【作者】 凌珑；

【导师】 俞小莉；

【作者基本信息】 浙江大学 ， 车辆工程， 2021， 硕士

【摘要】 在石油紧缺与环境污染的大背景下,新能源汽车迅速发展,锂离子电池因而得到了广泛应用。为保证电池的安全、高效与长寿命运行,电池管理系统必不可少。SOC作为电池管理系统的关键状态量,对其稳定运行,实现整车能量高效管理、续驶里程精准预测等至关重要。因此,研究SOC估计方法以提高其估计精度具有重要意义。电池动态特性的准确模拟对提高SOC估计精度非常关键。然而,在SOC估计过程中,等效电路模型对电池动态特性的模拟精度通常是变化的。尽管自适应高阶容积卡尔曼滤波(AHCKF)对电池模型模拟的动态特性相比自适应容积卡尔曼滤波(ACKF)具有更高的估计精度,但其结果与电池真实动态特性的偏差可能更大。因此,AHCKF并不是在每个时间步长的SOC估计精度都优于ACKF。针对上述问题,本文探究了ACKF与AHCKF算法在SOC估计过程中,精度差异不断变化的特性。在此基础上,为进一步提高SOC估计的精度,分别设计了基于SOC估计误差绝对值最小以及基于概率的方法来融合ACKF与AHCKF的估计结果。比较后得出:基于概率融合的SOC估计方法更为可靠,且能有效提高SOC估计的精度。进一步,本文探究了SOC估计误差序列长度以及滤波器初始权重对基于概率融合的SOC估计方法的影响规律。结果表明,基于自适应噪声窗口内的误差序列进行滤波器权值更新更为准确。此外,本文还将滤波器初始权重优化问题视为一个一维的多目标优化问题,通过分析选择理想点来构造评价函数,采用黄金分割法和二次函数插值法相结合的方式来寻找最优解。结果显示,组合优化方法能在保证收敛性能的同时,减少寻优所需时间。此外,采用优化后的初始权值对ACKF与AHCKF进行概率融合估计能够进一步降低SOC估计的误差。更多还原

【Abstract】 Against the background of oil shortage and environmental pollution,green cars quickly became popular,and lithium-ion batteries have therefore been widely used.A battery management system(BMS)is essential to ensure the batteries’ safe,efficient,and long-life operation.As a key state of BMS,State of Charge(SOC)is critical for its steady operation,efficient energy management of vehicles,and accurate driving range prediction.Therefore,it is of great significance to study SOC estimation methods to improve its estimation accuracy.The accurate simulation of batteries’ dynamic characteristics is important for improving SOC estimation performance.However,an equivalent circuit model of batteries tends to have changing simulation accuracy of the battery’s dynamic characteristics during SOC estimation.Although the adaptive high-degree cubature Kalman filter(AHCKF)has a more accurate estimation of the dynamic characteristics simulated by the battery model than that of the adaptive cubature Kalman filter(ACKF),AHCKF may have lower estimation accuracy of the real dynamic characteristics of batteries.Therefore,AHCKF does not always outperform ACKF at each step during SOC estimation.In view of the above problem,this paper explored the changing characteristics of the accuracy difference between ACKF and AHCKF during SOC estimation.To improve the accuracy of SOC estimation,a method based on the absolute minimum error of SOC estimation and a probability-based method were designed to combine the estimation results of ACKF and AHCKF,respectively.After comparison,it is concluded that the SOC estimation method based on probabilistic fusion is more robust and can effectively improve the SOC estimation accuracy.Further,this paper explored the influence of the SOC estimation error sequences’ length and the filters’ initial weights on the probabilistic fusion-based SOC estimation method.The results show that it is more accurate to update the filter weights based on the error sequences in the adaptive noise window.Besides,this paper considered the filters’ initial weight optimization as a one-dimensional multi-objective optimization problem.Specifically,the evaluation function was constructed by the ideal point method after comparative analysis.Then,the golden section method and quadratic function interpolation method were combined to find the optimal solution.The combined approach can reduce the time required for optimization while ensuring convergence.The results show that the use of optimized initial weights for probabilistic fusion-based estimation can further decrease the SOC estimation error.更多还原