Abstract
The further development of electric mobility requires major scientific efforts to obtain reliable data for vehicle and drive development. Practical experience has repeatedly shown that vehicle data sheets do not contain realistic consumption and range figures. Since the fear of low range is a significant obstacle to the acceptance of electric mobility, a reliable database can provide developers with additional insights and create confidence among vehicle users. This study presents a detailed, yet easy-to-implement and modular physical model for both passenger and commercial battery electric vehicles. The model takes consumption-relevant parameters, such as seasonal influences, terrain character, and driving behavior, into account. Without any a posteriori parameter adjustments, an excellent agreement with known field data and other experimental observations is achieved. This validation conveys much credibility to model predictions regarding the real-world impact on energy consumption and cruising range in standardized driving cycles. Some of the conclusions, almost impossible to obtain experimentally, are that winter conditions and a hilly terrain each reduce the range by 7–9%, and aggressive driving reduces the range by up to 20%. The quantitative results also reveal the important contributions of recuperation and rolling resistance towards the overall energy budget.
Highlights
Type approvals of technical devices are usually based on standardized test procedures to ensure that the results are repeatable and that the performance of different devices can be compared on the basis of the test results
Our studies have shown that, despite the complexity of battery electric vehicles (BEVs) consumption data determination, a remarkable agreement with empirical data is possible with a model that only works with parameter values known a priori and that can be implemented with common desktop computer software
One reason for the model success may be that the action of an electric powertrain can be described more by physical models than is the case with internal combustion engines
Summary
Type approvals of technical devices are usually based on standardized test procedures to ensure that the results are repeatable and that the performance of different devices can be compared on the basis of the test results. The vehicle is tested with minimum payload; ancillary loads, such as lights, airconditioning, or window heating, are turned off; weather conditions, such as wind, rain, and high or low temperatures, are ignored; tires are inflated to pressures above recommended values; the test can be conducted at 2 km/h below the required speed; the final test results can be arbitrarily reduced by 4%; road inclination and curves are not emulated; etc For this reason, and because the gentle test speed pattern does not reflect actual driving habits, the energy consumption and—for ICEVs—the emissions of a vehicle on the road exceed NEDC-based manufacturer specifications [2]. As useful as standard test procedures may be in terms of repeatability and comparability, they obviously are no reliable basis for predicting the energy consumption on the road or the vehicle range It was the aim of this study to find a method to predict the real-world electricity consumption of BEVs, both cars and trucks, that meets the following requirements: 1. It was the aim of this study to find a method to predict the real-world electricity consumption of BEVs, both cars and trucks, that meets the following requirements: 1. Capability to judge how close standardized test procedures are to reality
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