Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

Abstract

The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation's alertness and numerical stability so as to achieve an accurate online adaption of model parameters.Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data-model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy.

A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

“…For the accurate estimation of the SOC, a reliable battery model is required. The existing models include the electrochemical model [35] and the equivalent circuit model (ECM) [24,28]. Among others, the ECMs have a better trade-off between accuracy and complexity.…”

“…The ALS method extracted the possible correlation in the innovation sequence to estimate the measurement noise covariance. On the other hand, in order to improve the robustness of SOC estimation, H infinity filters [1,[26][27][28] were employed to deal with gross errors or outliers in measurements. However, when there are both modeling errors and outliers in the battery system, those filters cannot achieve satisfactory accuracy.…”

A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

“…Therefore, the key step of a SOH estimation method is to accurately obtain the SOH evaluation parameter of the battery. Considering that the main behavior of the aging phenomenon of a lithium-ion battery is the attenuation of energy and power performance, most of the SOH estimation methods select the decrease of capacity [7][8][9][10][11][12][13][14][15][16] and/or the increase of impedance [17][18][19][20][21][22][23][24] as the evaluation parameter.…”

“…It utilizes the accumulated charge value and the change value of state of charge (SOC) during a charging or discharging process and uses the definition of SOC to calculate the total capacity value directly [8]. This method is often coupled with the SOC estimation methods in the literature [9][10][11][12]. The indirect group of methods is based on the relationship of the capacity and several characteristics of the battery.…”

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

“…26 However, there are too many parameters to be determined in the complex model and the calculation amount will be increased greatly, which may bring about parameter divergence problems. 27 An enhanced ECM was constructed by Wei et al 28 for the charge redistribution and temperature effect studies. An nonlinear fractional-order estimator was built with guaranteed robustness and stability for the SoC indication of the LIBs.…”