Enhancing Power Net Efficiency with Data-Driven Consumption Prediction - A Machine Learning Approach

Luxury car manufacturers are facing the challenge of transitioning their customers to electric mobility without compromising on comfort levels. While efforts to enhance efficiency have traditionally focused on powertrain and aerodynamics, the rise in efficiency of electric drive trains has highlighted the significance of low-voltage power consumers. To address this, our study proposes a data-driven modelling strategy that leverages real-time in-car communication data to gain insights into the power consumption behavior of low-voltage power consumers across various real-world driving scenarios. Our methodology involves a selection process for simulation-worthy consumers, an appropriate regression algorithm, feature selection techniques, and Key Performance Indicators to assess the models’ quality and efficiency.By utilizing our data-driven modelling strategy, we can provide an individual and transparent evaluation of each power consumer, enabling automotive development engineers to optimize their vehicles’ power distribution network and deliver an unparalleled driving experience.