Energy Logistics Cost Study for Wireless Charging Transportation Networks
Round 1
Reviewer 1 Report
This paper mainly proposes three different types of wireless charging systems to compare the cost of initial investment taking into account the battery’s state of charge (SoC) throughout a certain route. The charging infrastructure’s location and length are considered in the paper to optimize the minimum energy logistics cost. The relationship between the number of vehicles and energy logistic cost are also given followed by case studies and comparisons with different types of wireless charging network. The paper is structured with general demonstration of the figures, tables and discussion. However, there are some concerns need to be addressed:
- Section 3.3, only MATLAB Programming is proposed to estimate a battery’s SoC, rather than the corresponding algorithm or basis, while SoC estimation is an important component to determine the battery working status for allocating of charging units to fit the model. Can the authors provide some algorithmic basis to prove the rationally of SoC estimation?
- 8 (b) is to demonstrate a curve that simulates the change of battery energy level with time. If the remaining energy is used to indicate the status of the battery, can authors mark the total available capacity in the legend, so that the readers can judge whether the change in energy is within a reasonable charging interval? Alternatively, percentage can also be used to represent the value of SoC over time. From the curve of Fig. 8, it is observed that the vehicle is charged in a short period of about 30s (corresponding to the 2000s interval), is this reasonable that charging in such a short time? On the other hand, it can be noted that there is about 5 minutes of charging time after 4000s. Without considering the time when the vehicle is parked in the charging parking space (about 1 min), and the charging system starts (about 1 min), all the time is spent on charging. Is this reasonable that only 5 minutes can increase the value from 27 kWh to 36 kWh of the battery, which is far more efficient than wired charging? Can the authors give some justifications on these?
- From the results of all case studies, it can be noted that the energy logistics cost of different types of wireless charging networks are used to compared in this article. However, authors emphasize that the paper compares the cost of initial investment, but only a citation is given in the paper that the energy logistics cost accounts for most of the total cost of an EV-based transit system and does not provide the relationship between initial investment and energy logistics cost. Can the authors make the scope of the research and the case studies clearer by adding some explanations or redefine the research object?
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
I consider that the work does not deliver what the title promises.
The title is not exactly appropriate, because the systematic character is unclear, it doesn’t result from the paper. Moreover, the paper is not the actual design of a Wireless Charging Transportation Network, but a preliminary empirical study, which has to be developed in order to underlie to the basis of a real design.
The conclusions are not the most appropriate. For example, one says that "This study has shown the analysis of the potential implementation of wireless power charging to an actual bus route, reducing emissions, and improving traffic operations and planning", but only the first part of the statement is based on the results presented in the paper, the last part being a statement without coverage in the paper, which doesn't analyze the contribution of the a Wireless Charging Transportation Network to emissions reduction or traffic operations and planning improving.
Bibliographic references are too old in a large proportion, they should be updated with the articles that present demonstrators of Wireless Charging Transportation Networks now in operation.
There are now tools offering the capability of integrating driving data (simulated or collected from the real world), vehicle parameters (e.g., battery, motor, dimensions), and wireless charger characteristics (e.g. rate, locations, alignment) to generate energy and state-of-charge profiles for each vehicle, considering motoring, regenerative braking, and charging. Most often, those tools incorporate a multi-objective optimization layer for searching the optimum design parameters based on predefined objectives and constraints.
Author Response
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Author Response File: Author Response.pdf
Reviewer 3 Report
Please refer to the detailed comments file.
Comments for author File: Comments.pdf
Author Response
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Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
There is now an agreement between the title and the paper content.
An improvement is made, but some corrections are still needed. Please read the suggestions and comments in the attachment and act accordingly.
Comments for author File: Comments.pdf
Author Response
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Author Response File: Author Response.pdf
Reviewer 3 Report
The revised version of the manuscript has improved quality and is recommended for publication.
Author Response
The authors would like to thank the reviewer for this positive comment on our work.
