A Simple Internal Resistance Estimation Method Based on Open Circuit Voltage Test under Different Temperature Conditions

© 2018 IEEE. State-of-charge (SoC) is one critical parameter for battery management system. SoC cannot be directly measured but it can be estimated according to some information of battery management system such as voltage and current. Two commonly used methods to estimate the SoC are 1) by using current times a constant internal resistance, and 2) by referring to a SoC-resistance lookup table to interface with an open-circuit-voltage (OCV)-SoC lookup table. However, these widely used testing methods of internal resistance have not considered the influence of SoC, temperature and current rate. which are in fact related to internal resistance. Therefore, ignoring the temperature and current rate factors will obtain inaccurate internal resistance measurement and battery SoC estimation. This paper hence proposes a dynamic resistance model with improved accuracy through combining SoC-OCV at different ambient temperatures with different discharging rates defined at the standard ambient temperature (25 degree) condition. The proposed method will not only improve the accuracy but also reduce the testing time

Physics-informed neural networks with hard constraints for inverse design

Inverse design arises in a variety of areas in engineering such as acoustic, mechanics, thermal/electronic transport, electromagnetism, and optics. Topology optimization is a major form of inverse design, where we optimize a designed geometry to achieve targeted properties and the geometry is parameterized by a density function. This optimization is challenging, because it has a very high dimensionality and is usually constrained by partial differential equations (PDEs) and additional inequalities. Here, we propose a new deep learning method -- physics-informed neural networks with hard constraints (hPINNs) -- for solving topology optimization. hPINN leverages the recent development of PINNs for solving PDEs, and thus does not rely on any numerical PDE solver. However, all the constraints in PINNs are soft constraints, and hence we impose hard constraints by using the penalty method and the augmented Lagrangian method. We demonstrate the effectiveness of hPINN for a holography problem in optics and a fluid problem of Stokes flow. We achieve the same objective as conventional PDE-constrained optimization methods based on adjoint methods and numerical PDE solvers, but find that the design obtained from hPINN is often simpler and smoother for problems whose solution is not unique. Moreover, the implementation of inverse design with hPINN can be easier than that of conventional methods

The importance of preventive feedback: inference from observations of the stellar masses and metallicities of Milky Way dwarf galaxies

Dwarf galaxies are known to have remarkably low star formation efficiency due to strong feedback. Adopting the dwarf galaxies of the Milky Way as a laboratory, we explore a flexible semi-analytic galaxy formation model to understand how the feedback processes shape the satellite galaxies of the Milky Way. Using Markov-Chain Monte-Carlo, we exhaustively search a large parameter space of the model and rigorously show that the general wisdom of strong outflows as the primary feedback mechanism cannot simultaneously explain the stellar mass function and the mass--metallicity relation of the Milky Way satellites. An extended model that assumes that a fraction of baryons is prevented from collapsing into low-mass halos in the first place can be accurately constrained to simultaneously reproduce those observations. The inference suggests that two different physical mechanisms are needed to explain the two different data sets. In particular, moderate outflows with weak halo mass dependence are needed to explain the mass--metallicity relation, and prevention of baryons falling into shallow gravitational potentials of low-mass halos (e.g. "pre-heating") is needed to explain the low stellar mass fraction for a given subhalo mass.Comment: 14 pages, 4 figures, accepted for publication in Ap

The connection between the host halo and the satellite galaxies of the Milky Way

Many properties of the Milky Way's dark matter halo, including its mass assembly history, concentration, and subhalo population, remain poorly constrained. We explore the connection between these properties of the Milky Way and its satellite galaxy population, especially the implication of the presence of the Magellanic Clouds for the properties of the Milky Way halo. Using a suite of high-resolution $N$-body simulations of Milky Way-mass halos with a fixed final Mvir ~ 10^{12.1}Msun, we find that the presence of Magellanic Cloud-like satellites strongly correlates with the assembly history, concentration, and subhalo population of the host halo, such that Milky Way-mass systems with Magellanic Clouds have lower concentration, more rapid recent accretion, and more massive subhalos than typical halos of the same mass. Using a flexible semi-analytic galaxy formation model that is tuned to reproduce the stellar mass function of the classical dwarf galaxies of the Milky Way with Markov-Chain Monte-Carlo, we show that adopting host halos with different mass-assembly histories and concentrations can lead to different best-fit models for galaxy-formation physics, especially for the strength of feedback. These biases arise because the presence of the Magellanic Clouds boosts the overall population of high-mass subhalos, thus requiring a different stellar-mass-to-halo-mass ratio to match the data. These biases also lead to significant differences in the mass--metallicity relation, the kinematics of low-mass satellites, the number counts of small satellites associated with the Magellanic Clouds, and the stellar mass of Milky Way itself. Observations of these galaxy properties can thus provide useful constraints on the properties of the Milky Way halo.Comment: 20 pages, 12 figures, accepted for publication in ApJ. A new section on the effect of host halo mass-assembly history on the central galaxy stellar mass is adde

Accurate online battery impedance measurement method with low output voltage ripples on power converters

The conventional online battery impedance measurement method works by perturbing the duty cycle of the DC-DC power converter and measuring the response of the battery voltage and current. This periodical duty cycle perturbation will continuously generate large voltage ripples at the output of power converters. These large ripples will not easily be removed due to the high amplitude and wide frequency range and would be a challenge to meet tight output regulation. To solve this problem, this paper presents a new online battery impedance measurement technique by inserting a small switched resistor circuit (SRC) into the converter. The first contribution of this work is that the perturbation source is moved from the main switch to the input-side of the converter, so the ripples are reduced. The analysis and experimental results of the proposed method show a reduction of 16-times compared with the conventional method. The second contribution tackles the possible change of the battery state of charge (SOC) during the online battery measurement process, which will inevitably influence the impedance measurement accuracy. In this proposed method, battery impedance at multiple frequencies can be measured simultaneously using only one perturbation to accelerate measurement speed and minimize possible SOC change. The experimental impedance results coincide with a high-accuracy laboratory battery impedance analyzer

Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields

We prove the asymptotic normality of the kernel density estimator (introduced by Rosenblatt (1956) and Parzen (1962)) in the context of stationary strongly mixing random fields. Our approach is based on the Lindeberg's method rather than on Bernstein's small-block-large-block technique and coupling arguments widely used in previous works on nonparametric estimation for spatial processes. Our method allows us to consider only minimal conditions on the bandwidth parameter and provides a simple criterion on the (non-uniform) strong mixing coefficients which do not depend on the bandwith.Comment: 16 page