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Accurate capacity prediction is essential for the safe and reliable operation of batteries by anticipating potential failures beforehand. The performance of state-of-the-art capacity prediction methods is significantly hindered by the…
For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a…
This paper presents the development of machine learning-enabled data-driven models for effective capacity predictions for lithium-ion batteries under different cyclic conditions. To achieve this, a model structure is first proposed with the…
Battery aging is a natural process that contributes to capacity and power fade, resulting in a gradual performance degradation over time and usage. State of Charge (SOC) and State of Health (SOH) monitoring of an aging battery poses a…
Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing processes, cell formats, and a lack of generally available…
Accurate prediction of lithium-ion battery lifespan is vital for ensuring operational reliability and reducing maintenance costs in applications like electric vehicles and smart grids. This study presents a hybrid learning framework for…
For the efficient and safe use of lithium-ion batteries, diagnosing their current state and predicting future states are crucial. Although there exist many models for the prediction of battery cycle life, they typically have very complex…
Accurate prediction of the state-of-health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and efficient operation of electric vehicles (EVs). Battery packs in EVs experience nonuniform degradation due to…
The sustainable utilization of lithium-ion batteries (LIBs) is crucial to the global energy transition and carbon neutrality, yet data scarcity and heterogeneity remain major barriers across remanufacturing, reusing, and recycling. This…
Accurate estimation of the internal states of lithium-ion batteries is key towards improving their management for safety, efficiency and longevity purposes. Various approaches exist in the literature in this context, among which designing…
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…
In this paper, we develop a hybrid prediction framework for accurate electric vehicle (EV) charging time estimation, a capability that is critical for trip planning, user satisfaction, and efficient operation of charging infrastructure. We…
Accurately predicting the lifetime of battery cells in early cycles holds tremendous value for battery research and development as well as numerous downstream applications. This task is rather challenging because diverse conditions, such as…
Electrochemical models offer superior interpretability and reliability for battery degradation diagnosis. However, the high computational cost of iterative parameter identification severely hinders the practical implementation of…
Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the…
The state of health for lithium battery is necessary to ensure the reliability and safety for battery energy storage system. Accurate prediction battery state of health plays an extremely important role in guaranteeing safety and minimizing…
Lithium-ion (Li-ion) batteries are ubiquitous in electric vehicles (EVs) as efficient energy storage devices. The reliable operation of Li-ion batteries depends critically on the accurate estimation of battery capacity. However,…
Physico-chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery…
Equivalent Circuit Model(ECM)has been widelyused in battery modeling and state estimation because of itssimplicity, stability and interpretability.However, ECM maygenerate large estimation errors in extreme working conditionssuch as…
Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases. While external control arms built from real-world data…