A hybrid model predictive and fuzzy logic based control method for state of power estimation of series-connected Lithium-ion batteries in HEVs

2019 ◽  
Vol 24 ◽  
pp. 100758 ◽  
Author(s):  
M.J. Esfandyari ◽  
M.R. Hairi Yazdi ◽  
V. Esfahanian ◽  
M. Masih-Tehrani ◽  
H. Nehzati ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Lithium Ion Batteries ◽  
Hybrid Model ◽  
Control Method ◽  
Lithium Ion ◽  
Power Estimation ◽  
State Of Power

A novel fusion model based online state of power estimation method for lithium-ion capacitor

2021 ◽  
Vol 36 ◽  
pp. 102387
Author(s):  
Wenxin Chen ◽  
Cheng Xu ◽  
Manlin Chen ◽  
Kai Jiang ◽  
Kangli Wang
Keyword(s):  
Estimation Method ◽  
Lithium Ion ◽  
Power Estimation ◽  
Fusion Model ◽  
Model Based ◽  
Lithium Ion Capacitor ◽  
State Of Power

Modeling and Estimation for Advanced Battery Management

2019 ◽  
Vol 2 (1) ◽  
pp. 393-426 ◽  
Author(s):  
Xinfan Lin ◽  
Youngki Kim ◽  
Shankar Mohan ◽  
Jason B. Siegel ◽  
Anna G. Stefanopoulou
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The State ◽  
Consumer Electronics ◽  
Accurate Estimation ◽  
Health State ◽  
Battery Management ◽  
Space Applications ◽  
Internal States ◽  
State Of Power

The commercialization of lithium-ion batteries enabled the widespread use of portable consumer electronics and serious efforts to electrify trans-portation. Managing the potent brew of lithium-ion batteries in the large quantities necessary for vehicle propulsion is still challenging. From space applications a billion miles from Earth to the daily commute of a hybrid electric automobile, these batteries require sophisticated battery management systems based on accurate estimation of battery internal states. This system is the brain of the battery and is responsible for estimating the state of charge, state of health, state of power, and temperature. The state estimation relies on accurate prediction of complex electrochemical, thermal, and mechanical phenomena, which increases the importance of model and parameter accuracy. Moreover, as the batteries age, how should the parameters of the model change to accurately represent the performance, and how can we leverage the limited sensor information from the measured terminal voltage and sparse surface temperatures available in a battery system? With a frugal sensor set, what is the optimal sensor placement? This article reviews estimation techniques and error bounds regarding sensor noise and modeling errors, and concludes with an outlook on the research that will be necessary to enable fast charging, repurposing of batteries for grid energy storage, degradation prediction, and fault detection.

Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries

2012 ◽  
Vol 96 ◽  
pp. 378-386 ◽  
Author(s):  
Fengchun Sun ◽  
Rui Xiong ◽  
Hongwen He ◽  
Weiqing Li ◽  
Johan Eric Emmanuel Aussems
Keyword(s):  
Lithium Ion Batteries ◽  
Peak Power ◽  
Lithium Ion ◽  
Power Estimation ◽  
Parameter Method ◽  
Model Based
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