This book presents a groundbreaking approach to estimating the state of charge (SOC) of lithium-ion batteries. This method combines neural networks and Kalman filtering to enhance accuracy and adapt to battery aging, paving the way for improved battery management systems.
Yujie Wang
Author's books
Fuzzy Filter-Based State of Energy Estimation for Lithium-Ion Batteries
Real-time State of Energy (SOE) estimation is crucial for lithium-ion battery safety and predicting the driving range of electric vehicles. This book details an optimized algorithm combining a fuzzy controller and a Kalman filter for fast, accurate, and robust SOE estimation.
Intelligent Lithium-Ion Battery State of Charge (SOC) Estimation Methods
This book proposes a method for estimating lithium battery state of charge (SOC) by fusing deep learning and filtering algorithms. It uses BiLSTM neural networks to overcome the limitations of standard LSTM, resulting in improved feature extraction and greater accuracy.
Neural Network-Based State-of-Charge and State-of-Health Estimation
This book focuses on the co-estimation strategies of State-of-Charge (SOC) and State-of-Health (SOH) for lithium-ion batteries. It proposes a collaborative optimization strategy based on neural networks, providing technical references for scholars and engineers.