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Battery Parameter Estimation Method Based on Simulation Database
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Zhiwei CHEN1, 2, Tingting ZHANG2, Weige ZHANG1, 2, Yanru ZHANG1, 2, Junwei ZHANG1
Journal of Power Supply | 2026, 24(9) : 196 - 204
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Journal of Power Supply | 2026, 24(9): 196-204
• Renewable Energy Generation and Energy Storage •
Battery Parameter Estimation Method Based on Simulation Database
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Zhiwei CHEN1, 2, Tingting ZHANG2, Weige ZHANG1, 2, Yanru ZHANG1, 2, Junwei ZHANG1
Affiliations
  • 1.National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
  • 2.National Key Laboratory of Heavy-haul, Express and High-power Electric Locomotives, Zhuzhou 412000, China
Published: 2026-09-30 doi: 10.13234/j.issn.2095-2805.2026.9.196
Outline
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The inconsistency problem of lithium-ion batteries is one of the key factors affecting the overall performance output of battery packs, and accurate estimation of inconsistency parameters is crucial for the health management and optimization control of battery packs. The traditional complex single battery cell parameter estimation algorithm faces the problem of huge computational complexity when extended to all cells within the battery pack. A method for estimating inconsistent parameters based on an offline single battery cell simulation database is proposed. First, by building an equivalent circuit model to simulate and generate a comprehensive battery sample database. Then, segment voltage characteristic curves are extracted from the target battery charging data and compared with the sample library. Based on similarity measurement, the best matching sample is locked, thereby achieving rapid estimation of the capacity and internal resistance of all battery cells in the group. Subsequently, considering the interference of interpolation errors in the method and voltage sampling errors in practical applications, a correction mechanism based on sliding window search is proposed to improve estimation accuracy. Finally, method validation was conducted on 60 single battery-cell series connected battery packs set up in a laboratory environment. The results showed that the mean absolute percentage error (MAPE) of the capacity did not exceed 0.40%, and MAPE of the internal resistance did not exceed 2.60%, and maintained good stability on simulated sampling error noise data.

Lithium-ion battery  /  inconsistency parameter estimation  /  equivalent-circuit model  /  voltage characteristic curve
陈智伟, 张婷婷, 张维戈, 张言茹, 张珺玮. 电池组不一致性参数估算方法. 电源学报, 2026 , 24 (9) : 196 -204 . DOI: 10.13234/j.issn.2095-2805.2026.9.196
Zhiwei CHEN, Tingting ZHANG, Weige ZHANG, Yanru ZHANG, Junwei ZHANG. Battery Parameter Estimation Method Based on Simulation Database[J]. Journal of Power Supply, 2026 , 24 (9) : 196 -204 . DOI: 10.13234/j.issn.2095-2805.2026.9.196
  • Open Project Program of National Key Laboratory of Heavy-haul, Express and High-power Electric Locomotive(KFKT2022-016)
Year 2026 volume 24 Issue 9
43
7
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.9.196
  • Receive Date:2024-12-29
  • Online Date:2026-10-29
  • Published:2026-09-30
Article Data
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History
  • Received:2024-12-29
  • Revised:2025-02-08
  • Accepted:2025-02-24
Funding
Open Project Program of National Key Laboratory of Heavy-haul, Express and High-power Electric Locomotive(KFKT2022-016)
Affiliations
    1.National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
    2.National Key Laboratory of Heavy-haul, Express and High-power Electric Locomotives, Zhuzhou 412000, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
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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