Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression

2012 ◽  
Author(s):  
Datong Liu ◽  
Jingyue Pang ◽  
Jianbao Zhou ◽  
Yu Peng
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Battery ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Data Driven

Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression

Energy ◽  
2020 ◽  
Vol 205 ◽  
pp. 118000 ◽  
Author(s):  
Zhongwei Deng ◽  
Xiaosong Hu ◽  
Xianke Lin ◽  
Yunhong Che ◽  
Le Xu ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Battery ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
State Of Charge ◽  
Data Driven ◽  
Battery Packs ◽  
State Of Charge Estimation

Exploring Gaussian Process Regression and Unscented Kalman Filtering for Lithium-ion Battery Prognostics

2019 ◽  
Author(s):  
Imre Nagi ◽  
Darren Yin ◽  
Ali Yousafzai ◽  
Dimitrios Tzannetos ◽  
Ole J. Mengshoel ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Gaussian Process Regression ◽  
Lithium Ion

scholarly journals Prognostics for State of Health of Lithium-Ion Batteries Based on Gaussian Process Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Di Zhou ◽  
Hongtao Yin ◽  
Ping Fu ◽  
Xianhua Song ◽  
Wenbin Lu ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Battery ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Practical Significance ◽  
Accurate Estimation ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Li Ion ◽  
Qualitative Measure

Accurate estimation and prediction of the lithium-ion (Li-ion) batteries’ performance has important theoretical and practical significance to make better use of lithium-ion battery and to avoid unnecessary losses. State of health (SOH) estimation is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. To evaluate and predict the SOH of batteries, the Gaussian process regression with neural network (GPRNN) as its variance function is proposed. Experimental results confirm that the proposed method can be effectively applied to Li-ion battery monitoring and prognostics by quantitative comparison with basic GPR, combination LGPFR, combination QGPFR, and the multiscale GPR (SMK-GPR, P-MGPR, and SE-MGPR). The criteria of RMSE and MAPE of the proposed three models are reduced significantly compared to those of other existing methods.

Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis

2020 ◽  
Vol 445 ◽  
pp. 227281 ◽  
Author(s):  
Piyush Tagade ◽  
Krishnan S. Hariharan ◽  
Sanoop Ramachandran ◽  
Ashish Khandelwal ◽  
Arunava Naha ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Battery ◽  
Gaussian Process Regression ◽  
Lithium Ion
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