Design of Wireless Real Time Monitoring Lithium Ion Battery Charger Using Constant Current Constant Voltage Method

2021 ◽  
Vol 1 (10) ◽  
pp. 60-66
Author(s):  
Heri SURYOATMOJO
Keyword(s):  
Lithium Ion Battery ◽  
Real Time ◽  
Lithium Ion ◽  
Constant Current ◽  
Constant Voltage ◽  
Battery Charger ◽  
Real Time Monitoring

A MEMS platform for in situ, real-time monitoring of electrochemically induced mechanical changes in lithium-ion battery electrodes

2013 ◽  
Vol 23 (11) ◽  
pp. 114018 ◽  
Author(s):  
Ekaterina Pomerantseva ◽  
Hyun Jung ◽  
Markus Gnerlich ◽  
Sergio Baron ◽  
Konstantinos Gerasopoulos ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Real Time ◽  
Lithium Ion ◽  
Real Time Monitoring

Optimized charging of lithium-ion battery for electric vehicles: Adaptive multistage constant current–constant voltage charging strategy

2020 ◽  
Vol 146 ◽  
pp. 2688-2699 ◽  
Author(s):  
Yunjian Li ◽  
Kuining Li ◽  
Yi Xie ◽  
Jiangyan Liu ◽  
Chunyun Fu ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Constant Current ◽  
Constant Voltage ◽  
Charging Strategy

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

A novel spring-structured coaxial hierarchical SiO2@Co3O4 nanowire as a lithium-ion battery anode and its in situ real-time lithiation

2019 ◽  
Vol 31 (3) ◽  
pp. 035401 ◽  
Author(s):  
Tianli Han ◽  
Yingyi Ding ◽  
Yu Chen ◽  
Dong Cheng ◽  
Ping Zhou ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Real Time ◽  
Lithium Ion ◽  
Battery Anode

Design and consideration of off-board lithium-ion battery charger for electric vehicle

2020 ◽  
Vol 1517 ◽  
pp. 012080
Author(s):  
M R Rusli ◽  
A Mukhlisin ◽  
J Raharjo ◽  
M N Yuniarto ◽  
A Wikarta ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Battery Charger
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