Related papers: Fast data augmentation for battery degradation pre…

This paper proposes a fully unsupervised methodology for the reliable extraction of latent variables representing the characteristics of lithium-ion batteries (LIBs) from electrochemical impedance spectroscopy (EIS) data using information…

This paper presents a combination of machine learning techniques to enable prompt evaluation of retired electric vehicle batteries as to either retain those batteries for a second-life application and extend their operation beyond the…

Battery degradation is governed by complex and randomized cyclic conditions, yet existing modeling and prediction frameworks usually rely on rigid, unchanging protocols that fail to capture real-world dynamics. The stochastic electrical…

Hydrogen-based zero-emission ships are a key element in the decarbonization of the maritime sector. To strengthen these their economic competitiveness, it is key to drive their costs to a minimum. Current literature mainly focuses on fuel…

Grid-scale battery degradation unfolds over multi-year timescales under coupled electrochemical, thermal, and operational feedbacks difficult to capture using laboratory data or proprietary field datasets. This scarcity limits the…

Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping capacity with the growing number of charging/discharging…

Batteries are dynamic systems with complicated nonlinear aging, highly dependent on cell design, chemistry, manufacturing, and operational conditions. Prediction of battery cycle life and estimation of aging states is important to…

Battery health prognostics are critical for ensuring safety, efficiency, and sustainability in modern energy systems. However, it has been challenging to achieve accurate and robust prognostics due to complex battery degradation behaviors…

Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are two effective approaches for battery degradation monitoring. One limiting factor for their real-world application is that they require constant-current (CC)…

Fast charging of lithium-ion batteries remains a critical bottleneck for widespread adoption of electric vehicles and stationary energy storage systems, as improperly designed fast charging can accelerate battery degradation and shorten…

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health…

Hundreds of millions of people lack access to electricity. Decentralised solar-battery systems are key for addressing this whilst avoiding carbon emissions and air pollution, but are hindered by relatively high costs and rural locations…

Health modeling of lithium-ion batteries (LIBs) is crucial for safe and efficient energy management and carries significant socio-economic implications. Although Machine Learning (ML)-based State of Health (SOH) estimation methods have made…

Accurate modeling of lithium ion (li-ion) batteries is essential for enhancing the safety, and efficiency of electric vehicles and renewable energy systems. This paper presents a data-inspired approach for improving the fidelity of…

Designing lithium-ion batteries for long service life remains a challenge, as most cells are optimized for beginning-of-life metrics such as energy density, often overlooking how design and operating conditions shape degradation. This work…

Efficient parameter identification of electrochemical models is crucial for accurate monitoring and control of lithium-ion cells. This process becomes challenging when applied to complex models that rely on a considerable number of…

Analyzing rate-performance data in battery electrodes is greatly facilitated by access to simple analytical models. Here we describe a number of simple equations for fitting capacity-rate data. These equations output fit parameters, such as…

Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty liabilities. However, very few published models of battery…

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…

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…