Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer
المؤلفون المشاركون
Zhao, Xuan
Liu, Xiaodong
Ma, Jian
Zhang, Kai
Zhang, Yixi
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-07-16
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life.
This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity.
After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum.
Then the battery model parameters are optimized by IALO algorithm.
As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation.
However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming.
In this paper, noise information is optimized by IALO algorithm.
The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness.
The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation.
After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Kai& Ma, Jian& Zhao, Xuan& Liu, Xiaodong& Zhang, Yixi. 2019. Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1195866
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Kai…[et al.]. Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer. Mathematical Problems in Engineering No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1195866
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Kai& Ma, Jian& Zhao, Xuan& Liu, Xiaodong& Zhang, Yixi. Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1195866
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1195866
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر