The end-of-life event of the battery system of an electric vehicle is defined by a fixed end-of-life threshold value. However, this kind of end-of-life threshold does not capture the application and battery characteristics and, consequently, it has a low accuracy in describing the real end-of-life event. This paper proposes a systematic methodology to determine the end-of-life threshold that describes accurately the end-of-life event. The proposed methodology can be divided into three phases. In the first phase, the health indicators that represent the aging behavior of the battery are defined. In the second phase, the application specifications and battery characteristics are evaluated to generate the end-of-life criteria. Finally, in the third phase, the simulation environment used to calculate the end-of-life threshold is designed. In this third phase, the electric-thermal behavior of the battery at different aging conditions is simulated using an electro-thermal equivalent circuit model. The proposed methodology is applied to a high-energy electric vehicle application and to a high-power electric vehicle application. The stated hypotheses and the calculated end-of-life threshold of the high-energy application are empirically validated. The study shows that commonly assumed 80 or 70% EOL thresholds could lead to mayor under or over lifespan estimations.

Power cables are critical assets for the reliable operation of the grid. The cable lifetime is generally estimated from the conductor temperature, and associated lifetime reduction. However, these tasks are intricate due to the complex physics-of-failure (PoF) degradation mechanism of the cable. This is further complicated with the different sources of uncertainty that affect the cable lifetime estimation. Generally, simplified or deterministic PoF models are adopted resulting in non-accurate decision-making under uncertainty. In contrast, the integration of uncertainties leads to a probabilistic decision-making process impacting directly on the flexibility to adopt decisions. Accordingly, this paper presents a novel cable lifetime estimation framework that connects data-driven probabilistic uncertainty models with PoF-based operation, and degradation models through Bayesian state-estimation techniques. The framework estimates the cable health state, and infers confidence intervals to aid decision-making under uncertainty. The proposed approach is validated with a case study with different configuration parameters, and the effect of measurement errors on cable lifetime are evaluated with a sensitivity analysis. Results demonstrate that ambient temperature measurement errors influence more than load measurement errors, and the greater the cable conductor temperature the greater the influence of uncertainties on the lifetime estimate.

The use of dedicated components as a means of achieving desirable levels of fault tolerance in a system may result in high costs. A cost effective way of restoring failed functions is to use heterogeneous redundancies: components that, besides performing their primary intended design function, can also restore compatible functions of other components. In this paper, we apply a novel design methodology called D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) to assist in the systematic identification of heterogeneous redundancies, the design of hardware/software architectures including fault detection and reconfiguration, and the systematic dependability and cost assessments of the system. D3H2 integrates parameter uncertainty and criticality analyses to model inexact failure data in dependability assessment. The application to a railway case study is presented with a focus on analysing different reconfiguration strategies as well as types and levels of redundancies.

The increasing penetration of renewable energy sources (RESs) has a direct impact on the efficiency and reliability of the power grid. In this context, maintaining voltage stability and power quality has become more important than ever. Trans5 formers are crucial components in the grid and are generally equipped with on-load tap changer (OLTC) mechanisms. With the OLTC, transformers can automatically adapt to different voltage levels depending on the dynamics of the grid. Thereby, they assist in the penetration of RESs and improve system operation efficiency. However, the continuous grid fluctuations require increased activity from OLTCs, which in turn contributes to elevated stress on equipment and accelerated ageing. Existing OLTC monitoring solutions are generally invasive and time-consuming. Accordingly, this research work presents a new cost-effective OLTC monitoring approach based on audio signals. The approach is inspired by self-paced learning strategies. Initially, different monitoring solutions are evaluated and tested with respect to accuracy, computational cost, and robustness to noise. After the best architecture is selected, the audio signal processing approach is further refined through signal processing and Convolutional Neural Networks (CNNs). The proposed approach is benchmarked against AutoML, which shows a competitive performance, but with increased computational cost. The approach has been validated using real data from a transformer in operation.