Peer review of: Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using mac (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author):  

The manuscript applies machine learning to a large EIS dataset for commercial lithium-ion batteries 
to predict cycle number and the remaining useful life (RUL). In summary, this manuscript has some 
highly significant results that was worthy of publication, but should be revised to be more precise 
and accurate in the wording of the text. Below are more details comments to this effect, as well as 
pointing out a few other minor fixes to make. 


1. A verb is missing near the beginning of the last sentence on page 2. 
 

2. The manuscript claims that _no_ knowledge of the cycling temperature is needed to predict RUL, 
which is not true since some assumption must be made about the past and future temperatures, 
e.g., that they are the same and are constant. RUL is a very strong function of temperature, so any 
prediction that makes _no_ assumptions on the future temperature will be wildly inaccuate. For 
example, if a model predicts at cycle 50 that a RUL of 1000 based on data collected at 25 C, then the 
true RUL would be < 100 if the true future temperature was 55 C. 

This issue can be fixed by precisely stating the assumption on the future temperature being implicitly 
made, for example, that future operating temperatures will be close to the past operating 
temperatures. 

 

3. The manuscript should more clearly state the limitation of the study that all batteries are charged 
and discharged in exactly the same way (shown in Figure 1) and exactly the same temperature 
within each group of batteries (Fig. 4). As such, the machine learned model is shown to be accurate 
only for batteries that are charged and discharged in exactly the same way and exactly the same 
temperature in the past, and it is very unlikely that the machine learned model will make good 
predictions when the charging and discharging profiles are changed such as what happens in a 
hybrid electric vehicle, or the discharge changes substantially in an electric vehicle, or when the 
temperature varies. 

 

While I agree that the results are a significant advance worthy of publication in this journal, 
the manuscript should be more precise and complete in stating the limitations of the study. Given 
the limitations, many of the claims are too broad as stated, e.g., in the last line of Section V or the 
second to last line of page 9. The significant differences between what is shown in the study (perfect 
charging and discharging profiles for all future and past time, and perfect or nearly perfect 
temperature at a constant value for all future and past time) means that it is too premature to try to 
argue that the work "paves the way towards a new paradigm in the design of battery management 
systems". "Paving the way" is to strong of a statement, a more accurate statement would be "shows 
the potential value of GPR-based methods in the design of battery management systems" 



4. In the first full sentence of text below the Figure 2 caption, insert "for these batteries" between 
"degradation" and "[37]", as the statement does not hold for all batteries. 

 

5. The anti-heuristics statement at the end of page 9 that goes onto page 10 is an opinion that is not 
supported by evidence given in this manuscript. In no way do the results of the manuscript show 
that heuristics is not a valid or even better performing approach. Further, the results do not even 
compare with heuristics and so any sort of claim cannot be made with regard to the merits or 
demerits of heuristics. 

 

Furthermore, the relating of heuristic as a way to narrow down features is not a valid comparison to 
the availability of information-rich data, that is, there is no "either or" that can logically be made 
between the use of heuristics and the use of information-rich data. For example, no evidence is 
provided that indicates the proposed approach gives better results than the application of a clever 
heuristic to the same information-rich dataset. [The example of a valid statement is that EIS contains 
valuable information on cycle number and RUL prediction.] 

 

Third, what constitutes being "heuristic" is rather subjective, and statements in high-quality journals 
should not be based on matters of opinion. As an example, some first-principles battery experts 
would consider all of the methods in the manuscript to be based on "using heuristics to narrow 
down input features"---exactly the approach being demonized by the authors---because the 
impedences are trivial examples of features (features are transformations of raw data, the 
impedence data is a transformation of the raw data, and the manuscript explictly states that the 
impedences are features in lines 6 to 7 up from the bottom of page 6), the method proposed in the 
manuscript narrows down those features (e.g., see Fig. 3), and the approach does not employ first-
principles models--arguably making the approach empirical aka heuristic rather than first-principles. 

 

6. As another example of an overstated claim, the introduction states that "Our method accurately 
estimates the cycle number and RUL of a battery, at any point of its life, from a single impedance 
spectrum measurement, 

without any knowledge of the cycling temperature and SoC", which is not technically correct since 
some knowledge of the cycling temperature is used in the method, namely, that the temperature of 
all cycling in the past and future is exactly (or nearly exactly) the same for the batteries in which 
estimation of cycle number and RUL is being made. 

 

7. The Conclusions also make too strong of statements, that is, statements are made that are not 
adequately supported by the results in the manuscript. For example, the first sentence of the 
Conclusions is stated as being generally true when it is only true within the context of the limitations 
of the study. The second sentence is actually not correct technically, as the method does use 
knowledge of the cycling temperature, namely, that the past and future cycling temperature is the 
same constant value for all cycles. 

 

The comment on interpretability is also too strong. Noting that the low-frequency region is the most 
predictive is not really an insight, but is rather an observation. An example of insight is that you 
would be able to confidentially learn something about the underlying mechanisms that reduce the 
capacity from applying the method. The manuscript cannot claim to be the first to show that 
identification of frequencies that change in EIS provides insight into battery degradation, since such 
results have already been established in the literature, e.g., [22] and do not require use of GPR or 
even machine learning. (Note that the claim that it is "challenging to pick out quantitative features 
correlated with degradation -- made in the introduction -- is overstated since someone could do 
such picking by just applying lasso or elastic net, which have already been applied to predicting RUL 
in Li-ion batteries.) 

 

The last sentence of the conclusion is not supported by the manuscript, in that no evidence is 
provided that the method if broadly applicable, and the claim regarding "unique features" is 
overstated in that EIS and machine learning methods have been used before. Instead of focusing so 
much on salespeak, instead write the results in a precise and accurate manner and, if the results are 
strong enough (and my opinion is that the results are strong enough), then the results can be 
published while informing the reader in a technically accurate and precise way. 

 

8. Each author should go through the manuscript in some detail and ask which statements are 100% 
technically accurate and supported by data--as stated--and then revise the sentences that do not 
satisfy those conditions. 

 

9. Guassian processes have been applied to lithium-ion battery RUL prediction problems, but could 
not find the text in the manuscript that explains how the manuscript's use of Guassian processes 
relates to the past applications in the literature. I believe that the specific implementation of 
Gaussian processes as done in the manuscript has not been applied to lithium-ion batteries, but that 
review of closely related literature on other ways to apply Gaussian processes to lithium-ion 
batteries is needed for the readers (and reviewers) to make a precise understanding of the 
differences in the applications. 

 

9. The term "magic frequencies" on page 6 is misleading. While machine learning methods that do 
feature selection can often pick a small number of features that are able to be used to make 
predictions, typically the features are very highly correlated, in which the features selected by the 
machine learning method are not necessary significantly better than other features. Frequency data 
such as in the manuscript are especially highly correlated, so this point applies. When applied to 
frequency data, typically a model built on taking frequencies close to the selected frequencies gives 
prediction accuracies that are indistinguishable from the originally selected features -- when applied 
to a rigorous cross validation (e.g., nested). As such, there is nothing "magic" about the originally 
selected features. 


The paragraph at the end of page 6 that goes onto page 7 refers to intepretability being enabled by 
the method, but the evidence that is provided is not convincing. Someone could make the same 
observation on mechanism made in the first two lines of text below the Fig. 2 capture by a quick 
inspection of the EIS curves as a function of cycle number. 

 


Reviewer #2 (Remarks to the Author): 


This paper makes a good-faith attempt to adopt machine learning based interpretation of EIS data 
for prediction of remaining useful life (RUL) in Li-ion batteries. While the effort is duly noted, the 
following shortcomings preclude further consideration for publication of this work in this high-
impact journal. 

 

- Failure of lithium-ion batteries is a non-linear process and makes prediction of battery life and state 
of health a difficult task. In this study, there is only one set of data being used as a training set at a 
given condition. This makes the method more of a fitting than machine learning. This seems to be 
the case that the authors have just one cell for training and one cell for prediction at 3 different 
temperatures. Even at same cycling conditions, cells tend to fail at a different cycle number due to 
the inherent non-linearity of the degradation processes in the system. So just using two cells for a 
condition with one as a training set and other for prediction can at best be a glorified curve fitting 
than machine learning. Not to mention, the authors try to identify the cycle number rather than the 
capacity retention. 



- It is not clear why choosing the cycle number as the appropriate output predictor rather than the 
capacity retention or the state of health. Does this study assume that the failure of all lithium-ion 
batteries can be similar at a given condition? If so justify the assumption. Elaborate on the logic for 
choosing cycle number as the output to be predicted. 

 
- Although the coefficients of fit are high enough, it needs to be noted that not enough datasets are 
used to realistically calculate the coefficient. So even if the model is really good, the study does not 
have enough data to validate the same. 



- The capacity retention curves of the training set as well as the predictor set should be provided 
(e.g., in the supplementary information).