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Research on SOC Estimation of Lithium-ion Battery Based on WOA Optimized Extended Kalman Algorithm
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Aoran XU1, Jing DAI2, Cailian GU1, Xuemin LENG1, Jiahe WEI1
Journal of Power Supply | 2025, 23(2) : 232 - 239
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Journal of Power Supply | 2025, 23(2): 232-239
Battery and Energy Storage
Research on SOC Estimation of Lithium-ion Battery Based on WOA Optimized Extended Kalman Algorithm
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Aoran XU1, Jing DAI2, Cailian GU1, Xuemin LENG1, Jiahe WEI1
Affiliations
  • 1 School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
  • 2 Marketing Service Center, State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang 110136, China
Published: 2025-03-30 doi: 10.13234/j.issn.2095-2805.2025.2.232
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The development of industry and economy has caused a huge consumption of energy, which brings serious energy crisis and environmental pollution. Therefore, building a safe and clean energy interconnection network is a way to solve the relationship among social development, environment and energy at present. Nowadays, different countries have proposed their policies for the development of new energy electric vehicles (EVs). As the core component of EVs, lithium-ion batteries are directly related to the driving performance and safety of EVs. The state-of-charge (SOC) estimation is a core parameter of lithium-ion batteries used in various industries, and the estimation accuracy is directly related to the service life and efficiency of batteries. In this paper, the problem of battery SOC estimation accuracy in EV applications is studied, and an SOC estimation method based on the extended Kalman filter (EKF) optimized by the whale optimization algorithm (WOA) is proposed. On the basis of constructing the covariance matrix of system noise and observation noise, the improved and optimized WOA-EKF algorithm is used to optimize the noise covariance matrix under dynamic conditions, thus improving the SOC estimation accuracy. The model parameter identification and comparative simulation verification are carried out in MATLAB/ Simulink. Results show that the SOC estimation of lithium-ion batteries based on the WOA optimized EKF algorithm can control the SOC estimation error to be within 2% under different working conditions, which is of significance to the promotion of develop- ment of batteries in the new energy field.

Lithium-ion battery  /  state-of-charge (SOC) estimation  /  observation noise  /  whale optimization algorithm-extended Kalman filter (WOA-EKF)
Aoran XU, Jing DAI, Cailian GU, Xuemin LENG, Jiahe WEI. Research on SOC Estimation of Lithium-ion Battery Based on WOA Optimized Extended Kalman Algorithm[J]. Journal of Power Supply, 2025 , 23 (2) : 232 -239 . DOI: 10.13234/j.issn.2095-2805.2025.2.232
  • Liaoning Doctoral Startup Fund(2021-BS-198)
  • Science and Technology 2020 Project of Educational Department of Liaoning Province(JJL-2008)
Year 2025 volume 23 Issue 2
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.2.232
  • Receive Date:2022-04-25
  • Online Date:2025-07-09
  • Published:2025-03-30
Article Data
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History
  • Received:2022-04-25
  • Revised:2022-07-17
  • Accepted:2022-07-19
Funding
Liaoning Doctoral Startup Fund(2021-BS-198)
Science and Technology 2020 Project of Educational Department of Liaoning Province(JJL-2008)
Affiliations
    1 School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
    2 Marketing Service Center, State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang 110136, China
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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
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占总种数比例
Percentage of
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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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