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State of Charge Estimation for Power Battery Base on Improved Particle Filter

التفاصيل البيبلوغرافية
العنوان: State of Charge Estimation for Power Battery Base on Improved Particle Filter
المؤلفون: Xingtao Liu, Xiaojie Fan, Li Wang, Ji Wu
المصدر: World Electric Vehicle Journal, Vol 14, Iss 1, p 8 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Electrical engineering. Electronics. Nuclear engineering
LCC:Transportation engineering
مصطلحات موضوعية: lithium-ion battery, state of charge, particle filter, particle swarm optimization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Transportation engineering, TA1001-1280
الوصف: In this paper, an improved particle filter (Improved Particle Swarm Optimized Particle Filter, IPSO-PF) algorithm is proposed to estimate the state of charge (SOC) of lithium-ion batteries. It solves the problem of inaccurate posterior estimation due to particle degradation. The algorithm divides the particle population into three parts and designs different updating methods to realize self-variation and mutual learning of particles, which effectively promotes global development and avoids falling into local optimum. Firstly, a second-order RC equivalent circuit model is established. Secondly, the model parameters are identified by the particle swarm optimization algorithm. Finally, the proposed algorithm is verified under four different driving conditions. The results show that the root mean square error (RMSE) of the proposed algorithm is within 0.4% under different driving conditions, and the maximum error (ME) is less than 1%, showing good generalization. Compared with the EKF, PF, and PSO-PF algorithms, the IPSO-PF algorithm significantly improves the estimation accuracy of SOC, which verifies the superiority of the proposed algorithm.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2032-6653
Relation: https://www.mdpi.com/2032-6653/14/1/8; https://doaj.org/toc/2032-6653
DOI: 10.3390/wevj14010008
URL الوصول: https://doaj.org/article/3f2e803df07b4774a5ebce89b4e2d2fb
رقم الأكسشن: edsdoj.3f2e803df07b4774a5ebce89b4e2d2fb
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20326653
DOI:10.3390/wevj14010008