The design of intelligent charger of electric vehicle under the control of microcontrollers

被引：0

作者：

Liu H. ^{[1
]}

机构：

[1] Shandong Polytechnic, Jinan

来源：

International Journal of Mechatronics and Applied Mechanics | 2020年 / 1卷 / 07期

关键词：

Charger; Electric Vehicle; MCU;

D O I：

10.17683/ijomam/issue7.15

中图分类号：

学科分类号：

摘要：

To explore the mechanism and effect of the intelligent charger of electric vehicle under the control of microcontroller (MCU), the working effect of the intelligent charger of electric vehicle was discussed by constructing the charger model of electric vehicle based on STC12C5204AD MCU and the simulation experiment method. The specific energy density of the battery for the charger under the action of STC12C5204AD, 8xC752, and C504 MCU and the charging times for the battery life under different single chip computers were mainly studied. Moreover, the effect and mechanism of the intelligent charger and the common charger were explored. The research results showed that the battery based on STC12C5204AD MCU had little effect on the change of specific energy of battery, the specific energy basically maintained the initial value, and the effect was prominent. Compared with 8xC752 and C504 MCU, STC12C5204AD MCU had the most prominent effect on delaying the decline of battery life under the same charging times. In contrast with the general charger, the intelligent charger would change its charging voltage according to the change of the rated voltage of the battery to maintain the maximum protection of the battery life. The ordinary battery could maintain a certain value without random change. To sum up, compared with the common electric vehicle charger, the intelligent charger has obvious advantages, which significantly improves the protection of the battery and delays the decline of it. The intelligent electric vehicle charger based on STC12C5204AD MCU has outstanding effect. The model of the intelligent electric vehicle charger based on STC12C5204AD meets the high standard requirements of the electric vehicle for the charger. The research on the intelligent charger of electric vehicle under the control of MCU has a positive effect on the follow-up research. © 2020, Cefin Publishing House. All rights reserved.

引用

页码：99 / 107

页数：8

共 17 条

[1]

Jiang G., He H., Yan J, Et al., Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox, IEEE Transactions on Industrial Electronics, 66, 4, pp. 3196-3207, (2018)

[2]

Peeters C., Guillaume P., Helsen J., Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy, Renewable Energy, 116, pp. 74-87, (2018)

[3]

Dao P. B., Staszewski W. J., Barszcz T, Et al., Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data, Renewable Energy, 116, pp. 107-122, (2018)

[4]

Feng Z., Qin S., Liang M., Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary condition, Renewable Energy, 85, pp. 45-56, (2016)

[5]

Chen J., Pan J., Li Z, Et al., Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals, Renewable Energy, 89, pp. 80-92, (2016)

[6]

Cao M., Qiu Y., Feng Y, Et al., Study of wind turbine fault diagnosis based on unscented Kalman filter and SCADA data, Energies, 9, 10, (2016)

[7]

Maheswari R. U., Umamaheswari R., Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train–A contemporary survey, Mechanical Systems and Signal Processing, 85, pp. 296-311, (2017)

[8]

Astolfi D., Scappaticci L., Terzi L., Fault diagnosis of wind turbine gearboxes through temperature and vibration data, International Journal of Renewable Energy Research (IJRER), 7, 2, pp. 965-976, (2017)

[9]

Zhao H., Liu H., Hu W, Et al., Anomaly detection and fault analysis of wind turbine components based on deep learning network, Renewable energy, 127, pp. 825-834, (2018)

[10]

Qiu Y., Feng Y., Sun J, Et al., Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data, IET Renewable Power Generation, 10, 5, pp. 661-668, (2016)

← 1 2 →