Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries
By M. Petzl, Michael A. Danzer. IEEE transactions on energy conversion, 2013
196 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/e7ae169e9d58c7684ae89e5da275521f0d4587ab
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Chinemerem Christopher Ibe-Ekeocha
- Hicham Chaoui
- Jinhao Meng
- Guangzhao Luo
- Chunting Chris Mi
- Wenxin Huang
- Yuhong Fu
- Fei Gao
In Electrical and Electronic Engineering
- Chinemerem Christopher Ibe-Ekeocha
- Hicham Chaoui
- Jinhao Meng
- Guangzhao Luo
- Chunting Chris Mi
- Wenxin Huang
- Yuhong Fu
- Fei Gao
Citations in the corpus
- State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (2017, background): “It effectiveness is based on a relationship between the voltage at battery’s terminals and its SOC [10], [11].”
- Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis (2018, background): “Nevertheless, the performance of the voltage-capacity rate curve is sensitive to the sampling frequency and the measurement noise [40,45].”
- Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016, methodology): “The conventional methods for SOC estimation include Ampere hour counting or coulomb counting [6], open-circuit voltage estimation [7], artificial neural networks [8], and others [9]–[12], all of which can be adopted to complete the SOC estimation process and obtain acceptable results in different ap”