基于改进粒子滤波的电缆收放车电池RUL预测

RUL prediction of cable retracting vehicle battery based on improved particle filter

为了改善我国矿井下的作业环境,发展新能源矿下运输装备将成为未来发展的必然趋势。而锂离子电池作为目前新能源电动汽车的主要供能来源,随着锂离子电池循环使用次数的增加,锂离子电池容量会呈现逐渐衰退态势。因此为了提高锂离子电池在循环使用过程中的安全性与可靠性,对其剩余使用寿命(Remaining Useful Life,RUL)的预测必不可少。基于电缆收放车的应用背景,选择了与电池老化相匹配的双指数模型来模拟电池使用过程中的容量衰退情况,同时选取了电池容量作为评价电池指标。粒子滤波(Particle Filter,PF)算法作为解决非线性系统参数估计和状态滤波的主流方法,在目标跟踪与预测领域有着广泛的应用。从保证粒子多样性的角度出发,引入蝴蝶优化算法对粒子滤波算法优化,在每一次粒子滤波计算完毕后产生新粒子集。通过对粒子集中的粒子重新赋权,以保证粒子的多样性,从而克服粒子滤波算法中的粒子退化问题,有助于用户根据预测结果做出维修决策。

In order to improve the operating environment of the mine,it will become an inevitable trend for the future development to develop the transportation equipment under the new energy mine.As the main energy supply source of new energy electric vehicles at present,lithium-ion battery capacity will gradually decline with the increase of the number of lithium-ion battery cycles.Therefore,in order to improve the safety and reliability of lithium-ion battery in the process of recycling,it is necessary to predict its Remaining Useful Life(RUL).In this paper,based on the application background of cable retracting and retracting vehicle,a double exponential model matching battery aging was selected to simulate the capacity decline during battery use,and the battery capacity was selected as the evaluation index of battery.Particle filtering(PF)algorithm,as the main method to solve the nonlinear system parameter estimation and state filtering,has a wide range of applications in the field of target tracking and prediction.In order to ensure the diversity of particles,the butterfly optimization algorithm was introduced to optimize the particle filtering algorithm,and a new particle set was generated after each particle filtering calculation.By re-weighting the particles in the particle concentration to ensure the diversity of particles,the particle degradation problem in the particle filtering algorithm can be overcome,and users can make maintenance decisions based on the predicted results.