Battery-SoC-Estimation and Battery_SoC_Estimation
These are competitors offering alternative approaches to the same problem—one emphasizes physics-based hybrid modeling for SAE publication rigor, while the other focuses on stochastic methods—so practitioners would typically select based on their preference for interpretability versus statistical robustness rather than using both.
About Battery-SoC-Estimation
uw-mad-dash/Battery-SoC-Estimation
Data and code for the paper 'Estimating Battery State-of-Charge within 1% using Machine Learning and Physics-based Models' (SAE'23)
This project helps engineers and researchers accurately estimate the remaining charge in lithium-polymer (LiPo) batteries. It takes raw battery sensor data, like voltage, current, and temperature, and uses machine learning models to predict the battery's State-of-Charge (SoC) within 1% accuracy. This is ideal for battery engineers, electric vehicle designers, or power system managers who need precise battery monitoring.
About Battery_SoC_Estimation
uslumt/Battery_SoC_Estimation
Battery State Of Charge(SoC) Estimation Using Stochastic Methods & Machine Learning.
This project helps battery engineers and manufacturers accurately estimate the remaining charge in lithium-ion batteries. By taking raw battery sensor data, it provides precise State of Charge (SoC) estimations using various machine learning and stochastic methods. This is crucial for optimizing battery performance and ensuring reliable operation in electric vehicles and other battery-powered systems.
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