Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Round 1

Reviewer 1 Report

Dear Authors,

Thanks for your submission "Lithium-ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter".
The problem in question is an interesting one. There had been many works that worked on SoC estimation, and it is an ongoing field. 
Please see my comments below:

Language: The English language constructions used in this manuscript appropriate and meets the standard expected in scientific communiuty.

Abstract: It is brief but conveys the points nicely and can make a concerned reader interested.

Introduction is okay -it states the goal and what the mansucript is about in details. The literature review is also okay, but may be expanded bit more to inlcude a bit more from the published works of many other researches. A review papaer was published in MPDI Energies a while back (https://0-www-mdpi-com.brum.beds.ac.uk/1996-1073/12/3/446) -many of these are still relevant, while many had updates. Also, when the authors compared this work to prior published work and presented the contribution of this work  -more details can be added. 
this work is, as per authors, "aiming to further improve the accuracy of battery SOC estimation." The authors may mention a little bit of 'why' this is going to be more accurate, not in details but one/two lines would help.

Section 2 is well written. In line 151-52 "This paper introduces the SA algorithm on the forgetting factor recursive least square algorithm." Are the authors mentioining that the Simulated Annealing is used on the forgetting factor recursive least square algorithm  -for the first time? If so, this statement/something similar should be included in introduction.

Section 3: Gets to the point.

Section 4: Well written, especially the flowchart of Fig 4 is well presented.

Section 5: The verification section is well-presented and conveys the findings in text and graphics appropriately.

Section 6: Conclusion reads well. 

I would like to thank the authors for investing their time and energy in this particular work and preparation of this manuscript.

Sincerely
The Reviewer

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

A lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. Aiming at increasing the accuracy of the estimated parameters. It can be applied in electric energy storage systems, in electric vehicles.

The authors performed a good contextualization and bibliographic review. The methodology was also presented in good detail.

As for form, the authors followed the journal's requirements. I only quote, in lines 249 and 292, not to abbreviate the word Figure.

However, although the results presented are satisfactory in relation to a traditional algorithm, I believe that the discussion of results should be better presented, such as comparing the results with works mentioned in the introduction to the article.

Author Response

Please see the attachment.

Author Response File: Author Response.docx