A Collaborative Estimation Scheme for Lithium-Ion Battery State of Charge and State of Health Based on Electrochemical Model

Abstract

Many studies focus on single state of health or state of charge estimation. How to effectively combine the two for joint estimation is particularly important. This paper relies on our previous research foundation of SOH, and further increases the electrochemical model, and realizes the SOC estimation of the battery under different SOH stages by combining the two. An SPM model based on electrochemical mechanism is proposed and sensitivity analysis is performed for the parameters in the model. Three types of dynamic conditions, FUDS, BJDST and DST, are used to verify the feasibility of the model. Further, the synergistic estimation of SOH and SOC is realized by the change of the difference between the two lithium ion concentrations with the battery decay when SOC=100% and SOC=0% in the SPM model. In the offline state, a linear regression relationship is constructed between the results of the GPR model and the parameters of the SPM model. During online prediction, the parameters of SPM are obtained by calibrating the offline model, and the goal of estimating SOC based on SOH is realized. The experimental results show that the maximum error value of SOC estimation does not exceed 0.08, and the model has high accuracy.

A review of battery state of charge estimation and management systems: Models and future prospective

“…Balancing between the model accuracy and complexity improves in this model performance limited and can be divided into two different general ways: the first is to utilize the simplified SP model with the integration of adaptive techniques such as extended Kalman filters (KF) or Adaptive Particle Swarm Optimization (APSO) (Y. Liu et al, 2021; Wu et al, 2022). which can provide precise estimations, while the second is to simplify the P2D model complexity using specific model‐order‐reduction methods (W. Li, Fan, et al, 2020), which can scale down the needed computational time by over 600 times compared to the conventional model (C. Li, Cui, et al, 2021).…”

State of Charge Estimation for Lithium-Ion Battery Based on Unscented Kalman Filter and Long Short-Term Memory Neural Network

“…Commonly used models include the electrochemical mechanism model, equivalent circuit model (ECM), and black-box model [13]. Because the electrochemical mechanism model is too complex, it has a relatively low level of application [14]. The ECM, particularly the first-order RC model and second-order RC model, is widely used due to its simplicity, low computational requirements, and ability to reflect the internal mechanism to some extent [15,16].…”

SOC Estimation Methods for Lithium-Ion Batteries without Current Monitoring

“…A battery model, which includes equivalent circuit models (ECM) [21] and electrochemical models, provides high simulation accuracy and reflects the external characteristics of the battery. The electrochemical model parameters have physical meanings and act as a bridge between the external characteristics of the battery and the internal electrochemical reaction mechanism of the battery [22][23][24]. In order to achieve accurate SOC estimation, the BMS requires current measurements as input to the estimator.…”