This paper aims to provide an analysis of battery electric vehicle (BEV) performance based on a comprehensive collection of specifications of over 40 currently globally available BEVs. The analysis comprises dimensional, powertrain, and performance data. The collected data are provided in this paper to facilitate further academic studies. There are a wide variety of BEVs from small to large as well as high-performance sedans and sport cars. It was found that all BEVs use Li-ion batteries with a specific energy of 55–170 Wh/kg and that battery weights represent 13%–37% of the vehicle weight. Furthermore, the majority of BEVs use permanent magnet motors, where some indicate power densities of 1.5–3.1 kW/kg. The specified New European Driving Cycle (NEDC) driving range varies between 85 and 528 km, and the energy consumption between 117 and 268 Wh/km. A rough estimation indicates that the powertrain’s share of the net energy consumption during NEDC may be up to around 40%–54%.

Performance Analysis of Current BEVs Based on a Comprehensive Review of Specifications

On-Road Turbulence

CFD investigation on wheel rotation modelling

Knowledge of the remaining driving range is crucial for drivers of battery electric vehicles (BEVs), because maximum range and charging infrastructure are limited. This and the fact that current implementations of range displays are deemed unreliable leads to range anxiety and ultimately less acceptance of BEVs. Every range estimation algorithm depends on various uncertain inputs and hence, its result is also uncertain. A more reliable range estimation could be achieved by assessing uncertainties of the underlying predictions and measurements and their influence on the calculation result. This paper presents a method for accurately calculating the probability of reaching the destination of a given route. For this, uncertainty of future velocity and battery state is analyzed using the example of an actual test vehicle. The error propagation through a physical model of the powertrain is evaluated by means of Monte-Carlo method and Taylor series expansion. Range uncertainty is investigated for different sets of input parameters, yielding a necessary safety margin between 12% and 23% battery charge. It is also shown that uncertainty more than doubles over lifetime of the vehicle and that neglecting this uncertainty can lead to unpredictable stranding for disadvantageous initial conditions. Knowledge of range uncertainty can reduce the necessary safety margin and avoid stranding of vehicles. In addition, displaying the uncertainty information to the driver alleviates range anxiety of drivers and consequently, acceptance of electric vehicles can be raised in society.

Influence of Measurement and Prediction Uncertainties on Range Estimation for Electric Vehicles

https://hal.archives-ouvertes.fr/hal-01832640/document

A Lattice-Boltzmann model for low-Mach reactive flows

The Aerodynamic Development of the Tesla Model S - Part 1: Overview

The Aerodynamic Development of the Tesla Model S - Part 2: Wheel Design Optimization

Alternative Simulation Methods for Assessing Aerodynamic Drag in Realistic Crosswind

Application of Real-World Wind Conditions for Assessing Aerodynamic Drag for On-Road Range Prediction

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The Aerodynamic Development of the Tesla Model S - Part 1: Overview

The Aerodynamic Development of the Tesla Model S - Part 2: Wheel Design Optimization

Alternative Simulation Methods for Assessing Aerodynamic Drag in Realistic Crosswind

Application of Real-World Wind Conditions for Assessing Aerodynamic Drag for On-Road Range Prediction