Haijun Ruan

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Assistant Professor

Coventry University
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United Kingdom

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Positions (1)

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Haijun Ruan

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Coventry University

PhD in Battery Degradation Modelling, AI and Machine Learning for Battery Prognostics

Coventry University is advertising a PhD opportunity in battery degradation modelling, artificial intelligence, machine learning, and battery prognostics . The project focuses on combining physics-based modelling with AI/ML to better predict how batteries age, estimate remaining useful life (RUL) , quantify prediction uncertainty, and explore second-life battery and battery repurposing applications. This is a four-year, full-time PhD with interdisciplinary research training and an international research attachment/collaboration with A*STAR - Agency for Science, Technology and Research in Singapore. The work is positioned at the intersection of energy storage , sustainable energy , battery science , and machine learning . Eligibility highlights: applicants should have a background in Electrical and Electronic Engineering , Mechanical Engineering , Computer Science , Mathematics , Control Engineering , Electrochemical Engineering , or a related STEM discipline. Prior experience in batteries, modelling, AI, or machine learning is considered an advantage. Location: Coventry University, UK, with an international research attachment in Singapore. Deadline: 15 November 2026. How to apply: review the project page and submit your application through the provided link. If you are interested in battery prognostics, degradation modelling, and trustworthy AI for energy storage, this is a strong fit.

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Review of Low-Temperature Performance, Modeling and Heating for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have the advantages of high energy/power densities, low self-discharge rate, and long cycle life, and thus are widely used in electric vehicles (EVs). However, at low temperatures, the peak power and available energy of LIBs drop sharply, with a high risk of lithium plating during charging. This poor performance significantly impacts the application of EVs in cold weather and dramatically limits the promotion of EVs in high-latitude regions. This challenge recently attracted much attention, especially investigating the performance decrease for LIBs at low temperatures, and exploring the solutions; however, limited reviews exist on this topic. Here, we thoroughly review the state-of-the-arts about battery performance decrease, modeling, and preheating, aiming to drive effective solutions for addressing the low-temperature challenge of LIBs. We outline the performance limitations of LIBs at low temperatures and quantify the significant changes in (dis)charging performance and resistance of LIBs at low temperatures. The various models considering low-temperature influencing factors are also tabulated and summarized, with the modeling improvement for describing low-temperature performance highlighted. Furthermore, we categorize the existing heating methods, and the metrics such as heating rate, energy consumption, and lifetime impact are highlighted to provide fundamental insights into the heating methods. Finally, the limits of current research on low-temperature LIBs are outlined, and an outlook on future research direction is provided.

Year:

2023

Collaborators (1)

cheng zhang

Coventry University

UNITED KINGDOM
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