相关论文: The Case for DeepSOH: Addressing Path Dependency f…
Existing approaches for battery health forecasting often rely on extensive cycling histories and continuously monitored cells. In contrast, many real-world scenarios provide only sparse information, e.g. a single diagnostic cycle. In our…
Battery degradation significantly impacts the reliability and efficiency of energy storage systems, particularly in electric vehicles and industrial applications. Predicting the remaining useful life (RUL) of lithium-ion batteries is…
Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications. Accurate RUL prediction improves the reliability and…
Energy storage solutions play an increasingly important role in modern infrastructure and lead-acid batteries are among the most commonly used in the rechargeable category. Due to normal degradation over time, correctly determining the…
Battery health monitoring is critical for the efficient and reliable operation of electric vehicles (EVs). This study introduces a transformer-based framework for estimating the State of Health (SoH) and predicting the Remaining Useful Life…
Batteries are an essential component in a deeply decarbonized future. Understanding battery performance and "useful life" as a function of design and use is of paramount importance to accelerating adoption. Historically, battery state of…
Lithium-ion batteries (Li-ion) have revolutionized energy storage technology, becoming integral to our daily lives by powering a diverse range of devices and applications. Their high energy density, fast power response, recyclability, and…
Accurate estimation of state of health (SOH) is critical for battery applications. Current model-based SOH estimation methods typically rely on low C-rate constant current tests to extract health parameters like solid phase volume fraction…
Real-time monitoring of the state of health (SoH) of batteries remains a major challenge, particularly in microgrids where operational constraints limit the use of traditional methods. As part of the 4BLife project, we propose an innovative…
Accurate prediction of the Remaining Useful Life (RUL) is essential for enabling timely maintenance of lithium-ion batteries, impacting the operational efficiency of electric applications that rely on them. This paper proposes a RUL…
Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH,) and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external…
Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The existing methods to predict the Remaining Useful Life(RUL) of…
Being able to predict battery internal states that are related to battery degradation is a key aspect to improve battery lifetime and performance, enhancing cleaner electric transportation and energy generation. However, most present…
Parameterisation of coupled degradation mechanisms in lithium-ion batteries is a major challenge. Interactions between the mechanisms depend on usage conditions: C-rate, rest state-of-charge (SoC), depth-of-discharge (DoD) and temperature.…
Understanding battery degradation in electric vehicles (EVs) under real-world conditions remains a critical yet under-explored area of research. Central to this investigation is the challenge of estimating the specific degradation modes in…
Accurately estimating the Remaining Useful Life (RUL) of a battery is essential for determining its lifespan and recharge requirements. In this work, we develop machine learning-based models to predict and classify battery RUL. We introduce…
Deep-space habitats (DSHs) are safety-critical systems that must operate autonomously for long periods, often beyond the reach of ground-based maintenance or expert intervention. Monitoring system health and anticipating failures are…
The State of Health (SOH) of lithium-ion batteries is directly related to their safety and efficiency, yet effective assessment of SOH remains challenging for real-world applications (e.g., electric vehicle). In this paper, the estimation…
Predicting lithium-ion battery lifetime is one of the greatest unsolved problems in battery research right now. Recent years have witnessed a surge in lifetime prediction papers using physics-based, empirical, or data-driven models, most of…
Accurate assessment of lithium-ion battery ageing is challenged by cell-to-cell variability, heterogeneous cycling protocols, and limited transferability of data-driven models across datasets. In particular, robust identification of…