We cannot accept new source files as corrections for your paper. If possible, please annotate the PDF proof we have sent you with your corrections and upload it via the Author Gateway. Alternatively, you may send us your corrections in list format. You may also upload revised graphics via the Author Gateway. r Authors: Please note that once you click "approve with no changes," the proofing process is now complete and your paper will be sent for final publication and printing. Once your paper is posted on Xplore, it is considered final and the article of record. No further changes will be allowed at this point so please ensure scrutiny of your final proof. r Authors: Unless invited or otherwise informed, a mandatory Excessive Paper Length charges will be incurred if your paper is over the page limit set by the society in the Information for Authors. If you have any questions regarding overlength page charges, need an invoice, or have any other billing questions, please contact reprints@ieee.org as they handle these billing requests. QUERIES

This publication will present best practices for incremental capacity analysis, a technique whose popularity is growing year by year because of its ability to identify battery degradation modes for diagnosis and prognosis. While not complicated in principles, the analysis can often feel overwhelming for newcomers because of contradictory information introduced by ill-analyzed datasets. This work aims to summarize and centralize good practices to provide a strong baseline to start a proper analysis. We will provide general comments on the technique and how to avoid the main pitfalls. We will also discuss the best starting points for the most common battery chemistries such as layered oxides, iron phosphate, spinel or blends for positive electrodes and graphite, silicon oxide, or lithium titanate for negative electrodes. Finally, a set of complete synthetic degradation maps for the most common commercially available chemistries will be provided and discussed to serve as guide for future studies.

In the last few years, several Li-ion battery technologies have been studied and developed for its use in Electric Vehicles (EVs). Among these, Lithium Iron Phosphate (LFP) batteries are considered a promising battery technology for EVs, due to its key advantages, such as cycle life, efficiency and reliability, to name a few. This work evaluates 5 commercial LFP batteries, studied under various testing scenarios, including cycle life, energy efficiency, power capability or internal resistance test. The obtained results are compared with the longterm U.S. Advanced Consortium (USABC) goals, in order to validate the feasibility of this technology for its use in EVs. We found that although the batteries successfully met some important USABC goals, there are still several technical challenges to be addressed, such as the increase of the energy density or the reduction of its final cost.

No abstract

Internal resistance (IR) is considered one of the most important parameters of a battery, as it is used to evaluate the battery's power performance, energy efficiency, aging mechanisms or equivalent circuit modeling. In addition, in electric vehicle (EV) applications, the IR provides essential information related with regenerative braking capabilities, dynamic charge and discharge efficiencies, or physical degradation of the battery. This work aims to provide the insight details of the IR of a battery under several testing conditions and methods, to present its practical implications on EVs. The experimental tests are carried out on lithium iron phosphate (LFP) batteries ranging from 16 Ah to 100 Ah, suitable for its use in EVs. We study the IR dependency with battery's capacity, SOC and the charge/discharge rate; also, the convenience of using a certain IR measurement method is evaluated. Furthermore, the main results are put into context for practical EV applications, to enhance the design of battery management systems (BMS) in relation with the system's energy efficiency.

We cannot accept new source files as corrections for your paper. If possible, please annotate the PDF proof we have sent you with your corrections and upload it via the Author Gateway. Alternatively, you may send us your corrections in list format. You may also upload revised graphics via the Author Gateway. r Authors: Please note that once you click "approve with no changes," the proofing process is now complete and your paper will be sent for final publication and printing. Once your paper is posted on Xplore, it is considered final and the article of record. No further changes will be allowed at this point so please ensure scrutiny of your final proof. r Authors: Unless invited or otherwise informed, a mandatory Excessive Paper Length charges will be incurred if your paper is over the page limit set by the society in the Information for Authors. If you have any questions regarding overlength page charges, need an invoice, or have any other billing questions, please contact reprints@ieee.org as they handle these billing requests. QUERIES

DC internal resistance (IR) is considered one of the most important parameters of a battery, as it is used to evaluate the battery's power performance, energy efficiency, aging mechanisms or equivalent circuit modeling. In electric vehicle (EV) applications, the IR during charge gives also essential information related with regenerative braking and dynamic charge efficiency. In this work, we tested four lithium iron phosphate batteries (LFP) ranging from 16 Ah to 100 Ah, suitable for its use in EVs. We carried out the analysis using three different IR methods, and performed the tests at three charging rates (nominal, mid and high) through several states of charge (SOC). In this paper, we study the IR dependency with battery's capacity, SOC and the charging rate; also, the convenience of using a certain IR method is analyzed.Furthermore, the main results are put into context for practical EV applications, to enhance the design of battery management systems (BMS) in relation with the system's energy efficiency.

No abstract