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Improvement of SOC Estimation Method and On-Line Monitoring for Lithium-Ion Batteries Based on Single-Particle Li Diffusion Model
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Mafeng Tao1, Jiankun Zhao1, Naixing Yang1, 2, Yunxiao Zhuang1, Gaofan Zhang1
Automobile Technology | 2024, (2) : 33 - 38
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Automobile Technology | 2024, (2): 33-38
Improvement of SOC Estimation Method and On-Line Monitoring for Lithium-Ion Batteries Based on Single-Particle Li Diffusion Model
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Mafeng Tao1, Jiankun Zhao1, Naixing Yang1, 2, Yunxiao Zhuang1, Gaofan Zhang1
Affiliations
  • 1 School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055
  • 2 Shaanxi Key Laboratory of Nano-Materials and Technology, Xi’an University of Architecture and Technology, Xi’an 710055
Published: 2024-02-24 doi: 10.19620/j.cnki.1000-3703.20230354
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In order to improve the prediction accuracy of State Of Charge (SOC) of power batteries, this paper proposed a method to improve the traditional ampere-hour intergral method to estimate the SOC of the lithium-ion battery based on a single particle Li diffusion model. The solver programs for estimating SOC with traditional and improved ampere-hour integration method and communication interface programs for battery data acquisition were written in software LabVIEW, which achieved the on-line monitoring of the battery SOC under different environmental temperatures and currents by the two methods. The results show that, under the above discharging conditions, the maximum estimation errors of the improved method and the traditional ampere-hour integral method are 1.11% and 1.89%, respectively. When the discharge current changes dramatically, the battery SOC estimated by the improved method fluctuates less than the traditional ampere-hour integral method.

Lithium-ion batteries  /  State Of Charge (SOC)  /  Diffusion model  /  Ampere-hour integration method
Mafeng Tao, Jiankun Zhao, Naixing Yang, Yunxiao Zhuang, Gaofan Zhang. Improvement of SOC Estimation Method and On-Line Monitoring for Lithium-Ion Batteries Based on Single-Particle Li Diffusion Model[J]. Automobile Technology, 2024 , (2) : 33 -38 . DOI: 10.19620/j.cnki.1000-3703.20230354
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doi: 10.19620/j.cnki.1000-3703.20230354
  • Online Date：2025-12-25
  • Published：2024-02-24
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    1 School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055
    2 Shaanxi Key Laboratory of Nano-Materials and Technology, Xi’an University of Architecture and Technology, Xi’an 710055
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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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