基于全新等效电路模型的电池关键状态在线联合估计器
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作者:
作者单位:

1.天津工业大学;2.华晨宝马汽车有限公司

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通讯作者:

中图分类号:

TM912

基金项目:

国家重点研发计划 (2021YFB2501800). 天津市研究生科研创新项目 (2021YJSS065). 国家自然基金(61802280,61806143, 61772365, 41772123). 天津市自然科学基金 (18JCQNJC77200).


Online Joint Estimator of Battery Key States based on a New Equivalent Circuit Model
Author:
Affiliation:

1.TianGong University;2.BMW Brilliance Automotive

Fund Project:

National Key R&D Program of China 2021YFB2501800;Tianjin Research Innovation Project for Postgraduate Students 2021YJSS065;National Natural Science Foundation of China 61802280,61806143, 61772365, 41772123;Tianjin Natural Science Foundation 18JCQNJC77200;

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    摘要:

    本文针对电池三大关键状态 (State of Charge--SOC、State of Health--SOH、State of Power--SOP) 之间相互耦合的关系,同时考虑到其估计精度受到电池时变的内部参数等因素影响的问题,提出了一种基于自回归等效电路模型 (Autoregression Equivalent Circuit Model--AR-ECM) 的电池关键状态在线联合估计算法。该方法提出基于AR模型的全新电池ECM,并给出同时表征SOC、SOH和电池内部压降的状态空间方程以及区别化参数更新策略。在此基础上,考虑状态方程容易发生不正定的问题,提出采用平方根无迹卡尔曼滤波 (Square Root Unscent Kalman Filter--SR-UKF) 算法实现电池状态的联合估计。此算法的优势在于真正实现了电池关键状态以及ECM参数的联合估计,更符合实际工程应用需求。仿真验证表明,在噪声干扰环境下,该联合估计器能得到较高的精确度和稳定性。

    Abstract:

    Aiming at the coupling relationship between the three key states of battery (state of charge -- SOC, state of health -- SOH, state of power -- SOP), and considering that their estimation accuracy is affected by the time-varying internal parameters of battery and other factors, an online joint estimation algorithm of battery key states based on Autoregressive equivalent circuit model (AR-ECM) is proposed. The method proposes a new battery ECM based on the AR model, and gives the state space equation that characterizes the SOC,SOH and internal voltage drop of the battery simultaneously, as well as the differentiated parameter updating strategy. Based on this, considering the problem that the state equation is prone to non-positive definiteness, the Square Root Unscented Kalman Filter (SR-UKF) algorithm is used to achieve the joint estimation of the battery states. The advantage of this algorithm is that it truly achieves the joint estimation of the key battery states and ECM parameters, which is more in line with the practical engineering application requirements. The simulation verification shows that the joint estimator can obtain high accuracy and stability under noise disturbance.

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  • 收稿日期:2021-10-21
  • 最后修改日期:2022-02-25
  • 录用日期:2022-01-19
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