Round 1

Reviewer 1 Report

 

In the present version of the manuscript, it has a minor contribution, which is not expected to advance the state-of-the-art. Some comments are given as follows:

1.      I believe that your title is not able to highlight the novelty of this research.

2.      The abstract is weak and does not indicate the findings of this study well. The authors also should highlight the main findings obtained from the proposed technique.

3.      The bibliography may be improved, as there are 19 out of 34 references, i.e. over 55% older than 5 years. Also, there are 14 out of 34 references, i.e. over 41% older than 10 years.

4.      The structure of the manuscript is not mentioned in the introduction.

5.      In the introduction, there is no mention of the algorithms used.

6.      In the introduction, the novelty of the paper should be clearly stated.

7.      The parameter values of the meta-heuristic algorithms are unknown. Also, the criteria or references for these values must be specified.

8.      It is better to use the conclusion title alone.

9.      Future research directions should be suggested in the conclusion.

10.  This version needs professional proofreading to address the numerous grammatical errors and writing mechanics.

 

Author Response

Responses to Reviewer 1

In the present version of the manuscript, it has a minor contribution, which is not expected to advance the state-of-the-art.

Thank you for your time spend to revise the article and especially your appreciation of the research scope of your work, we appreciate your careful review which certainly helped us to improve the manuscript.

The contribution of the work is to evaluate, how scientific approaches on the evaluation of the state of health of batteries can be applied on real world batteries. The given case might be particular in the sense that it does not include any battery management system. Still, this also gives the possibility to evaluate gross values. Even though we understand the remark of the reviewer that there is no expected advance of the state-of-the-art or the article, we still think that your work contributes to the question of the application of research in real world systems outside the laboratory environment.

Some comments are given as follows:

1. I believe that your title is not able to highlight the novelty of this research.

We thank the reviewer for this thoughtful remark, as the title of an article is the first impression of a work it must clear and able to highlight the novelty of this research. The novelty in this case is the study of a “in use Lithium-Ion Battery Pack and the goal to “use a black box model to predict EoL”.

 

Therefore, we propose the following title “Aging study of in use Lithium-Ion Battery Packs to predict End of Life using Black Box Model”.

 

2. The abstract is weak and does not indicate the findings of this study well. The authors also should highlight the main findings obtained from the proposed technique.

Thank you for your feedback on the abstract. As the abstract of an article is very visible, it is of highest importance that it is clear and highlights the main findings obtained. With this goal, the abstract was improved as follows:

 

“In order to study the state of health (SOH) of unbalanced battery packs a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are studied, leading to the identification of five key parameters to indicate aging as well as parameters influencing aging. Based on the measurement results a simple black-box model using evolutionary genetic algorithm is presented, which is used as end-of-life prediction model of the battery pack, successfully providing an approximate estimation of aging. This approach might thus be used for the supervision of battery systems during real life use.”

 

3. The bibliography may be improved, as there are 19 out of 34 references, i.e. over 55% older than 5 years. Also, there are 14 out of 34 references, i.e. over 41% older than 10 years.

 

Thank you for the helpful remark. A great number of articles have been published recently and it sometimes is hard to keep track. Still in the following we will be more careful to integrate recent work. The references have been improved using more recent articles that better present the work in the domain, the new reference list can be seen below, with the changed references highlighted in bold.

 

 

[1]	X. Han, M. Ouyang, L. Lu, J. Li, Y. Zheng and Z. Li, "A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification," Journal of Power Sources, vol. 251, p. 38 – 54, April 2014.
[2]	S. Santhanagopalan, Q. Guo, P. Ramadass and R. E. White, "Review of models for predicting the cycling performance of lithium ion batteries," Journal of Power Sources, vol. 156, p. 620–628, June 2006.
[3]	S. a. Nidhra and J. d Dndeti, "Black box and white box testing techniques-a literature review," International Journal of Embedded Systems and Applications (IJESA), p. 29 66 50, 2012.
[4]	T. Wang and Q. Lin, "Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model," Journal of Machine Learning Research, vol. 22, p. 0 – 38, 2021.
[5]	R. Xiong, L. Li and J. Tian, "Towards a smarter battery management system: A critical review on battery state of health monitoring methods," Journal of Power Sources, vol. 405, p. 18–29, November 2018.
[6]	J. J. Hwang, "Sustainable transport strategy for promoting zero-emission electric scooters in Taiwan," Renewable and Sustainable Energy Reviews, vol. 14, p. 1390–1399, June 2010.
[7]	Hacker Motor Shop, Hacker Brushless Motors - TopFuel LiPo 20C Eco X 5000mAh.
[8]	Hacker Motor Shop, Hacer Brushless Motos - iCharger.
[9]	Ministerio para la Transición Energética, Gobierno de España. (s.f.). Agencia Estatal de Meteorología (AEMET).
[10]	X. Han, L. Lu, Y. Zheng, X. Feng, Z. Li, J. Li and M. Ouyang, "A review on the key issues of the lithium ion battery degradation among the whole life cycle," eTransportation, vol. 1, p. 100005, August 2019.
[11]	A. Barai, K. Uddin, W. D. Widanage, A. McGordon and P. Jennings, "A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells," Scientific Reports, vol. 8, January 2018.
[12]	Q. D. H. W. X. a. J. B. Gao, "Impedance Modeling and Aging Research of the Lithium-Ion Batteries Using the EIS Technique," SAE Technical Paper 2019-01-0596, 2019.
[13]	T. Bank, J. Feldmann, S. Klamor, S. Bihn and D. U. Sauer, "Extensive aging analysis of high-power lithium titanate oxide batteries: Impact of the passive electrode effect," Journal of Power Sources, vol. 473, p. 228566, 2020.
[14]	E. Redondo-Iglesias, P. Venet and S. Pelissier, "Global Model for Self-discharge and Capacity Fade in Lithium-ion Batteries Based on the Generalized Eyring Relationship," IEEE Transactions on Vehicular Technology, vol. 67, no. 1, pp. 104-113, 2017.
[15]	S. Bharathraj, A. Kaushik, S. P. Adiga, S. M. Kolake, T. Song and Y. Sung, "Accessing the current limits in lithium ion batteries: Analysis of propensity for unexpected power loss as a function of depth of discharge, temperature and pulse duration," Journal of Power Sources, vol. 494, p. 229723, May 2021.
[16]	C. Savard, E. Iakovleva, D. Ivanchenko and A. Rassolkin, "Accesible Battery Model with Aging Dependency," Energies, vol. 14, p. 3493, 2021.
[17]	O. Erdinc, B. Vural and M. Uzunoglu, "A dynamic lithium-ion battery model considering the effects of temperature and capacity fading," in 2009 International Conference on Clean Electrical Power, 2009.
[18]	N. Yang, X. Zhang, B. Shang and G. Li, "Unbalanced discharging and aging due to temperature differences among the cells in a lithium-ion battery pack with parallel combination," Journal of Power Sources, vol. 306, p. 733–741, February 2016.
[19]	L. Su, J. Zhang, J. Huang, H. Ge, Z. Li, F. Xie and B. Y. Liaw, "Path dependence of lithium ion cells aging under storage conditions," Journal of Power Sources, vol. 315, p. 35–46, May 2016.
[20]	S. Barcellona and L. Piegari, "Effect of current on cycle aging of lithium ion batteries," Journal of Energy Storage, vol. 29, p. 101310, June 2020.
[21]	S.-W. Tan, S.-W. Huang and Y. L. S.-S. Hsieh, "The Estimation Life Cycle of Lithium-Ion Battery Bases on Deep Learning Network and Genetic Algorithm," Energies, no. 14, p. 4423, 2021.
[22]	D. Zhang, B. S. Haran, A. Durairajan, R. E. White, Y. Podrazhansky and B. N. Popov, "Studies on capacity fade of lithium-ion batteries," Journal of Power Sources, vol. 91, p. 122–129, December 2000.
[23]	A. Krupp, E. Ferg, F. Schuldy, K. Derendorf and D. Agert, "Encremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells," Batteries, vol. 7, no. 2, 2021.
[24]	H.-T. Lin, T.-J. Liang and S.-M. Chen, "Estimation of Battery State of Health Using Probabilistic Neural Network," IEEE Transactions on Industrial Informatics, vol. 9, p. 679–685, May 2013.
[25]	M. Luzi, "Design and Implementation of Machine Learning Techniques for Modeling and Managing Battery Energy Storage Systems," 2019.
[26]	S. Tamilselvi, S. Gunasundari, N. Karuppiah, A. Razak RK, S. Madhusudan, V. M. Nagarajan, T. Sathish, M. Z. M. Shamim, C. A. Saleel and A. Afzal, "A Review on Battery Modelling Techniques," Sustainability, vol. 13, 2021.
[27]	R. Poli, J. Kennedy and T. Blackwell, "Particle swarm optimization," Swarm Intelligence, vol. 1, p. 33–57, August 2007.
[28]	J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions," IEEE Transactions on Evolutionary Computation, vol. 10, p. 281–295, June 2006.
[29]	M.-A. T. S. Albadr, M. Ajob and F. Al-Dhief, "Genetic Algorithm Based on Natural Selection Theory of Optimization Problems," Symmetry, vol. 12, p. 1758, 2020.
[30]	M. Dorigo, M. Birattari and T. Stutzle, "Ant colony optimization," IEEE Computational Intelligence Magazine, vol. 1, p. 28–39, November 2006.
[31]	D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of Global Optimization, vol. 39, p. 459–471, April 2007.
[32]	L. Rozaqi, E. Rijanto and S. Kanarachos, "Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study," Journal of Mechatronics, Electrical Power, and Vehicular Technology, vol. 8, no. 1, 2017.
[33]	O. Loyola-Gonzalez, "Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View," IEEE Access, vol. 7, p. 154096–154113, 2019.

 

 

1. The structure of the manuscript is not mentioned in the introduction.

Thank you for your remark. You are right the introduction of the structure of the manuscript has not been mentioned in the original version of the article. This has been corrected.

 

“In the next section the experimental setup is presented. A thorough analysis of measurement results with the goal to identify parameters indicating cell aging is presented in section 3. The black box model based on genetic algorithm (GA) is presented in section 4, it is trained with various sets of data with the goal to predict battery aging. The paper closes with conclusions in section 5.”

 

2. In the introduction, there is no mention of the algorithms used.

We thank the reviewer for this observation, to improve the understanding it is better to mention the algorithms used. The information has been added in the introduction section.

“This project conducts a simplified study of the aging of lithium-ion batteries from a database taken from the real use of 9 Hacker Topfuel Eco-x batteries packs of 5000 mAh and 10 lithium cells [7] used in aeromodelling of aircrafts during the years 2016 to 2021. The objective of the first part of the analysis is to discuss the relationships between the data obtained and to provide an explanation about the performance evolution of these batteries. The aim is to determine which of the parameters analyzed are most decisive in aging. A non-dimensional representation is chosen to harmonize influence of parameters.

Subsequently, different evolution algorithms used to make black box battery models are compared. The implementation of the chosen model is presented, as well as a battery life prediction model design, using genetic algorithm (GA) in Matlab.”

3. In the introduction, the novelty of the paper should be clearly stated.

Thank you for your feedback, that the novelty of the paper is not clearly stated. We rephrased the introduction the introduction carefully to highlight the novelty of the paper more clearly.

 

“This article proposes an innovative approach to study the aging of real use unsupervised battery packs via non-dimensional indicators. This approach, that contributes to the study of battery aging allows to generate an aging prediction window, that is verified experimentally. Therefore, it is interesting to study the aging in small unsupervised battery packs, not only to improve these applications but also to draw some conclusions on battery packs without the influence of BMS.”

 

4. The parameter values of the meta-heuristic algorithms are unknown. Also, the criteria or references for these values must be specified.

We thank the reviewer for this carful review of our work. Even if we carefully described our modelling approach, we agree that some important information are missing. First, we added bibliographic references on the genetic algorithm-based model. Furthermore, we added a section indicating the number of populations and the calculation time in the section as follows.

“To have a proper relationship between shorter computation time and greater model accuracy, a population of 1000 members has been chosen. Considering that the 7 batteries are evaluated the computation time between iterations is approximately 0,2s, using a computer with 8GB RAM and an Intel Core i5-7200U processor. The time it takes the algorithm to find a suitable solution depends on its own evolution and is random. The average duration has been estimated at around 2.6 minutes, with about 750 iterations.”

 

5. It is better to use the conclusion title alone.

We thank the reviewer for his/her suggestion. We changed the title of section 3 to “Conclusions”

 

6. Future research directions should be suggested in the conclusion.

Thank you for this remark, we agree that a perspective of future research must be integrated in a conclusion section and added as future paths, the evaluation of state of health of batteries on bigger data set on systems both with and without BMS with the hope that internal resistance and/or relaxation voltage drop is an interesting and easy to access parameter for the SOH of a battery.

 

“Therefore, the presented method to study battery aging providing both an approach using easily accessible data and a black box model to predict future aging provides a different point of view of battery analysis. In the future the presented approach to identify the aging or batteries bases on internal resistance and/or relaxation voltage drop should be applied to bigger data sets for both batteries with and without BMS to see if it is possible to estimate SOH via those simple to measure parameters.”

 

10. This version needs professional proofreading to address the numerous grammatical errors and writing mechanics.

The article has been thoroughly revised by a native English speaker, who is also expert in the domain of batteries and has thus the double qualification needed to provide correct English in the context of this article.

Author Response File: Author Response.pdf

Reviewer 2 Report

It is interesting to study the aging in small unsupervised battery packs. However, as an article, the paper is still lack of scientificity. The author needs to improve the following contents before the paper was recommended for publication.

 

1. The quality of the pictures in the paper is poor. The text size in the figure is different, and the line width in the figure is inconsistent. The text in the figure is not clear.
2. Methodology section is missing in this paper. If this article is a statistical law, the number of samples is too small.
3. In table I and Figure 20, the selection basis of model input is insufficient. The influence of the ignore capacity loss data on the accuracy of the model needs to be discussed in theory. It is difficult for the author to convince the readers for the reason of "not accurate data".
4. The acquisition of data needs to be described in detail. Although the author has mentioned that "model ''icharger 67 4010 duo'' [8] has been used", physical photos or system block diagrams are not provided.

Author Response

It is interesting to study the aging in small unsupervised battery packs. However, as an article, the paper is still lack of scientificity. The author needs to improve the following contents before the paper was recommended for publication.

We would like to thank the reviewer 2 for his/her review on our article. Your remarks will certainly help to improve the quality of the paper. We are aware that the presented work is a bit outside the common laboratory work. Still, we think that the work has an interesting added value and do our best in order to underline the scientific aspects of the article.

 

1. The quality of the pictures in the paper is poor. The text size in the figure is different, and the line width in the figure is inconsistent. The text in the figure is not clear.

Thank you for this remark. All figures have been revised to improve their quality. As they have all been prepared in the same way, the difference of text size and line within figures is because sometimes we chose to present some figures in smaller versions. However, due to your remarks, we prefer to present all in the same size.

 

2. Methodology section is missing in this paper. If this article is a statistical law, the number of samples is too small.

Thank you very much for this remark. The methodology was two-fold. First, we studied available parameters from battery real uses to identify aging. Second, we used the same data to create a GA black box model to predict future aging and finally studied if the predicted results are in line with the studied battery aging.

The section 1 Introduction has been adapted to show the methodology in more detail:

 

“Lithium-ion batteries are key enablers for the sustainable use of energy. One important open question is to analyze the aging of batteries in real use. Numerous works are conducted in laboratory conditions based on isolated battery cells in order to understand the aging [1] and subsequently models which are capable of predicting aging based on analytical [2], black box [3] or hybrid are proposed [4]. These might be valid for single cell applications like cell phones in well-defined conditions. However, for most applications battery packs with multiple cells in series and parallel supervised by a battery management system (BMS) are used. For these kinds of applications, the aging might be more difficult to track due to the influence of BMS and therefore less studies are known [5]. Still, there are quite a few applications using small unsupervised battery modules of some cells in series without supervision by a BMS. This kind of application can be found not only in model aircrafts as in the presented study but also in small mobility solutions like scooters [6]. This article proposes an innovative approach to study the aging of real use unsupervised battery packs via non-dimensional indicators. This approach, that contributes to the study of battery aging allows to generate an aging prediction window, that is verified experimentally.

This project conducts a simplified study of the aging of lithium-ion batteries from a database taken from the real use of 9 Hacker Topfuel Eco-x batteries packs of 5000 mAh and 10 lithium cells [7] used in aeromodelling of aircrafts during the years 2016 to 2021. The objective of the first part of the analysis is to discuss the relationships between the data obtained and to provide an explanation about the performance evolution of these batteries. The aim is to determine which of the parameters analyzed are most decisive in aging. A non-dimensional representation is chosen to harmonize influence of parameters.

Subsequently, different evolution algorithms used to make black box battery models are compared. The implementation of the chosen model is presented, as well as a battery life prediction model design, using genetic algorithm (GA) in MATLAB The purpose of the project is to explain a procedure for studying and modelling the performance of small unsupervised battery packs. Although the data shows trends conform to the theory, the results still must be discussed carefully, as the measurements are done in real use and not in laboratory conditions.

In the next section the experimental setup is presented. A thorough analysis of measurement results with the goal to identify parameters indicating cell aging is presented in section 3. The black box model based on GA is presented in section 4, it is trained with various sets of data with the goal to predict battery aging. The paper closes with conclusions in section 5.”

 

Regarding the amount of data for statistical analysis, we agree that more data is always better. Still, we were very happy to find a user, which is not a trained scientific, who tracks his batteries so closely over their entire lifetime and who was willing to share them for the study. In total we had information about 9 batteries with 10 cells each over a duration of five years. Even though the amount of data is still limited, we already found it very interesting to be able to study them in the scope to trace their SOH evolution. Still, for future work we would have preferred to have additional information like the temperature or a more precise tracing of internal references.

 

3. In table I and Figure 20, the selection basis of model input is insufficient. The influence of the ignore capacity loss data on the accuracy of the model needs to be discussed in theory. It is difficult for the author to convince the readers for the reason of "not accurate data".

We thank the reviewer for his/her very helpful remark.

For us this study was the first of the kind of analysis and in future work we will for sure be more rigorous regarding the criteria of the parameters that will be used for the model. Still, we know that batteries will have to be tracked during their entire life and a certain number of parameters must be stored for possible future use. Still even though this will be in the domain of big data, we are not convinced that all use data can be sort. Therefore, the study must be considered as first attempt to identify easily accessible SOH indicators.

Still, doing an analysis with bigger data sets, will help to select the model basis more carefully. As clearly stated in the article, the internal resistance, which was not easily measured seems proportional to the relaxation voltage loss. Even though, it has been neglected in this study this parameter might be interesting to study.

Regarding the capacity loss, the chosen approach using a floating scope of ∆V is unusual, known works normally consider the capacity on fixed voltage ranges. For example using the incremental capacity method [22]. Therefore, the study of this part has not been put forward in this case.

[22]	D. Zhang, B. S. Haran, A. Durairajan, R. E. White, Y. Podrazhansky and B. N. Popov, "Studies on capacity fade of lithium-ion batteries," Journal of Power Sources, vol. 91, p. 122–129, December 2000.

 

 

4. The acquisition of data needs to be described in detail. Although the author has mentioned that "model ''icharger 67 4010 duo'' [8] has been used", physical photos or system block diagrams are not provided.

Thank you for this remark. We added a photo and some technical data of the charger in the description.

To obtain the data related to the charging processes an intelligent charger, model ''icharger 4010 duo'' [8] has been used (Figure 1). This charger can charge two batteries in parallel with a maximum power of 2000W (max 1400W per channel) and has some integrated options like the measurement of the voltage with a precision of 1mV and the internal resistance with a precision of 0.1Ω. All values can be saved on a Micro SD card.

Figure 1: icharger 4010 duo [8]

 

Finally, we sincerely hope that we have managed to address the required revision both in this document and the revised document and our article is now fit for publication.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The responses to the Reviewer's comments have been checked. Generally speaking, the authors have presented a better revision and given some reasonable explanations.
This version needs professional proofreading to address the grammatical errors and writing mechanics

Author Response

Responses to Reviewer 1

The responses to the Reviewer's comments have been checked. Generally speaking, the authors have presented a better revision and given some reasonable explanations.

Thank you for your time spend to revise the article and especially your appreciation of the research scope of your work, we appreciate your careful review which certainly helped us to improve the manuscript.

This version needs professional proofreading to address the grammatical errors and writing mechanics.

The revised article has been thoroughly updated. I am the corresponding author of the article and I have 20+ years experience in scientific work in English including the writing and editing of numerous research reports and scientific articles. Until now my English level has always been fine. Moreover, Sudnya Vaidya, one of the co-authors of the article, is native English speaker also experienced in writing scientific articles and thoroughly revised both versions of the article using on top a recognized English language software.

Maybe some phrases are a bit long, but I cannot see major issues requiring professional proofreading. Still, I have double checked with MDPI, before the article is published it is cross checked by an English editing team, which will resolve smaller issues.

Author Response File: Author Response.pdf

Reviewer 2 Report

Although the paper has some improvements, it is still lack of innovation.  Furthermore, the quality of the figures is obviously lower than that of the published papers in this journal.

Author Response

Responses to Reviewer 2

Although the paper has some improvements, it is still lack of innovation. 

We would like to thank the reviewer 2 for his/her review on our article. Your remarks will certainly help to improve the quality of the paper.

We can understand the remark of the reviewer to some extend. Maybe he considers the fact that we do not present another mathematical approach for estimation or modelling as lack of innovation. Still, we invite the reviewer to consider the two following aspects as contribution to innovation:

1. We were able to gather real life data of battery aging over several years. The use was not influenced by research interest whatsoever. Still the user noted down all the presented values. So, what we present are not realistic but real data. To your knowledge the number of scientific articles showing battery aging bases on real data is still very limited and our work presents an interesting contribution.
2. The prediction of RUL (remaining useful life) is an important aspect to reinforce confidence of users in lithium-ion batteries and therefore their adaption on the market. The presented black box model, shows that a quite simple model, that needs limited amount of memory while running in real time, can already provide a robust prediction of the RUL.

Lines 615ff: “The presented black box model thus showed that a rather simple model can give a good prediction of the remaining useful life (RUL) of a battery integrating the changes throughout the battery life. Moreover, this model needs a limited amount of memory and can still run in real time. It would be interesting to integrate such a model a in the charger or vehicle software.”

Lines 637ff: “That might on a battery charger or vehicle software showing a prediction of RUL integrating measurement throughout battery life.”

Lines 649f: “and integrate a RUL estimation in user software.”

We are sorry if these aspects have not been clear in the paper. We applied some changes to highlight the innovative aspect of the work hoping that they become more evident for the reviewer and the readers.

Author Response File: Author Response.pdf