Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

Round 1

Reviewer 1 Report

Review for sustainability-752122:

„Research on factors that influence the fast charging behavior of private Battery Electric Vehicles“

The direction of Electric vehicles (EV) is a very hot theme that leads me a decision that I will review for this paper. This direction should be researched and developed more and more. In this review, it can be seen that,

(1) Fossil energy will cannot supply for human demand forever. The direction of renewable energy is correct, but all of them is not always easy and feasible to do. For example, solar module is clean, but the manufacture technology is not clean. The total cost for applications, manufactures and treating for our environment is not cheap for this. Wind energy is clean, but is not stable and dependent. Others are complicated or potential is not high enough.

(2) The other direction is saving energy for electric devices or/and their operation modes. Or the other direction is the replacement of equipment/vehicles using fossil energy by the ones using electrical energy (EVs). On the one side, they can apply electrical energy that can be manufactured by clean energy. On the other side, their CO2 emission is zero to reduce environment pollution. Other advantages of noise reduction, start time or efficiency are also interesting issues.

(3) There are two type of EVs including central EVs (electrical trams, or high speed electrical trains  ...etc.) and decentral EVs (electric car, motorbike, bike…etc.). In central EV systems, electrical generators stay at a place and the cable must bring electrical energy to EVs. Whereas, in decentral EV systems, electrical energy must be brought on the EVs in their operation. Herewith, there are three types of energy carrier including (1) pure battery (electrical energy store), (2) generator (other energy types are converted directly into electrical energy for EVs in their operation) and (3) hybrid type (both electrical energy store and generator).

(4) In the above types, it is dependent on the range of power (low, middle and high power), work area (private, professional or special area) and work properties (long term, short term, repeatable): Herewith, the EVs, converters/inverters/drivers for EVs and others can be designed and calculated appropriately.

(5) In this paper, the authors only focus on “private Battery Electric Vehicles”. It may means that the EVs have low power, work privately and do not repeat frequently (start/stop). They do not interest on the electrical converters/inverters (Thyristor, Mosfest, Diode DC-AC or others), the electrical motor type (DC motor, AC motor or others) that can cause negative transient effects for battery. They also do not interest to the types of battery and battery technologies that have different properties of chemical electrical transformation, phenomena and lifetime. These properties decide for the most appropriate charging way. They do not interest on the converters/inverters (AD – DC) and their properties for charging batteries, what influence strongly on the battery.

(6) Technically, there are 2 main processes that take place on the researched system: The first one is from AC (Grid) to DC (Battery) with the important objects of battery and charging converter/inverter for charging. The second one is from DC (Battery) to AC (in the case of AC – Motor) or DC (in the case of DC motor) with the important object of battery, operation converter/inverter and motor. These processes do not operate concurrently in your description (private Battery Electric Vehicles) but they influence each other. In your paper, the descriptions show that you do not know/understand about these processes, their properties/technical parameters and influences. It means that this paper relates to the key technical issue, but do not mention to the one and therefore, do not treat the theme technically.

(7) Until this point, the paper is purely statistical issue. You take the data of trajectories, sort them into categories of charging modes and connect the parameters with the factors of start-SPC, time-origin, travel time last-fast status, driving speed, wind power, day of the week, temperature, weather and day. There are even overlaps and unclear parameters of the factors such as weather and temperatures or wind power. What is wind power in this case? How can be calculated for the wind power? The modeling and verifying is not serious enough to do.

I thinks, the paper should need a very big improvement! I have listed some improvements in the PDF-files. It can be summarized as follows,

• OK, you say “aiming to analyze the fast charging behavior of BEV drivers, identify the main affecting factor on the decision of charging and reveal the preference of fast charging selection” and an important thing is “ a fast charge prediction modelling considering the significant factors is proposed”
• So, firstly understand about battery and technology like above mentions.
• Please apply more diagrams to explain:
• Your model is a black box.
• The properties of your objects?
• What are your inputs? E.g. Travel time, travel distance, temperature, wind speed, daytime…etc.
• What are your outputs? E.g. fast charging/total charging ratio …etc.
• What are noise into your model? Capacity of battery, number of charging, battery lifetime...etc.???
• In your statistical model, the method of sampling and synchronizing are very important and interesting. You have explained in 150 – 164, why do not you apply diagram to your interesting points such as automatic sampling, the real time, and a schematic diagram of data collection what include devices, techniques and properties.
• Figure 1and 13 is extreme bad at both text and resolution. Why do not you convert into table?
• The part of literature review: You say so much, but nothing is so extreme special lessons what you can learn from those. 88-90 has a very interesting thing about demand. There is a theory about demand side management. If you can read and apply for the BEVs, it is perfect.
• Something belongs to writing and English style such as repeating words, so complicated sentences an tense
• Legend and other mistakes in Figure 2, 3
• Analysis in 261 and 262 can be improved better
• “Analysis of influence factors upon fast charging behavior” in 4 can be predicted without any study! Therefore, you try to show the special things in these figures and data.
• .”Significant factors analysis of fast charging behavior” in 5 and “Fast charging behavior prediction” in 6 are your special contributes should be carried out deeply and explained clearly so that readers can understand what are scientific contributes here. E.g. why the factors are significant compared with others? Explanations from Table 2 and 3 are not good enough. The explanations in 6.1, 6.2, Table 4 and 5 are the same. What parameters are key and why? They tell us what? Why are they important in this study?

Comments for author File: Comments.pdf

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

This is a good paper. It tries to solve the problem that due to the limited power cell performance of battery electric vehicles  drivers endure a short cruising range and a long charging time. A prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, univariate linear regression with several factors and multivariate linear regression model. The study helps better understanding fast charging behavior and further contribution to the future improvement of fast charging efficiency.

Author Response

We appreciate the reviewer’s insightful comment!

Round 2

Reviewer 1 Report

Dear Authors and Editors,

Thank you so much, because you have listened from my reviews, improved your disadvantages and also identified your disadvantages for future works.

In this time, I have focussed on Part 5 and 6, where the signifiacant factors are identified (5) and then prediction models are constructed. Logigically, it is very good in the link together. And the used methods are also very good. Using 3/10 data for verifying of the models built based on 7/10 data are also OK. The descriptions were improved well so that reader can understand how the authors have done and why.

However, there are something that must be improved futher:
(1) I think, the authors write quite quickly. Some simple grammar errors happen frequently. I have showed fews in the PDF-file, but no time to find all of them. Please check carefully again.
(2) In Part 5, there is the conflict between the written text and the result of Table 5. I have not understood.
(3) In Part 6,
a) I think, the authors should improve some mathematical symbols. It is not in programming.
b) The explaination is too complicated and not correct/not good in scientifical writing.
c) Please supply the parameters "alpha", "beta" of the models so that readers can check/apply them.
d) the prediction accuracy, how can you calculate this percentage?
e) the figures 13, a, b, c are too bad of resolutions and quality. It must be improved.

Thanks and regards,

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf