基于自适应遗传算法的蓄电池充电优化策略

Optimization Strategy of Battery Charging Based on Adaptive Genetic Algorithm

为了提高蓄电池充电质量,针对蓄电池充电过程的复杂多变性以及传统模糊算法中模糊控制规则、量化因子和比例因子对专家经验的过度依赖,以及单神经元自适应算法中神经元比例系数的选择对系统稳定性影响等问题,将自适应遗传算法与改进的PID算法相结合,提出了一种自适应遗传PID算法用于蓄电池的充电优化。根据蓄电池的充电特性建立蓄电池充电等效模型,对蓄电池的电压环和电流环分别进行了仿真。仿真结果表明,相对于对比算法,该算法控制下的充电系统具有更短的上升时间和调整时间、更小的超调量、更好的动态响应性能。

In order to improve the quality of battery charging,an adaptive genetic PID control method is put forward for battery charging optimization by combining adaptive genetic algorithm and improved PID algorithm to solve the complex variability of battery charging process,the excessive dependence of fuzzy control rules,quantitative factors and proportional factors in traditional fuzzy algorithms on expert experience,and the influence of the selection of neuron proportional coefficient in single neuron adaptive algorithm on system stability.According to the charging characteristics,the equivalent model of battery charging is established.Voltage loop and current loop of charging system are simulated respectively.The results show that,compared with other algorithms,the proposed algorithm is better in rising time,overshoot and adjustment time,which has better dynamic performance.