soc-estimation

Battery state of charge estimation using kalman filter in Matlab

Neural ODE-based State of Charge (SOC) estimation for Li-ion batteries using the NASA Battery Dataset. Built as a weekend project to explore learned dynamics for battery modeling, with visualizations designed for engineering audiences.

MATLAB/Simulink model of a Series Hybrid Electric Vehicle (SHEV) with rule-based energy management, battery SOC estimation, fuel consumption and emission analysis.

Sandbox to develop, test and compare neural network-enabled estimation techniques for state of charge of a sample lithium-ion battery using deep learning methodologies, utilizing transient signals to predict state across points in time.

MATLAB/Simulink simulation of a 4S Li-ion BMS with SOC estimation, protection logic and cell balancing

Battery SOC estimation using equivalent circuit models, EKF, embedded-oriented implementation, and C firmware-style code

Deep learning State-of-Charge estimation for EV lithium-ion batteries on the LG 18650HG2 benchmark (-20 to +40 C): six architectures compared under identical conditions, each trained across seeds.

Prior-based patch-level representation learning for electric vehicle battery state-of-charge estimation across a wide temperature scope

MATLAB/Simulink battery SOC estimation project comparing Coulomb Counting, EKF, and UKF using a 1-RC lithium-ion battery model.

MATLAB implementation of VBMCCKF for Li-ion battery SOC estimation.

Machine learning-based battery State of Charge estimation using charge–discharge data, Coulomb counting, and Random Forest regression in Python.

Robust and lightweight lithium-ion battery SOC estimation using a short-history Random Forest under temperature variation and sensor uncertainty.

Simulation and Data Science Project: Model-based State-of-Charge estimation with a Kalman filter (MATLAB/Simulink) and data-driven Remaining-Useful-Life prediction using a Long Short-Term Memory Recurrent Neural Network (PyTorch).

Interactive Battery State of Charge (SoC) Estimator built with Python, Streamlit, Plotly, Pandas, and NumPy.

Battery Management System (BMS) State of Charge (SoC) estimator for Lithium-Ion batteries using an Extended Kalman Filter (EKF). Features a realistic simulation environment with sensor fusion, noise compensation, and voltage relaxation dynamics modeling.

State of Charge (SoC) estimation algorithms and battery modeling tools for Lithium-ion Battery Management Systems (BMS)

Adaptive 2RC Thevenin EKF for LG INR18650 MJ1 with autonomous excitation gating and causal online Bayesian R0 adaptation in MATLAB/Simulink.

Self-calibrating Dual Extended Kalman Filter (DEKF) state-of-charge estimator for LFP home batteries — AppDaemon apps for Home Assistant

EV Battery Management System (BMS) with Discrete Extended Kalman Filter (EKF) SOC estimation in MATLAB/Simulink.