Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%.
Hydrogen leakage and emission will bring adverse effects to the environment and human health. When it enters a certain concentration range, it will bring the potential explosion. Hydrogen emissions from fuel cell vehicles can be divided into two sources: normal operation of the system and leakage under abnormal circumstances. This paper mainly uses the flowmeter method to measure the hydrogen emission from fuel cell electric vehicle（FCEV）. The different emission characteristics of different types of FCEV under idle and steady cycle conditions were analyzed. The hydrogen emission levels of different types of FCEV under idle and cycle conditions were verified by analyzing and processing the experimental data through a certain amount of hydrogen emission tests of FCEV.
HELVIS (Hybrid Electric Vehicle In Low Scale) is a mini-HEV platform used on the research of HEVs (Hybrid Electric Vehicles), through which students of all degrees have the opportunity to be introduced to the universe that surrounds HEVs in many aspects. In this work the HELVIS-Sim is presented. HELVIS-Sim is a full dynamic & kinematic vehicular simulator for the HELVIS platform, consisting of a Simulink™ environment through which the states of a large number of variables related to the vehicle can be observed and analyzed. Specially in this paper, the focus is in the control of HELVIS EDS (Electronic Differential System), presenting classic, A.I.-based (Artificial Intelligence) and optimal robust controllers in the problem of the adjustment of the rear angular speeds. HELVIS-Sim results are then compared to experimental data obtained from the real HELVIS EDS, with the aid of a dSpace™ real time interface board.
Energy efficiency and leakage magnetic field (LMF) are two important issues in electric vehicle inductive chargers. In this work, the maximum achievable coil efficiency and the corresponding LMF strength are formulated as functions of hardware parameters, and figure of merits (FOM) are proposed for assessing the efficiency and LMF performance of the coil assembly pair. The impacts of the coil assemblies’ geometric parameters on both FOMs are examined with the aid of finite element analysis (FEA), and measures to improve the FOMs are extracted from FEA results. A coil assembly pair is manually optimized within given dimensional limits. Compared with the initial design, the optimized one achieves higher efficiency and lower LMF strength while consuming less copper. The performance improvement is verified by FEA results and experimental data measured on an 85 kHz electric vehicle inductive charger prototype. The key measures for coil assembly optimization are summarized.
This paper addresses the problem of estimating the tire cornering stiffness coecient and the yaw moment of inertia of a scaled car-like vehicle. The method merges measurements information of the vehicle lateral response along with its nonlinear planar model. Aiming effective and accurate results, we propose solving an optimization problem based on a representative dataset obtained experimentally using a persistently exciting input. The validation of the proposed method is shown by comparing the agreement between numerical simulations, evaluated with the estimated parameters, with experimental data. Three representative maneuvers are considered for this purpose, including indoor and outdoor experiments.
A program was developed using the software Mathematica to simulate the dynamical behavior of an electric racing car, an electrathon. In conjunction with experimental data it is focused to allow the Borregos-CEM Racing Team decide which settings have to be adjusted in order to increase the velocity of the racing car while decreasing its energy consumption, i.e. the current demanded to the batteries.
Keywords: Model, electric, racing, vehicle, dynamic, simulation, Electrathon
A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries
