scholarly journals Lithium-ion Battery Charging System using Constant-Current Method with Fuzzy Logic Based ATmega16

2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Rossi Passarella ◽  
Ahmad Fali Oklilas ◽  
Tarida Mathilda
Keyword(s):  
Fuzzy Logic ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Current Method ◽  
Constant Current ◽  
Battery Charging ◽  
Charging System

scholarly journals Add-On Type Pulse Charger for Quick Charging Li-Ion Batteries

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 227 ◽  
Author(s):  
Bongwoo Kwak ◽  
Myungbok Kim ◽  
Jonghoon Kim
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Current Method ◽  
Constant Current ◽  
Experimental Conditions ◽  
Battery Charging ◽  
Long Run ◽  
Pulse Charging ◽  
Pulse Charge ◽  
Battery Packs

In this paper, an add-on type pulse charger is proposed to shorten the charging time of a lithium ion battery. To evaluate the performance of the proposed pulse charge method, an add-on type pulse charger prototype is designed and implemented. Pulse charging is applied to 18650 cylindrical lithium ion battery packs with 10 series and 2 parallel structures. The proposed pulse charger is controlled by pulse duty, frequency and magnitude. Various experimental conditions are applied to optimize the charging parameters of the pulse charging technique. Battery charging data are analyzed according to the current magnitude and duty at 500 Hz and 1000 Hz and 2000 Hz frequency conditions. The proposed system is similar to the charging speed of the constant current method under new battery conditions. However, it was confirmed that as the battery performance is degraded, the charging speed due to pulse charging increases. Thus, in applications where battery charging/discharging occurs frequently, the proposed pulse charger has the advantage of fast charging in the long run over conventional constant current (CC) chargers.

scholarly journals Primary Control Method of Wireless Charging System Based on Load Characteristics

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1269 ◽  
Author(s):  
Guodong Chen ◽  
Chao Rao ◽  
Yue Sun ◽  
Zhenxin Chen ◽  
Chunsen Tang ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Control Method ◽  
Lithium Ion ◽  
Input Voltage ◽  
Constant Current ◽  
Primary Control ◽  
Wireless Charging ◽  
Charging System ◽  
Equivalent Load ◽  
Secondary Side

Aiming at the output control issues of a lithium ion battery wireless charging system, a primary side control method based on load characteristic identification is proposed. The primary side impedance is calculated by detecting the effective value of the primary side voltage and current, and the mapping relationship between the equivalent load and the primary side impedance is established based on the AC impedance model. Using this mapping relation, the output of the secondary side can be regulated indirectly by controlling the input voltage of the inverter. Compared with the traditional control methods, the proposed control method not only eliminates the communication requirement between the primary side and secondary side, but also simplifies the hardware circuit design, reduces the complexity of the control circuit and also reduces the volume and cost of the system. In the paper, the impedance characteristics of the lithium ion battery at constant current and constant voltage stage are analyzed. The principle of the primary side control method is expounded and the realization method is given. The feasibility of the proposed control method is verified by simulation and experiment.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

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.

Optimal pulse-modulated Lithium-ion battery charging: Algorithms and simulation

2018 ◽  
Vol 15 ◽  
pp. 359-367 ◽  
Author(s):  
Huazhen Fang ◽  
Christopher Depcik ◽  
Vadim Lvovich
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Charging
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