نحوه شارژ باتری یون لیتیوم
ترجمه نشده

نحوه شارژ باتری یون لیتیوم

عنوان فارسی مقاله: تخمین نحوه شارژ باتری یون لیتیوم با استفاده از یک مدل شبکه عصبی بهبود یافته و فیلتر کالمن توسعه یافته
عنوان انگلیسی مقاله: State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter
مجله/کنفرانس: مجله تولید پاک – Journal of Cleaner Production
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: هوش مصنوعی
کلمات کلیدی فارسی: وسایل نقلیه الکتریکی، باتری یون لیتیوم، نحوه شارژ، شبکه عصبی، فیلتر کالمن توسعه یافته، دمای پایین
کلمات کلیدی انگلیسی: Electric vehicles، Lithium-ion battery، State-of-charge، Neural network، Extended Kalman filter، Low temperature
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.jclepro.2019.06.273
دانشگاه: Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China
صفحات مقاله انگلیسی: 12
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 7.096 در سال 2018
شاخص H_index: 150 در سال 2019
شاخص SJR: 1.620 در سال 2018
شناسه ISSN: 0959-6526
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13258
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Neural network for battery modeling

3. Extended Kalman filtering algorithm for SoC estimation

4. Experiments and data preprocessing

5. Verification and discussion

6. Hardware-in-Loop verification

7. Conclusions

Acknowledgments

References

بخشی از مقاله (انگلیسی)

Abstract

Accurate state-of-charge (SoC) estimation is remarkably difficult due to nonlinear characteristics of batteries and complex application environment in electric vehicles (EVs), particularly low temperature and low SoC. In this paper, an improved battery model is first built using a feedforward neural network (FFNN) by introducing newly defined inputs. Based on the FFNN model and the extended Kalman filter algorithm, a FFNN-based SoC estimation method is designed, and its robustness is verified and discussed using the experimental data obtained at different temperatures. Finally, a hardware-in-loop test bench is built to further evaluate the real-time and generalization of the designed FFNN model. The results show that the SoC estimation can converge to the reference value at erroneous settings of an initial SoC error and an initial capacity error, and the SoC estimation errors can be stabilized within 2% after convergence, which applies to all the cases discussed in this paper, including low temperature and low SoC. This indicates that the FFNN-based method is an effective method to estimate SoC accurately in complex EV application environment.

Introduction

With the improvement of performance requirements for electric vehicles (EVs), such as longer driving range and faster speed, more powerful energy sources are needed. At present, lithium ion batteries are the most commonly used energy sources in EVs due to their advantage of higher energy density and longer lifetime than other batteries with different chemistries (Subburaj et al., 2015), but they still cannot provide sufficient energy to drive EVs as far as fossil fuels in traditional vehicles. Under these circumstances, it is crucial to fully utilize the energy stored in batteries for EVs through battery management systems (BMSs). Battery state-of-charge (SoC) estimation is one of the main tasks of BMSs and its accuracy influences performances of other functions in BMSs, including charging control (Di Yin et al., 2016), balancing control (Ma et al., 2018), thermal management (Zhu et al., 2015), and safety management (Xiong et al., 2019a). Due to nonlinear characteristics and complex operation environments of batteries in EVs, it is very difficult to obtain accurate SoC, so a welldesigned SoC estimation method is necessary for any BMSs.