Power simulation of lithium ion battery through battery model is of great significance for dynamic response simulation, heat generation calculation and charge-discharge strategy development. The accuracy and applicability of the model become crucial. In order to demonstrate the battery transient characteristics more effectively, a novel identification method for parameters of the 2nd order RC equivalent circuit model was proposed. Based on the derived evolution law of battery transient characteristics under the continuous pulse excitation, four feature points are extracted for parameter identification in each cycle. The proposed method reduced the time cost of identification from 11796.88s to 0.06s while ensuring that the error of voltage doesn’t exceed 2.2mV. In order to verify the power profiles applicability of the proposed method, applicability analysis of power profile for different identification methods was carried out including the methods using different amount of data (4N points, 200 points, 6000 points) under unidirectional current pulse excitation (UCPE), bidirectional current pulse excitation (BCPE) and unidirectional voltage pulse excitation (UVPE). It was illustrated that the identification process using data of multiple cycles could significantly reduce errors, including maximum error and average error. What’s more, the proposed method under UCPE had the lowest maximum error of 0.420% in voltage simulation and −0.421% in the current simulation of power profiles. Compared with the conventional method (using 200 points of single pulse data for parameter identification), the proposed method can reduce the average voltage error and the maximum error by 62.5% and 11.8% respectively under the DST power profile.

中文翻译：

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基于瞬态特性数据的电动汽车功率分布锂离子电池模型参数辨识方法研究

通过电池模型对锂离子电池进行功率模拟，对于动态响应模拟、发热计算和充放电策略制定具有重要意义。模型的准确性和适用性变得至关重要。为了更有效地展示电池瞬态特性，提出了一种新的二阶RC等效电路模型参数辨识方法。基于推导的连续脉冲激励下电池瞬态特性演化规律，提取4个特征点用于每个循环的参数识别。该方法在保证电压误差不超过2.2mV的情况下，将识别时间成本从11796.88s降低到0.06s。为了验证所提出方法的功率分布适用性，对不同数据量（4N点、200点、6000点）在单向电流脉冲激励（UCPE）、双向电流脉冲激励（BCPE）和单向电压脉冲下的不同识别方法的功率分布进行了适用性分析激发（UVPE）。说明使用多周期数据的识别过程可以显着减少误差，包括最大误差和平均误差。更重要的是，UCPE 下提出的方法在电压模拟中的最大误差最低为 0.420%，在功率分布的电流模拟中为 -0.421%。与传统方法（使用200个单脉冲数据进行参数辨识）相比，该方法平均电压误差和最大误差分别降低了62.5%和11%。