不同时间尺度下锂电池SoC估算研究

Research on Lithium-ion Battery State of Charge Estimation Under Different Time Scale

摘要:

针对当前电池荷电状态(SoC)估算算法在处理器运算过程中计算量大,耗费处理器资源多的问题,提出在SoC估算中同时增大辨识时间尺度和估算时间尺度；采用带遗忘因子递推最小二乘算法辨识电池模型参数,并探究不同大小的时间尺度对SoC估算精度的影响；仿真结果表明,随着辨识时间尺度和估算时间尺度增大,SoC估算精度下降且计算量快速下降,计算消耗时间呈指数减少；当时间尺度过大时,SoC估算精度难以保证,计算消耗时间减少不明显,收敛时间长；在综合考虑估算精度和计算耗费时间情况下,可以找到一个最优的时间尺度用于保证SoC估算精度同时大大降低计算量；该方法为后续实现电动汽车SoC在线估算提供理论依据。

Abstract:

In order to solve the problem of heavy calculation burden and large consumption of microcontroller cost that faced in current algorithm of state of charge (SoC) estimation. Increasing time scale of estimation and parameters identification is proposed to estimate SoC. The recursive least squares algorithm with forgetting factor is used to identify battery model and estimate the SoC. The influence of different sizes of time scales for the accuracy of SoC estimation is explored. Simulation results show with the increase of identified time scale and estimated time scale, the accuracy of SoC decrease and calculation burden decline rapidly, the computing time present an exponential decay. An oversize time scale can't guarantee SoC estimation accuracy, the computing time is not decay obviously and the convergence time is long, Comprehensive consideration of estimation accuracy and computing time, an optimal time scale can be achieved which can reduce calculation burden, and ensure accuracy of SoC estimation. The method provides theory basis for subsequent implementation of online SoC estimation in electric vehicles. 