State of Charge Estimation by Joint Approach With Model-Based and Data-Driven Algorithm for Lithium-Ion Battery

In order to ensure the safety and service life of lithium-ion batteries for automotive applications, accurate state of charge (SOC) is required for system management in the process of driving. It is particularly challenging to estimate the SOC by using the online approach, for which the battery dyna...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on instrumentation and measurement Ročník 71; s. 1 - 10
Hlavní autoři: Shi, Qin, Jiang, Zhengxin, Wang, Zhi, Shao, Xingguo, He, Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Batteries
Battery charge dynamics
Bayesian belief network (BBN)
Belief networks
Computational modeling
Control algorithms
Control theory
discrete fractional model
Estimation
extended Kalman particle filter
Heuristic algorithms
Linear programming
Lithium-ion batteries
Machine learning
Mathematical models
Nonlinear dynamics
Parameter identification
Rechargeable batteries
Service life
State of charge
ISSN:0018-9456, 1557-9662
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In order to ensure the safety and service life of lithium-ion batteries for automotive applications, accurate state of charge (SOC) is required for system management in the process of driving. It is particularly challenging to estimate the SOC by using the online approach, for which the battery dynamics model is nonlinear. Many researchers have focused on model-based or data-driven algorithms alone, but comparatively few of them use a joint approach with the two types of algorithms. The data-driven algorithm is self-learning and has better adaptability, while the model-based algorithm is more stable and has stronger robustness. If these advantages can be combined, a better SOC estimation approach will be developed. In this article, based on battery charge dynamics, a complex fractional-order model of battery is simplified into a discrete fraction model for engineering application of control algorithm. A Bayesian belief network (BBN) is used to estimate the battery model parameters, and the adaptive extended Kalman particle filter (aEKPF) is used to estimate the SOC. In order to obtain accurate parameters of battery model for training, linear programming is used to identify the parameters online. Collectively, this article designs a joint approach of how the aEKPF with BBN estimates the SOC precisely. A developed approach has been downloaded into a battery control unit and tested in real-world conditions using a battery test bench to realize practical applications of the joint approach.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3199253