Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network

Abstract

Currently, Lithium-ion batteries (LiB) are widely applied in energy storage devices in smart grids and electric vehicles. The state of charge (SOC) is an indication of the available battery capacity, and is one of the most important factors that should be monitored to optimize LiB’s performance and improve its lifetime. However, because the SOC relies on many nonlinear factors, it is difficult to estimate accurately. This paper presented the design of an effective SOC estimation method for a LiB pack Battery Management System (BMS) based on Kalman Filter (KF) and Artificial Neural Network (ANN). First, considering the configuration and specifications of the BMS and LiB pack, an ANN was constructed for the SOC estimation, and then the ANN was trained and tested using the Google TensorFlow open-source library. An SOC estimation model based on the extended KF (EKF) and a Thevenin battery model was developed. Then, we proposed a combined mode EKF-ANN that integrates the estimation of the EKF into the ANN. Both methods were evaluated through experiments conducted on a real LiB pack. As a result, the ANN and KF methods showed maximum errors of 2.6% and 2.8%, but the EKF-ANN method showed better performance with less than 1% error.

Memristive Equivalent Circuit Model for Battery

“…Its advantage is in simplicity and low computational cost. The drawbacks come from the struggle to recover from the bad initial condition since the absence of feedback [27].…”

A novel Drosophila‐back propagation method for the lithium‐ion battery state of charge estimation adaptive to complex working conditions

“…The hybrid estimation method is based on the ECM estimation and the introduction of neural networks to make corrections. Dao et al 21 combined the EKF and ANN, and Zhao et al 22 used a joint estimation of BP‐AEKF. On the one hand, if the model is accurate and the results of the equivalent model estimate are accurate, the neural network will converge quickly and the effect is good.…”

Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison

“…Lithium-ion batteries (LIBs) have been extensively commercialized as a primary energy storage technology for electric vehicles (EVs), stationary energy storage in the smart grid system and several other consumer electronics. The primary dominating factors of LIBs over other energy storage technologies include high energy density, long lifespan, and declining cost [1][2][3][4]. However, from literature and practice, it is noticed that the performance of LIBs as well as the durability and reliability are significantly influenced by the operating temperature.…”