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Shiyun Liu

@shiyunliu.bsky.social

Battery researcher building open battery data infrastructure. Working on testing, modelling, diagnostics, and AI-assisted tools for battery R&D. Research Fellow @ University of Leeds.

25 Posts6 Follower18 Folgt
Shiyun Liu@shiyunliu.bsky.social · 4mo ago

Battery Data Standard v0.2.2 is out. 🔋 This release improves support for auxiliary temperature channels with encoded headers, and adds example notebooks for MATLAB, time-series and EIS workflows. ✅ Better temperature-channel recognition 📊 New example notebooks 📦 Available now on PyPI

Battery Data Standard | Battery Cycler Data Conversion

Battery Data Standard is a Python library and CLI that converts raw battery cycler exports into validated, analysis-ready BDS data with provenance and quality reports.

bds.energy

Shiyun Liu@shiyunliu.bsky.social · 4mo ago

Battery Data Standard v0.1.2 is out. This release improves battery dataset ingestion: 1. Arbin Excel: better auto-selection of Channel_* sheets over duplicate RawData_* sheets 2. EIS Excel: support for ACIM_* sheets with Freq, Zmod, Zphz

Shiyun Liu@shiyunliu.bsky.social · 4mo ago

Cold-temperature performance is often highlighted as a potential advantage of sodium-ion batteries. Some manufacturers even report very high capacity retention at −40 °C, but these values are strongly protocol-dependent.

Bar charts comparing sub-zero discharge performance of five 18650 cell chemistries: three sodium-ion chemistries, NFM, CFM and NFPP, and two lithium-ion reference chemistries, LFP and NCA. Cells were discharged at 0.2 C after charging at 25 °C and soaking at the target temperature. At −20 °C, the sodium-ion cells retained around 87–90% of their 25 °C capacity, while LFP and NCA retained around 75% and 72%, respectively. At −40 °C, the sodium-ion cells still delivered measurable capacity, with retention of about 51% for NFM, 62% for CFM and 56% for NFPP. The lithium-ion reference cells at −40 °C were not reported because they did not sustain discharge under this protocol.
Ioneras@ioneras.com · 2y ago

New research reveals that dynamic discharge profiles, mimicking real-world EV usage, can extend lithium-ion battery life by up to 38% compared to constant current discharge. Machine learning insights emphasize the role of current pulses and time-induced ageing. www.nature.com/articles/s41...

Dynamic cycling enhances battery lifetime - Nature Energy

Lithium-ion batteries degrade in complex ways. This study shows that cycling under realistic electric vehicle driving profiles enhances battery lifetime by up to 38% compared with constant current cyc...

nature.com

Ioneras@ioneras.com · 2y ago

Are you a researcher in battery fields like materials, monitoring, modeling, or control? 🧪🔋 IONERAS is your ultimate hub for tracking Li-ion and Na-ion battery research. Get automated insights and stay ahead in the ever-evolving battery technology landscape. 🚀 #BatteryResearch #Innovation

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