Training and Validation Methodology for Range Estimation Algorithms

Estimating the range of battery electric vehicles is one of the most challenging topics for the current trend in the automotive industry, the electrification of vehicles. Range anxiety still limits the adoption of battery electric vehicles. Since the range estimation is dependent on different influencing factors, complex algorithms to accurately estimate the vehicles consumption are required. To evaluate the accuracy of data-driven machine learning algorithms, an exhaustive training and validation procedure is mandatory. In this paper, we propose a novel methodology for the development and validation of range estimation algorithms based on machine learning validation approaches. The proposed methodology considers the evaluation of driver-specific and driver-unspecific performance. In addition, an error measure is introduced to assess the performance of range estimation algorithms. This approach is demonstrated and evaluated on a set of recorded real-world driving data. It is shown that our approach helps to analyze the performance of the range estimation algorithm and the influences of different parameter sets.