A modular Python toolkit that cleans messy battery cycling data, extracts mechanistic insights, and delivers early-life AI predictions of cell lifetime—all in one streamlined pipeline.

Analyze battery cycle life and capacity degradation systematically using Python. Visualize capacity vs. cycle, Coulombic efficiency, dQ/dV curves, and fit SOH decay models for fast insights into battery performance.电池循环寿命与容量衰减自动分析，可视化输出容量-循环次数、库仑效率、dQ/dV 曲线，并拟合 SOH 衰减模型，用于快速评估电池性能变化趋势。

It provides functionalities for data reading, preprocessing, analysis, and visualization, helping users evaluate battery performance metrics such as capacity, cycle life, internal resistance, power density, energy density, and charge-discharge efficiency.

Battery_KneePoint_Detection_Data provides datasets and analysis scripts for lithium-ion cell degradation studies. It includes cycle-wise efficiency, thermal, and resistance metrics with knee point detection results, supporting state-of-health (SoH) monitoring and remaining useful life (RUL) forecasting.

Driving Sustainability: From Waste to Valuable Resources

Analysis ESS battery data and predict cycle life based on the 1 to 100 cycle summary data

Analysis using the "Second-life lithium-ion battery aging dataset based on grid storage cycling" dataset

It is QNN Machine learning technique used for Thermal analysis of EV batteries in order to prevent heating issues and less life cycle of batteries.

this project will use CALCE data for the study of battery cycle life sensitivity analysis and machine learning based aging modeling

"A data analysis project focused on monitoring and predicting mobile battery degradation over time. This tool analyzes charging cycles and capacity loss to estimate remaining battery life."

EV Battery Health Analysis is an end‑to‑end ML + web app that ingests NASA battery cycling data, engineers degradation features, trains baseline SOH/RUL models, and serves predictions through a FastAPI dashboard. It provides clear, user‑friendly explanations of battery health, remaining life, and risk, plus interactive charts and cycle inspection

This project uses machine learning to predict battery cycle life. It achieved a 95% accuracy in classifying battery longevity by analyzing 124 cells under fast-charging conditions. The analysis involved engineering voltage-curve features and applying elastic net and logistic regression models, which improved accuracy by 50%.

This project presents a thermodynamic and practical evaluation of Lithium-Ion and Flow batteries, focusing on their performance, cost, and sustainability for future energy storage solutions. Key highlights: Comparative analysis of energy density, power density, cycle life, and efficiency.

Python-based Life Cycle Cost Analysis (LCCA) tool evaluating five energy system configurations — Grid Only, Grid+Solar, Grid+Solar+Battery, and hybrid diesel variants — across Nigeria's four NERC tariff bands (A–D). Computes NPC, LCOE, and LOLP over a 25-year horizon with Monte Carlo simulation and sensitivity analysis.