State-of-charge and state-of-health estimation for lithium-ion battery using the direct wave signals of guided wave

2021 ◽  
Vol 39 ◽  
pp. 102657
Author(s):  
Guoqi Zhao ◽  
Yu Liu ◽  
Gang Liu ◽  
Shiping Jiang ◽  
Wenfeng Hao
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Guided Wave ◽  
State Of Charge ◽  
State Of Health ◽  
Direct Wave

Performance Analysis of Lithium-Ion Battery Based on State of Charge and State of Health for EV Applications

2021 ◽  
Author(s):  
Mohan Thakre ◽  
Jayesh Bhartiy ◽  
Sunil Gawde ◽  
Om Pagar ◽  
Mangesh Darekar ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
State Of Health

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

2018 ◽  
Author(s):  
Zhongbao Wei ◽  
Feng Leng ◽  
Zhongjie He ◽  
Wenyu Zhang ◽  
Kaiyuan Li
Keyword(s):  
Lithium Ion Battery ◽  
Data Model ◽  
Experimental Studies ◽  
Lithium Ion ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Fusion Method ◽  
State Of Health ◽  
Model Fusion

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, the online model identification is scrutinized to achieve high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation alertness and numerical stability, so as to achieve 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 LIB. Simulation and experimental studies are performed to evaluate the performance of the proposed data-model fusion method. Results suggest that the proposed method can 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 accuracy and high stability.

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