Related papers: Data-driven nonparametric Li-ion battery ageing mo…
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this…
Lithium plating during fast charging is a critical degradation mechanism that accelerates capacity fade and can trigger catastrophic safety failures. Recent work has shown that plating onset can manifest in incremental-capacity analysis as…
Accurately predicting the state of health for sodium-ion batteries is crucial for managing battery modules, playing a vital role in ensuring operational safety. However, highly accurate models available thus far are rare due to a lack of…
Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce a novel multi modal data-driven approach employing an Automatic…
The rapid pace of replacing fossil fuel propelled transport by electric vehicles is critically dependent on high-performing, high energy density and efficient batteries. Optimal and safe use of existing battery cells and development of…
An important objective of designing lithium-ion rechargeable battery cells is to maximize their rate performance without compromising the energy density, which is mainly achieved through computationally expensive numerical simulations at…
Li-ion batteries gradually lose their capacity with time and use; therefore, ageing forecasts are key to designs of battery powered systems. So far, cell-type-specific studies without standardised testing practices have lead to a variety of…
To meet the fairly high safety and reliability requirements in practice, the state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a close relationship with the degradation performance, has been extensively studied…
This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells. Concept of confidence bound regions is introduced and advantages of this concept…
Modeling lithium-ion battery (LIB) degradation offers significant cost savings and enhances the safety and reliability of electric vehicles (EVs) and battery energy storage systems (BESS). Whilst data-driven methods have received great…
Electrochemical batteries are ubiquitous devices in our society. When they are employed in mission-critical applications, the ability to precisely predict the end of discharge under highly variable environmental and operating conditions is…
We consider the task of controlling a battery while balancing two competing objectives that evolve over different time scales. The short-term objective, such as arbitrage or load smoothing, improves with more battery cycling, while the…
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…
Physics-based battery modelling has emerged to accelerate battery materials discovery and performance assessment. Its success, however, is still hindered by difficulties in aligning models to experimental data. Bayesian approaches are a…
Fast and reliable validation of novel designs in complex physical systems such as batteries is critical to accelerating technological innovation. However, battery research and development remain bottlenecked by the prohibitively high time…
This paper presents a Bayesian parameter estimation approach and identifiability analysis for a lithium-ion battery model, to determine the uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset of the model…
This paper proposes a hierarchical Bayesian model for probabilistic estimation of the electric vehicle battery capacity fade. Since the battery aging factors such as temperature, current, and state of charge are not fixed, and they change…
The reuse of retired electric vehicle batteries in grid energy storage offers environmental and economic benefits. This study concentrates on health monitoring algorithms for retired batteries deployed in grid storage. Over 15 months of…
Lithium-ion batteries dominate battery research and industry due to their long research history and high energy density. However, increasing demand and limited lithium resources have raised lithium prices and battery costs, motivating…
This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that…