Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

Abstract

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries

“…The original data for each feature sequence is normalized in the range [-1, 1] before training and testing to optimize the robustness, convergence rate, and speed up the gradient descent of the NARX network, as presented in Eq. (6).…”

“…The battery management system (BMS) is embedded to monitor the battery parameters, including current, voltage, etc., under various operating conditions, such as temperatures, current rates, etc., to accurately estimate the state of charge (SOC) to ensure the safety and reliability of the batteries in EVs [6,7]. SOC is the ratio of the remaining useful capacity during a given cycle to the maximum possible charge that can be stored in the battery (nominal capacity).…”

State of energy estimation of lithium-ion battery based on long short-term memory optimization Adaptive Cubature Kalman filter

“…Li et al [ 14 ] introduced the constraint condition of the pneumatic principle to replace the temperature correction coefficient, which can realize the fast convergence of SOC. Li et al [ 15 ] the vector forgetting factor recursive least squares method is utilized for model parameter online identification.…”