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Adaptive Multistate Combined Estimation for Lithium-Ion Battery at Different Temperatures
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Zhongwei Wang1, Kun Yang1, Chao Ma1, Jilei Wang2, Jie Wang1
Automobile Technology | 2025, (4) : 20 - 31
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Automobile Technology | 2025, (4): 20-31
Adaptive Multistate Combined Estimation for Lithium-Ion Battery at Different Temperatures
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Zhongwei Wang1, Kun Yang1, Chao Ma1, Jilei Wang2, Jie Wang1
Affiliations
  • 1 Shandong University of Technology, Zibo 255000
  • 2 Changchun Automotive Test Center Co., Ltd., Changchun 130013
Published: 2025-04-24 doi: 10.19620/j.cnki.1000-3703.20231131
Outline
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In order to accurately estimate the battery parameters, state of charge and power state at different temperatures, a recursive least squares method combined with adaptive extended Kalman filter algorithm based on adaptive forgetting factor is proposed. By correcting and updating parameters in real time, the accuracy of battery parameter identification and state-of-charge estimation is improved. Based on the constraints of the model terminal voltage identification results, the state-of-charge estimation results and the maximum discharge current of the battery, the joint estimation of the battery power state is realized. The test results show that the maximum absolute error of the identification voltage and the maximum absolute error of the state of charge are 62.699 mV and 1.894%, respectively under the dynamic stress test condition. When the continuous discharge time is 5 s, 30 s and 120 s, the average error of battery power is 5.6×10-3 W, 6.5×10-3 W and 8.0×10-3 W, respectively. The proposed adaptive joint estimation algorithm can improve the accuracy of parameter identification and state estimation effectively.

Lithium-ion battery  /  Adaptive Forgetting Factor Recursive Least Squares  /  Adaptive Extended Kalman Filter  /  Online parameter identification  /  Combined estimation
Zhongwei Wang, Kun Yang, Chao Ma, Jilei Wang, Jie Wang. Adaptive Multistate Combined Estimation for Lithium-Ion Battery at Different Temperatures[J]. Automobile Technology, 2025 , (4) : 20 -31 . DOI: 10.19620/j.cnki.1000-3703.20231131
Year 2025 volume Issue 4
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doi: 10.19620/j.cnki.1000-3703.20231131
  • Online Date:2025-11-15
  • Published:2025-04-24
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  • Revised:2024-03-11
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    1 Shandong University of Technology, Zibo 255000
    2 Changchun Automotive Test Center Co., Ltd., Changchun 130013
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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