Intelligent Control Technology of Charge and Discharge for Ni-MH Battery of Electric Vehicles

Abstract

Abstract: The Ni-MH battery is the power source of electric vehicles (EV) and plays a decisive role in the power performance. The charge and discharge control principle of Ni-MH battery used in EV is introduced. The whole construction and function flow of monitor and control for Ni-MH battery of EV are presented. The charge and discharge control arithmetic is investigated so that the Ni-MH energy of the EV may be consumed reasonably. After the investigation of the charge and discharge characteristic curves of Ni-MH battery, the effects of the charge and discharge voltage, current and battery temperature on the capacity are also analyzed. The experimental results show that the control system can make energy utilization increase by more than 20 percent, and it features strong self-adaptive ability and reliable operation, and moreover, the battery life prolonged remarkably.

Key words: Charge and discharge, Electric vehicle(EV), Energy management, Intelligent control, Ni-MH battery

CLC Number:

U 482. 3

XIE Qinghua;LU Yong;ZHANG Qi. Intelligent Control Technology of Charge and Discharge for Ni-MH Battery of Electric Vehicles[J]. , 2008, 44(8): 185-189.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/

http://www.cjmenet.com.cn/EN/Y2008/V44/I8/185

[1]	LIU Hui, Ma Xiaokang, HAN Lijin, XIANG Changle. Real-time Energy Management Strategy for Hybrid Electric Vehicles Based on Deep Reinforcement Learning and Model Predictive Control [J]. Journal of Mechanical Engineering, 2026, 62(6): 302-313.
[2]	ZHANG Baokun, DENG Junjun, WANG Zhenpo, CHEN Deliang, LI Lantian, LI Mingyang. Review of Bidirectional Wireless Power Transfer Systems for Electric Vehicles [J]. Journal of Mechanical Engineering, 2025, 61(8): 170-192.
[3]	HUANG Ying. Research on Intelligent Fusion Method for Multi Sensor Data in Complex Systems [J]. Journal of Mechanical Engineering, 2025, 61(4): 67-73.
[4]	YOU Guogui, LU Jianwei, ZHANG Guojun, SU Junshou, CAI Zemin. Review of Research on Energy Management Strategy for Plug-in Hybrid Electric Vehicles [J]. Journal of Mechanical Engineering, 2025, 61(4): 195-218.
[5]	XIONG Rui, LI Xinggang. Lithium Plating Evaluation Method for Li-ion Batteries Based on Simplified P2D Model [J]. Journal of Mechanical Engineering, 2025, 61(22): 176-188.
[6]	TANG Xiaolin, GAN Jiongpeng, ZHANG Zhenguo. Research on the Collaborative Energy Management Strategy of Hybrid Electric Vehicle Platoon Based on Multi-agent Deep Reinforcement Learning in the Transverse and Longitudinal Coupled Car-following Scenario [J]. Journal of Mechanical Engineering, 2025, 61(2): 236-246.
[7]	YANG Chao, DU Xuelong, WANG Weida, YANG Liuquan, XIANG Changle. Research on Real-time Energy Management Strategy for Hybrid Electric Vehicles based on Edge-cloud Collaborative Optimization [J]. Journal of Mechanical Engineering, 2025, 61(16): 227-238.
[8]	ZHANG Fengqi, ZHANG Tao, XIE Shaobo, TIAN Zejie, HU Xiaosong. Coordinated Optimization of Speed Planning and Energy-saving Control for Hybrid Vehicles Considering the Influence of Multiple Preceding Vehicles [J]. Journal of Mechanical Engineering, 2025, 61(16): 259-272.
[9]	FU Zhu,, CHEN Weimin, CHEN Li. Energy Management Strategy Based on Wave Height and Photovoltaic Real-time Prediction for PV-diesel-battery Unmanned Surface Vessel [J]. Journal of Mechanical Engineering, 2025, 61(16): 293-304.
[10]	XIE Shaobo, ZHANG Tao, ZHANG Fengqi, FAN Ning, HU Xiaosong. Hierarchical Optimization of Speed Planning and Energy Management for Hybrid Electric Vehicles Considering the Impact of Vehicle Queuing at Intersections [J]. Journal of Mechanical Engineering, 2025, 61(14): 197-211.
[11]	CAO Yanxu, WEI Zhongbao, SONG Ruoyang, HE Hongwen. Research on Multi-objective EMS of Fuel Cell Hybrid Electric Vehicle under Car Following Scenario Based on Deep Reinforcement Learning [J]. Journal of Mechanical Engineering, 2025, 61(12): 191-203.
[12]	DENG Jianxin, YUAN Bangyi, HUANG Qiulin, DING Dukun, XIN Manyu, LIU Guangming. Review on the Key Technologies of Complex Surfaces Polishing Based on Robots [J]. Journal of Mechanical Engineering, 2024, 60(7): 1-21.
[13]	GAO Kai, LUO Pan, XIE Jin, HU Lin, CHEN Bin, DU Ronghua. Hybrid Electric Vehicle Speed Profile Prediction Based on Data Fusion [J]. Journal of Mechanical Engineering, 2024, 60(6): 342-353.
[14]	ZHANG Xiaojun, WU Yaqi, LIU Haoxue, ZHONG Daofang. Design and Analysis of Wheel-footed Magnetic Adsorption Wall-climbing Robot with Passing Obstacles Capability [J]. Journal of Mechanical Engineering, 2024, 60(1): 248-261.
[15]	WEI Xiao-dong, SUN Chao, LIU Bo, HUO Wei-wei, REN Qiang, SUN Feng-chun. Co-optimization of Vehicle Speed and Energy for Fuel Cell Vehicles [J]. Journal of Mechanical Engineering, 2023, 59(8): 204-212.

Intelligent Control Technology of Charge and Discharge for Ni-MH Battery of Electric Vehicles

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments