This report gives the failure rates for the major tritium containing (, glovebox systems that comprise the Secondary Containment System at the -Tritium Systems Test Assembly, which is a fusion research and technology ; facility at the Los Alamos National Laboratory. The component failure reports, the numbers of components, and operating times or demands are ali r'c given in this report, and sample calculations of the binomial demand failure rates and poisson hourly failure rates are given in the "' appendices. The faiil;re rates for these componentsform a solid data point based on actual operating experience, where there is very little t published information. The eight years of nearly continuous Secondary Containment System operationsshould result in steady state failure rate values. These data should be useful for future fusion reactor design work _'_ and safety assessment tasks. , • . ,_.

The estimation of lithium ion capacity fade and impedance rise on real application is always a challenging work due to the associated complexity. This work envisages the study of the battery charging profile indicators (CPI) to estimate battery health indicators (capacity and resistance, BHI), for high energy density lithium-ion batteries. Different incremental capacity (IC) parameters of the charging profile will be studied and compared to the battery capacity and resistance, in order to identify the data with the best correlation. In this sense, the constant voltage (CV) step duration, the magnitudes of the IC curve peaks, and the position of these peaks will be studied. Additionally, the behaviour of the IC curve will be modeled to determine if there is any correlation between the IC model parameters and the capacity and resistance. Results show that the developed IC parameter calculation and the correlation strategy are able to evaluate the SOH with less than 1% mean error for capacity and resistance estimation. The algorithm has been implemented on a real battery module and validated on a real platform, emulating heavy duty application conditions. In this preliminary validation, 1% and 3% error has been quantified for capacity and resistance estimation.

This report gives the failure rates for the major tritium containing (, glovebox systems that comprise the Secondary Containment System at the-Tritium Systems Test Assembly, which is a fusion research and technology ; facility at the Los Alamos National Laboratory. The component failure reports, the numbers of components, and operating times or demands are ali r'c given in this report, and sample calculations of the binomial demand failure rates and poisson hourly failure rates are given in the "' appendices. The faiil;re rates for these componentsform a solid data point based on actual operating experience, where there is very little t published information. The eight years of nearly continuous Secondary Containment System operationsshould result in steady state failure rate values. These data should be useful for future fusion reactor design work _'_ and safety assessment tasks.

The control of the battery-thermal-management-system (BTMS) is key to prevent catastrophic events and to ensure long lifespans of the batteries. Nonetheless, to achieve a high-quality control of BTMS, several technical challenges must be faced: safe and homogeneous control in a multi element system with just one actuator, limited computational resources, and energy consumption restrictions. To address those challenges and restrictions, we propose a surrogate BTMS control model consisting of a classification machine-learning model that defines the optimum cooling-heating power of the actuator according to several temperature measurements. The la-belled-data required to build the control model is generated from a simulation environment that integrates model-predictive-control and linear optimization concepts. As a result, a controller that optimally controls the actuator with multi-input temperature signals in a multi-objective optimization problem is constructed. This paper benchmarks the response of the proposal using different classification machine-learning models and compares them with the responses of a state diagram controller and a PID controller. The results show that the proposed surrogate model has 35% less energy consumption than the evaluated state diagram, and 60% less energy consumption than a traditional PID controller, while dealing with multi-input and multi-objective systems.

There is an exponential increase in electric vehicles on the road that need a follow up in terms of warranty. The proposed state of warranty (SOW) is a metastate that qualitatively describes the warranty fulfillment level of an electric vehicle. All the relevant warranty information is synthesized in a single merit while maintaining the level of detail through the qualitative substates. The developed SOW is calculated with a rule-based logic of an expert system that evaluates the quantitative value of three substates: the remaining warranty, the remaining health and the remaining useful warranty. The SOW provides a synthesized and user-friendly description of the warranty fulfillment state while providing quantitative detailed information of the most relevant features of each of the different maintenance methodologies.