A hybrid Kalman filter for SOC estimation of lithium-ion batteries

Hao, Tianyun; Ding, Jie; Tu, Taotao

doi:10.23919/ccc55666.2022.9901537

2022 41st Chinese Control Conference (CCC) 2022

DOI: 10.23919/ccc55666.2022.9901537

|View full text |Cite

A hybrid Kalman filter for SOC estimation of lithium-ion batteries

Tianyun Hao

Jie Ding

Taotao Tu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Qin,

Hao,

Ding

2023

J. Electrochem. Soc.

Self Cite

View full text Add to dashboard Cite

State of charge (SOC) refers to the remaining capacity of the battery, which cannot be measured directly. A multi-measurement Kalman filter which is composed of two sub Kalman filters is constructed to improve the estimation accuracy of SOC. The two sub filters share the same state function but have different measurements, namely the terminal voltage and the SOC estimation from neural network, respectively. Based on minimizing the trace of error covariance, an optimal weighted matrix is computed to fuse the estimates of the two sub filters. The training dataset of neural network is collected from mixed discharging cycles experiment and corresponding charging process. By comparing the results with model-based methods, such as H-infinity filter, unscented Kalman filter, data-driven methods, like neural networks and hybrid method, the multi-measurement Kalman filter is verified by both the root mean square error and mean absolute error that are less than 2% in different drive cycles.

show abstract

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Qin,

Hao,

Ding

2023

J. Electrochem. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A hybrid Kalman filter for SOC estimation of lithium-ion batteries

Cited by 1 publication

References 23 publications

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Multi-Measurement Kalman-Filtering-Based Neural Network Estimator for SOC of Lithium Batteries

Contact Info

Product

Resources

About