Validation of range estimation for electric vehicles based on recorded real-world driving data

Resource type
Patrick Petersen, Stefan Scheubner, Jacob Langner, Stefan Otten, Moritz Vaillant, Sebastian Fünfgeld and Eric Sax
Book title
19. Internationalen Symposiums
Electrification of vehicles is a growing trend in the automotive industry. Battery electric vehicles offer the potential to reduce greenhouse gas emissions, but short maximum range and missing charging infrastructure limits user acceptance. Range anxiety is a great challenge for battery electric vehicle drivers, therefore accurate methods for range estimation are required to satisfy customer needs. Novel algorithms for range estimation include many information sources such as traffic, geographical and weather data. The increase of algorithmic complexity and the behavior of various non-deterministic influences lead to a high demand for intensive verification and validation methods for such predictive features. In this paper, we propose a methodology for the verification and validation of range estimation algorithms based on recorded real-world driving data. The steadily growing pool of recorded test drives from test campaigns enables statistically significant testing. A generalization step enables the usage of all recorded data regardless of the vehicles architecture. New software versions of range estimation algorithms can be tested and a higher feature maturity can be achieved without the need for cost- and time-intensive real-world test drives. This approach is demonstrated and evaluated in a small test campaign.
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Published by
Patrick Petersen