基于模糊神经网络PID控制的智能充电方法研究  

Research on intelligent charging method based on fuzzy neural network PID control

作　　者：朱望纯[1,2] 孙启林 ZHU Wang-chun;SUN Qi-lin(Schoo of Elecronic Engineering and Aumamin Cuiilin Universityof Electronic Technology,Guilin Guangxi 541004,China;Guangxi Key Laboratory of Automatic Detecting Technology and Instruments,Guilin Guangxi 540004,China)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,广西桂林541004 [2]广西自动检测技术与仪器重点实验室,广西桂林541004

出　　处：《电源技术》2020年第3期414-417,共4页Chinese Journal of Power Sources

基　　金：广西自动检测技术与仪器重点实验室主任基金项目(YQ16111);桂林电子科技大学研究生教育创新计划资助项目(2019YCXS096)。

摘　　要：铅酸蓄电池充电过程具有多变量、非线性、时变性、滞后性的特点,现有充电技术的不足,严重影响了充电效率和电池寿命。提出了一种基于模糊RBF神经网络PID控制的间歇正负脉冲的充电控制策略。利用具有较强逻辑推理能力的模糊PID与RBF神经网络相结合,实现充电参数的动态调整和充电电流的在线控制。通过实验和仿真测试结果表明,本充电控制方法有效缩短了充电时间,充电电流曲线能更好地逼近马斯充电曲线,达到了提高充电效率和延长蓄电池使用寿命的目的。The lead acid battery charging process has the characteristics of multi-variable,nonlinear and time varying.The lack of existing charging technology has seriously affected the charging efficiency and battery life.Aiming at this problem,a charging method based on fuzzy RBF neural network PID control with intermittent positive and negative pulse is proposed.The fuzzy PID with strong logical reasoning ability is combined with the RBF neural network to realize dynamic adjustment of parameters and online control of charging current.The experimental and simulation test results show that the charging control method effectively shortens the charging time,and the charging current curve can better approach the MAS charging curve.The purpose of improving charging efficiency and extending battery life is achieved.

关 键 词：智能充电 RBF神经网络 模糊PID 间歇正负脉冲 铅酸蓄电池 

分 类 号：TM912[电气工程—电力电子与电力传动]