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 ◽  
...  

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.

Author(s):  
Nguyen Thi Diep ◽  
Nguyen Kien Trung ◽  
Tran Trong Minh

This paper presents a design of the wireless charging system for e-byke applications. The double-side LCC compensation circuit is used to achieve high efficiency and reduce the volt-ampere rating. A new constant current/voltage (CC/CV) charging control method at the transmitter side is proposed to avoid dual side wireless communication. This paper also presents a simple method of estimating both the coupling coefficient and load impedance only from the transmitter side. A wireless charging system of 2.5kW is built. Error in the CC/CV charging mode is 3.3% and 1.12%, respectively. System efficiency reaches 92.1% in CC charging mode.


Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2685 ◽  
Author(s):  
Lingbing Gong ◽  
Chunyan Xiao ◽  
Bin Cao ◽  
Yuliang Zhou

In order to shorten the wireless charging time of electric vehicles (EVs) and achieve stable charging, an adaptive smart control method for EV wireless charging is proposed in the paper. The method dynamically tracks the rechargeable battery state during the whole charging process, realizes multi-stage charging of constant current (CC) or constant voltage (CV) by switching two kinds of compensation networks of bilateral L3C and L3C-C, and regulates the charging voltage and current to make it as close as possible to the battery charging characteristic curve. This method can be implemented because the voltage source connected to the coupler and the compensation networks of bilateral L3C and L3C-C have the CC and CV source characteristics, respectively. On the basis of the established adaptive smart control system of EV wireless charging, the experiments of wireless data transmission and adaptive smart charging were conducted. The results showed that the designed control system had a response time of less than 200 ms and strong anti-interference ability and it shortened the charging time by about 16% compared with the time using traditional charging methods, thereby achieving a fast, stable, safe, and complete wireless charging process.


2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang

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.


Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7594
Author(s):  
Zhao-Wei Gong ◽  
Jin-Gang Li ◽  
Xiang-Qian Tong

This paper presents a series hybrid wireless charging system with an active adjustable circuitry offering constant current and constant voltage output characteristics. The series hybrid system consists of the inductor–capacitor–capacitor (LCC) and series-series (SS) networks are used for improving charging pad misalignment tolerance. An active switch is employed to provide an adjustable CC and CV output for different battery charging stages. To demonstrate the performance of the proposed method, a 310 W prototype was built. A systematic optimization in the parameter of the proposed topology to achieve relative constant output was analyzed within a certain range of the designed operating region. The experimental results indicate that the output current fluctuation is less than 5% with load variations, and the output voltage fluctuation is less than 5% with load varying from 19 to 70 Ω, as the pick-up pads misaligned within 50% of the pad outer diameter.


Sign in / Sign up

Export Citation Format

Share Document