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

Predictive theories of action observation propose that we use our own motor system as a guide for anticipating and understanding other people's actions through the generation of context-based expectations. According to this view, people should be better in predicting and interpreting those actions that are present in their own motor repertoire compared to those that are not. We recorded high-density event-related potentials (ERPs: P300, N400 and Slow Wave, SW) and source estimation in 80 subjects separated by their level of expertise (experts, beginners and naïves) as they observed realistic videos of Tango steps with different degrees of execution correctness. We also performed path analysis to infer causal relationships between ongoing anticipatory brain activity, evoked semantic responses, expertise measures and behavioral performance. We found that anticipatory activity, with sources in a fronto-parieto-occipital network, early discriminated between groups according to their level of expertise. Furthermore, this early activity significantly predicted subsequent semantic integration indexed by semantic responses (N400 and SW, sourced in temporal and motor regions) which also predicted motor expertise. In addition, motor expertise was a good predictor of behavioral performance. Our results show that neural and temporal dynamics underlying contextual action anticipation and comprehension can be interpreted in terms of successive levels of contextual prediction that are significantly modulated by subject's prior experience.

No abstract

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.

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

Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO 4 batteries using rule-based systems. In this paper, rulebased systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different LiFePO 4 batteries, and the results were found to be more accurate than those of a selection of statistical models and state-of-the-art artificial intelligence techniques. Index Terms-Computational intelligence, equivalent circuit model, LiFePO 4 batteries, semiphysical intelligent systems.0278-0046

No abstract