The Lithium-ion battery capacity prediction error analysis based on extended Kalman filtering

2014 ◽  
Author(s):  
Zhenwei Zhou ◽  
Yun Huang ◽  
Yudong Lu ◽  
Zhengyu Shi ◽  
Liangbiao Zhu ◽  
...  
Keyword(s):  
Error Analysis ◽  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Prediction Error ◽  
Lithium Ion ◽  
Extended Kalman Filtering ◽  
Battery Capacity ◽  
Capacity Prediction

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

2021 ◽  
pp. 014233122110579
Author(s):  
Tao Chen ◽  
Ciwei Gao ◽  
Hongxun Hui ◽  
Qiushi Cui ◽  
Huan Long
Keyword(s):  
Energy Storage ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Additive Models ◽  
Data Driven ◽  
Local Effects ◽  
Interaction Terms ◽  
Battery Capacity ◽  
Formulation Parameters ◽  
Capacity Prediction

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

scholarly journals Quantitative Analysis of Lithium-Ion Battery Capacity Prediction via Adaptive Bathtub-Shaped Function

Energies ◽  
2013 ◽  
Vol 6 (6) ◽  
pp. 3082-3096 ◽  
Author(s):  
Yi Chen ◽  
Qiang Miao ◽  
Bin Zheng ◽  
Shaomin Wu ◽  
Michael Pecht
Keyword(s):  
Quantitative Analysis ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Capacity ◽  
Capacity Prediction

Intelligent Computing for Extended Kalman Filtering SOC Algorithm of Lithium-Ion Battery

2018 ◽  
Vol 102 (2) ◽  
pp. 2063-2076
Author(s):  
Lanyong Zhang ◽  
Lei Zhang ◽  
Christos Papavassiliou ◽  
Sheng Liu
Keyword(s):  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Lithium Ion ◽  
Extended Kalman Filtering ◽  
Intelligent Computing

A novel voltage matching-adaptive extended Kalman filtering construction method for the state of charge prediction of lithium-ion batteries

2021 ◽  
pp. 014233122110294
Author(s):  
Weihao Shi ◽  
Shunli Wang ◽  
Lili Xia ◽  
Peng Yu ◽  
Bowen Li
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Extended Kalman Filtering

Accurately estimating the state of charge of lithium-ion batteries is of great significance to the development of the new energy industry. This research proposes a method for estimating the state of charge of lithium-ion batteries based on a voltage matching-adaptive extended Kalman filtering algorithm. The voltage matching part and the first-order resistance-capacitance (RC) part is combined into a new equivalent circuit model. This model improves the accuracy of voltage simulation at different charging and discharging stages through segment matching. Model-based adaptive extended Kalman filter algorithm adds a noise correction factor to adaptively correct the influence of noise on the estimation process and improve the estimation accuracy. The forgetting factor is introduced to improve the real-time performance of the algorithm. To verify the reliability of the model and algorithm, a multi-condition experiment is carried out on the lithium-ion battery. The verification results show that the simulation error of the circuit model to the working state of the lithium-ion battery is less than 0.0487V. The improved algorithm can accurately estimate the state of charge of lithium-ion batteries, the estimation accuracy of the discharge stage is 98.34%, and the estimation accuracy of the charging stage is 97.75%.

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