scholarly journals State of Charge and Lithium Manganate Batteries Internal Resistance Estimation at Low Charge/discharge rates

2021 ◽  
pp. 151024
Author(s):  
Xiongping Lin ◽  
Keyword(s):  
Internal Resistance ◽  
State Of Charge ◽  
Discharge Rates ◽  
Lithium Manganate ◽  
Low Charge ◽  
Charge Discharge

Metal-Organic Frameworks for High Charge-Discharge Rates in Lithium-Sulfur Batteries

2018 ◽  
Vol 57 (15) ◽  
pp. 3916-3921 ◽  
Author(s):  
Haoqing Jiang ◽  
Xiao-Chen Liu ◽  
Yushan Wu ◽  
Yufei Shu ◽  
Xuan Gong ◽  
...  
Keyword(s):  
Metal Organic Frameworks ◽  
High Charge ◽  
Discharge Rates ◽  
Lithium Sulfur Batteries ◽  
Metal Organic ◽  
Lithium Sulfur ◽  
Charge Discharge

Metal-Organic Frameworks for High Charge-Discharge Rates in Lithium-Sulfur Batteries

2018 ◽  
Vol 130 (15) ◽  
pp. 3980-3985 ◽  
Author(s):  
Haoqing Jiang ◽  
Xiao-Chen Liu ◽  
Yushan Wu ◽  
Yufei Shu ◽  
Xuan Gong ◽  
...  
Keyword(s):  
Metal Organic Frameworks ◽  
High Charge ◽  
Discharge Rates ◽  
Lithium Sulfur Batteries ◽  
Metal Organic ◽  
Lithium Sulfur ◽  
Charge Discharge

Combining Non-Parametric and Parametric Models for Stable and Computationally Efficient Battery Health Estimation

2020 ◽  
Author(s):  
Antti Aitio ◽  
David Howey
Keyword(s):  
Latent Variables ◽  
Linear Time ◽  
Full Range ◽  
Real Life ◽  
Internal Resistance ◽  
State Of Charge ◽  
Parametric Models ◽  
Model Parameters ◽  
Battery Management ◽  
Resistance Change

Abstract Equivalent circuit models for batteries are commonly used in electric vehicle battery management systems to estimate state of charge and other important latent variables. They are computationally inexpensive, but suffer from a loss of accuracy over the full range of conditions that may be experienced in real-life. One reason for this is that the model parameters, such as internal resistance, change over the lifetime of the battery due to degradation. However, estimating long term changes is challenging, because parameters also change with state of charge and other variables. To address this, we modelled the internal resistance parameter as a function of state of charge and degradation using a Gaussian process (GP). This was performed computationally efficiently using an algorithm [1] that interprets a GP to be the solution of a linear time-invariant stochastic differential equation. As a result, inference of the posterior distribution of the GP scales as 𝒪(n) and can be implemented recursively using a Kalman filter.

Electron-rich graphite-like electrode: stability vs. voltage for Al batteries

2018 ◽  
Vol 6 (23) ◽  
pp. 10776-10786 ◽  
Author(s):  
Preeti Bhauriyal ◽  
Priyanka Garg ◽  
Mahendra Patel ◽  
Biswarup Pathak
Keyword(s):  
Electrode Materials ◽  
Fast Charge ◽  
Electrode Stability ◽  
Discharge Rates ◽  
And Storage ◽  
Charge Discharge

The development of efficient Al batteries is hindered by the major challenge of finding advanced electrode materials, which can deliver higher voltage and storage capacities with ultra-fast charge/discharge rates.

scholarly journals Real time monitoring state-of-charge battery using internal resistance measurements for remote applications

2020 ◽  
Vol 1528 ◽  
pp. 012034
Author(s):  
Taufiq Alif Kurniawan ◽  
Aldyan Natajaya ◽  
Purnomo Sidi Priambodo ◽  
Gunawan Wibisono
Keyword(s):  
Real Time ◽  
Internal Resistance ◽  
State Of Charge ◽  
Real Time Monitoring
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