An Electric Vehicle Simulator for Realistic Battery Signals Generation from Data-sheet and Real-world Data

Electric vehicles (EVs) have been globally recognized as a reliable alternative to fossil fuel vehicles. The core component of an electric vehicle is its rechargeable battery pack. However, there still needs to be large-scale publicly available EV data to investigate and distribute effective solutions to monitor the conditions of the EV’s battery pack. Hence, we propose an EV simulator that generates EV battery pack internal signals starting from the input driving cycle. The simulated data resemble the behavior of a multi-cell EV battery pack undergoing the user’s utilization of the EV. The simulated data include vehicle speed, voltage, current, State of Charge (SOC), and internal temperature of the battery pack. The virtual-EV model simulator, including the battery pack subsystem, has been tuned using real-world EV data-sheet information. The battery pack embeds thermal and aging models for further realism, influencing the output signals given the environmental temperature and the battery’s State of Health (SOH). The data generated by the virtual EV simulator have been validated with real EV data signals sampled by an equivalent real-world EV. The data comparison yields a minimum R2 value of 0.94 and a Root Mean Squared Error not higher than 2.74V for the battery pack’s voltage and SOC, respectively

Impact of bidirectional EV charging stations on a distribution network: a Power Hardware-In-the-Loop implementation

The need for decarbonizing the entire energy system calls for new operational approaches in different sectors, currently (almost) fully dominated by fossil fuels, such as the transports. In particular, the decarbonization of the light-duty passenger transport, based on the implementation of Battery Electric Vehicles, may have a twofold benefit, because of (i) the reduction of local and global direct emissions, and (ii) the role that the Battery Electric Vehicles can have in supporting the operation of the power system in case of large share of non-dispatchable renewable energy sources. This paper aims to investigate, through a Power Hardware-In-the-Loop laboratory setup, the impacts of the Vehicle-to-Grid and Grid-to-Vehicle paradigms on a Low Voltage grid portion serving as grid infrastructure an energy community. The results show that the Low Voltage grid losses, if not taken into account, can cause a wrong evaluation of the expected impact on the grid of the Battery Electric Vehicles. Furthermore, the harmonics of current injected into the grid by several chargers could compromise the perceived power quality. Both the analyzed aspects must be hence carefully considered for properly evaluating pros and cons that the installation of several chargers may have on the grid side. The main contributions refer to the calculation of losses and to the evaluation of the power quality aspects through a Power Hardware-In-the-Loop configuration, enabling to take into account the harmonics interaction between charging stations and power grid

Modelling battery packs of real-world electric vehicles from data sheet information

Lithium-ion batteries have emerged as the leading enabling technology in developing Electric Vehicles (EVs), But, large-scale publicly available EV data are extremely difficult to find. So it becomes difficult to research and disseminate new methods for monitoring the battery pack of an EV. In this work, we propose a Simulink-based approach to define a virtual-EV model that simulates EV battery pack signals starting from input driving sessions. The battery pack module within the virtual-EV has been fine-tuned using data gathered from real-world EV data sheets. Moreover, the battery pack module includes thermal and aging models, impacting on the output signals, considering the temperature of the surrounding environment and the initial State of Health (SOH) of the battery pack. The virtual-EV generates time series of vehicle's speed, and battery pack's current, State of Charge (SOC), voltage, and average internal temperature according to the input driving cycle. We defined two Simulink EV models emulating two distinct real-world-EVs. Then, we assessed the performances of the simulators comparing the simulated data and real EV data signals collected by the same real-world-EV models, and we obtain, for both simulated EV models, R2 values higher than 0.70 and an RMSE of at most 7V and 8% for the voltage and SOC of the battery pack, respectively

Comparative Analysis of Neural Networks Techniques for Lithium-ion Battery SOH Estimation

Li-ion batteries have become the most important technology for electric mobility. One of the most pressing challenges is the development of reliable methods for battery state-of-health (SOH) diagnosis and estimation of remaining useful life. In electric mobility scenario, battery capacity degradation prediction is crucial to ensure service availability and life duration. This research work provides a comprehensive comparative analysis of neural networks for a data-driven approach suitable for SOH estimation on single cells, stressed under laboratory conditions. For this purpose, different neural networks (i.e., LSTM, GRU, 1D-CNN, CNN-LSTM) are trained and optimized on NASA Randomized Battery Usage dataset. Experimental results demonstrate that data-driven neural networks generally performed well SOH estimation on single cells. In detail, the 1D-CNN best predicts SOH and has the lowest variance in the output. The LSTM have the highest variance in estimating SOH, while GRU and CNN-LSTM tend to overestimate and underestimate the value of SOH, respectively

INFN What Next: Ultra-relativistic Heavy-Ion Collisions

This document was prepared by the community that is active in Italy, within INFN (Istituto Nazionale di Fisica Nucleare), in the field of ultra-relativistic heavy-ion collisions. The experimental study of the phase diagram of strongly-interacting matter and of the Quark-Gluon Plasma (QGP) deconfined state will proceed, in the next 10-15 years, along two directions: the high-energy regime at RHIC and at the LHC, and the low-energy regime at FAIR, NICA, SPS and RHIC. The Italian community is strongly involved in the present and future programme of the ALICE experiment, the upgrade of which will open, in the 2020s, a new phase of high-precision characterisation of the QGP properties at the LHC. As a complement of this main activity, there is a growing interest in a possible future experiment at the SPS, which would target the search for the onset of deconfinement using dimuon measurements. On a longer timescale, the community looks with interest at the ongoing studies and discussions on a possible fixed-target programme using the LHC ion beams and on the Future Circular Collider.Comment: 99 pages, 56 figure

Multiplicity dependence of jet-like two-particle correlations in p-Pb collisions at sNN\sqrt{s_{NN}} = 5.02 TeV

Two-particle angular correlations between unidentified charged trigger and associated particles are measured by the ALICE detector in p-Pb collisions at a nucleon-nucleon centre-of-mass energy of 5.02 TeV. The transverse-momentum range 0.7 $< p_{\rm{T}, assoc} < p_{\rm{T}, trig} <$ 5.0 GeV/$c$ is examined, to include correlations induced by jets originating from low momen\-tum-transfer scatterings (minijets). The correlations expressed as associated yield per trigger particle are obtained in the pseudorapidity range $|\eta|<0.9$. The near-side long-range pseudorapidity correlations observed in high-multiplicity p-Pb collisions are subtracted from both near-side short-range and away-side correlations in order to remove the non-jet-like components. The yields in the jet-like peaks are found to be invariant with event multiplicity with the exception of events with low multiplicity. This invariance is consistent with the particles being produced via the incoherent fragmentation of multiple parton--parton scatterings, while the yield related to the previously observed ridge structures is not jet-related. The number of uncorrelated sources of particle production is found to increase linearly with multiplicity, suggesting no saturation of the number of multi-parton interactions even in the highest multiplicity p-Pb collisions. Further, the number scales in the intermediate multiplicity region with the number of binary nucleon-nucleon collisions estimated with a Glauber Monte-Carlo simulation.Comment: 23 pages, 6 captioned figures, 1 table, authors from page 17, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/161

Anisotropic flow of charged hadrons, pions and (anti-)protons measured at high transverse momentum in Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm NN}}=2.76 TeV

The elliptic, $v_2$, triangular, $v_3$, and quadrangular, $v_4$, azimuthal anisotropic flow coefficients are measured for unidentified charged particles, pions and (anti-)protons in Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV with the ALICE detector at the Large Hadron Collider. Results obtained with the event plane and four-particle cumulant methods are reported for the pseudo-rapidity range $|\eta|<0.8$ at different collision centralities and as a function of transverse momentum, $p_{\rm T}$, out to $p_{\rm T}=20$ GeV/$c$. The observed non-zero elliptic and triangular flow depends only weakly on transverse momentum for $p_{\rm T}>8$ GeV/$c$. The small $p_{\rm T}$ dependence of the difference between elliptic flow results obtained from the event plane and four-particle cumulant methods suggests a common origin of flow fluctuations up to $p_{\rm T}=8$ GeV/$c$. The magnitude of the (anti-)proton elliptic and triangular flow is larger than that of pions out to at least $p_{\rm T}=8$ GeV/$c$ indicating that the particle type dependence persists out to high $p_{\rm T}$.Comment: 16 pages, 5 captioned figures, authors from page 11, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/186

Centrality dependence of charged particle production at large transverse momentum in Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm{NN}}} = 2.76 TeV

The inclusive transverse momentum ($p_{\rm T}$) distributions of primary charged particles are measured in the pseudo-rapidity range $|\eta|<0.8$ as a function of event centrality in Pb-Pb collisions at $\sqrt{s_{\rm{NN}}}=2.76$ TeV with ALICE at the LHC. The data are presented in the $p_{\rm T}$ range $0.15<p_{\rm T}<50$ GeV/$c$ for nine centrality intervals from 70-80% to 0-5%. The Pb-Pb spectra are presented in terms of the nuclear modification factor $R_{\rm{AA}}$ using a pp reference spectrum measured at the same collision energy. We observe that the suppression of high-$p_{\rm T}$ particles strongly depends on event centrality. In central collisions (0-5%) the yield is most suppressed with $R_{\rm{AA}}\approx0.13$ at $p_{\rm T}=6$-7 GeV/$c$. Above $p_{\rm T}=7$ GeV/$c$, there is a significant rise in the nuclear modification factor, which reaches $R_{\rm{AA}} \approx0.4$ for $p_{\rm T}>30$ GeV/$c$. In peripheral collisions (70-80%), the suppression is weaker with $R_{\rm{AA}} \approx 0.7$ almost independently of $p_{\rm T}$. The measured nuclear modification factors are compared to other measurements and model calculations.Comment: 17 pages, 4 captioned figures, 2 tables, authors from page 12, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/284