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Estimation of Lithium-ion Battery SOH Based on SSA-BPNN
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Kaifei ZHANG, Jinlong ZHANG, Manping LÜ
Journal of Power Supply | 2024, 22(5) : 278 - 285
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Journal of Power Supply | 2024, 22(5): 278-285
• Battery and Energy Storage •
Estimation of Lithium-ion Battery SOH Based on SSA-BPNN
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Kaifei ZHANG, Jinlong ZHANG, Manping LÜ
Affiliations
  • Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, School of Electrical Engineering Yanshan University Qinhuangdao 066004 China
Published: 2024-09-30 doi: 10.13234/j.issn.2095-2805.2024.5.278
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Since lithium-ion batteries have been widely applied in energy storage systems and electric vehicles, the accurate estimation of their state-of-health(SOH) is a necessary condition for ensuring the reliable and safe operation of the system. SOH is analyzed from the perspective of capacity, with seven health indicators which are extracted from the constant current-constant voltage charging voltage and temperature curves as input. Based on the data-driven method, a sparrow search algorithm-back propagation neural network(SSA-BPNN) SOH estimation method for lithium-ion batteries is proposed, and data enhancement is applied to further improve the model's robustness. Finally, this method is verified on the NASA Randomized Battery Usage Dataset. Compared with the traditional BP neural network without data enhancement, the SOH estimation accuracy of the proposed method is significantly improved. The maximum absolute error and root mean square error of SOH estimation on the test set are less than 3% and 1.32%, respectively. Experimental results show that this method has advantages of small error, fast convergence, global search capability and adaptation to different characteristics of battery aging.

Lithium-ion battery  /  state-of-health(SOH) estimation  /  data-driven  /  sparrow search algorithm-back prop-agation neural network (SSA-BPNN)  /  data enhancement
Kaifei ZHANG, Jinlong ZHANG, Manping LÜ. Estimation of Lithium-ion Battery SOH Based on SSA-BPNN[J]. Journal of Power Supply, 2024 , 22 (5) : 278 -285 . DOI: 10.13234/j.issn.2095-2805.2024.5.278
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Article Info
doi: 10.13234/j.issn.2095-2805.2024.5.278
  • Receive Date：2021-07-01
  • Online Date：2025-07-20
  • Published：2024-09-30
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History
  • Received：2021-07-01
  • Revised：2021-09-09
  • Accepted：2021-09-16
Affiliations
    Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, School of Electrical Engineering Yanshan University Qinhuangdao 066004 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
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Genus		 种数
Number of
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Percentage of total
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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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