| 引用本文: | 赵志康,钟 勇,武 强,范周慧,邱煌乐.宽温度下基于 AFFRLS-ASECKF 联合仿真的锂离子电池 SOC 估计(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（5）：114-123 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 由于锂离子电池的容量受温度影响较为严重,为了确保其在宽温度下安全高效的运行,需要准确估计
其宽温度范围内的荷电状态(SOC)。 方法 对宽温度范围内锂离子电池的各项特性进行分析,然后基于二阶 RC 等
效电路模型,搭建具有温度补偿的电池模型,并采用自适应遗忘因子递推最小二乘法(AFFRLS)对电池模型进行在
线参数辨识,同时结合自适应平方根容积卡尔曼滤波(ASRCKF)算法对宽温度范围内的电池在不同工况下进行
SOC 估计,并与容积卡尔曼滤波算法的 SOC 估计结果进行对比。 结果 AFFRLS-ASRCKF 联合算法的收敛速度更
快,收敛误差更小,在两种不同的工况下,宽温度范围内 AFFRLS -ASRCKF 联合算法的均方根误差均能保持在
0. 4%以内,表明 AFFRLS-ASRCKF 算法收敛性好,精度高,鲁棒性更好。 结论 该联合算法满足在宽温度下锂离子
电池 SOC 的估算精度,为锂离子电池安全高效运行提供了保障,并且可以进一步在实际电池组中研究因单体状态
不一致、单体电池之间相互影响或者电池组散热吸热等因素导致的电池内外温度发生变化从而影响电池组 SOC
估计的现象。 |
| 关键词: 锂离子电池 荷电状态 在线参数辨识 自适应平方根容积卡尔曼滤波 |
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| Lithium-ion Battery State of Charge Estimation through Joint Simulation with AFFRLS-ASECKF in WideTemperature Range |
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ZHAO Zhikang ZHONG Yong WU Qiang FAN Zhouhui QIU Huangle
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Fujian Provincial Key Laboratory of Automotive Electronic Drive Technology Fujian University of Technology Fuzhou
350118 China
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| Abstract: |
| Objective Due to the substantial impact of temperature on the capacity of lithium-ion batteries achieving
accurate state of charge SOC estimation across a wide temperature range is pivotal for ensuring the secure and efficient
operation of lithium-ion batteries. Methods The characteristics of lithium-ion batteries across a wide temperature range
were analyzed. Based on a second-order RC equivalent circuit model a temperature-compensated battery model was
established. The adaptive forgetting factor recursive least squares AFFRLS method was used for online parameter
identification of the battery model. Additionally the adaptive square root cubic Kalman filter ASRCKF algorithm was
employed for SOC estimation under different operating conditions across a wide temperature range and the results were
compared with those obtained using the cubic Kalman filter CKF algorithm. Results The AFFRLS-ASRCKF joint
algorithm showed faster convergence speed and smaller convergence error. In both operating conditions the root mean
square error of the SOC estimation using the AFFRLS-ASRCKF joint algorithm remained within 0. 4%. This indicated that
the AFFRLS-ASRCKF algorithm has good convergence high accuracy and better robustness. Conclusion The joint
algorithm meets the estimation accuracy of the state of charge SOC of lithium-ion batteries over a wide temperature
range providing a guarantee for the safe and efficient operation of lithium-ion batteries. In addition this algorithm can
be further applied to the research of actual battery packs taking into account the influence of internal and external
temperature changes on the state-of-charge SOC estimation of the battery pack. These temperature changes are caused
by factors such as the inconsistent states of individual cells the mutual influence between individual cells and the heat
dissipation and absorption of the battery pack. |
| Key words: lithium-ion battery state of charge SOC online parameter identification adaptive square root cubic
Kalman filter |
