, especially for those missions designed to last many decades. Space satellites have depended on various combinations of radioisotope thermoelectric generators (RGTs), solar arrays, and batteries for power. For deep space missions lasting as long as 50 + years, batteries will also be needed for applications when there is no sunlight and RTGs cannot support peak power demand due to their insufficient specific power. This paper addresses the potential use of lithium-ion batteries for long-term space missions. Using data collected from the literature and internal experiments, a calendar aging model is developed to assess the capacity fade as a function of temperature, state-of-charge and time. The results for various LIB chemistries are used to identify the best candidate chemistries and determine the conditions, with a focus on low temperatures, that can best enable deep space missions

Predicting Sets of Automata: Architecture, Evolution, Examples of Prognosis, and Applications

This chapter describes the sets of interacting automata constructed on the cascades of wavelet coefficients of input signal. The basic principles of the evolution of automata during the processing of incoming cascades and the vector of processes consisting of segments of cascades of constant length are described. The main principles of constructing the family of automata are determined from the internal symmetry of incoming cascades and the definition of symmetry groups of vector processes and their isotropy groups. The trajectories of states are defined on nontrivial topological spaces, the so-called degeneration spaces of the characteristic functional. The family of evolving automata with tunable communications architecture is designed to predict the state of engineering objects and identify predictors, early predictors, and hidden predictors of failure. This chapter provides examples of the work of predictive automata in various fields of engineering and medicine. It demonstrates the operation of the automaton in spaces with a nontrivial topology of input cascades, algorithms of the predictor search, and estimations. The family of evolving automata with reconstructing architecture of connections is designed to predict the state of engineering objects and medicine and identify predictors, early predictors, and hidden predictors of failure. The architecture and functional properties of automata are determined from the results and main conclusions

ESD Events To Wearable Medical Devices In Healthcare Environments—Part 1: Current Measurements

Wearable medical devices are widely used for monitoring and treatment of patients. Electrostatic discharge can render these devices unreliable and cause a temporary or permanent disturbance in their operation. In a healthcare environment, severe electrostatic discharge (ESD) can occur while a patient, lying down or sitting on a hospital bed with a wearable device, discharges the device via a grounded bedframe. To protect the devices from ESD damage, the worst-case discharge conditions in the usage environment need to be identified. Previous studies by authors revealed that such events could be more severe than the conventional human metal model (HMM). However, the impact of various body postures and device location on the body and the severity of the discharge current compared with HMM have not been investigated for healthcare environments. This study is an attempt to address the gap in the literature by investigating severe discharges in such environments and characterizing their current waveforms for three postures (standing on the floor, sitting, and lying down on a hospital bed), two device locations (hand and waist), and four body voltages (2, 4, 6, and 8 kV). This study highlights that the IEC 61000-4-2 standard may not be sufficient for testing wearable medical devices