恒流环境下锂离子电池热参数估计

Thermal parameters estimation of lithium-ion batteries under constant current conditions

基于简化Bernardi模型的锂离子电池热模型包含内阻R、开路电压Uocv、热容mCH及换热系数k等参数。其中,R与Uocv是电池荷电状态(state of charge,SOC)、电流I和电池温度Tbat的非线性函数,mCH与k是常数。由于上述参数所表征电池特性不同,迄今的估计方法往往需要进行多组不同实验以分别激励对应特性,耗时耗力。该文的热模型参数估计方法仅由一组恒电流实验和相应数据处理流程组成。一组多环境温度下的恒电流实验用于在短实验时间内得到足够数据;基于电池机理的实验数据处理流程则解决了参数的非线性特性以及受到的SOC与Tbat耦合变化的影响。与现有方法相比,该方法具有短时间内同时且准确估计出R(SOC,I,Tbat)、Uocv(SOC,Tbat)和常数mCH与k等优点,易于工程实现。实验验证了方法的上述特点。

Internal resistance(R), open circuit voltage(Uocv), heat capacity(mCH) and heat transfer coefficient(k) are key parameters of lithium-ion battery thermal model based on simplified Bernardi equation. R and Uocv are nonlinear functions of state of charge(SOC), current(I) and battery temperature(Tbat) and mCH and k are constants. Since the hidden battery properties behind these parameters are different, existing estimation methods usually conduct different tests to excite corresponding properties respectively, resulting in massive test time and labor. This paper proposes a thermal parameters estimation method only consisting of constant current tests and corresponding data dealing procedures. The constant current tests were conducted under various ambient temperatures to provide necessary data within short test time. The battery mechanism based data dealing procedures consider the parameters’ nonlinear characteristics and solve the SOC and T bat coupling variation problem. Compared with existing method, the proposed method can provide accurate R (SOC,I,Tbat), Uocv (SOC,Tbat), mCH and k estimation results simultaneously within short test time, and thus is easier for engineering application. Test results have verified the advantages.