Big data-driven load spectrum measurement and lightweight optimization for aluminum alloy truck frames

Abstract This study addresses the challenges of low fatigue life prediction accuracy and excessive weight in electric truck frames. A novel load spectrum acquisition method, based on internet of vehicles (IoV) big data statistics, is proposed to capture real user operating conditions. This method analyzes IoV operational data from 206 electric trucks, including driving routes, speeds, mileage, load conditions, etc., using K -means clustering and mapping application programming interfaces to classify and identify the data. It determines the proportions of typical user scenarios, operating conditions, and load conditions. Based on these proportions, strain time histories at key load-bearing points of the truck frame under various usage conditions were measured, and load-strain calibration tests were conducted, leading to a new method for obtaining truck frame load spectra. Using the experimentally measured P-SN curve, the fatigue life of the aluminum alloy truck frame was accurately predicted. A multi-objective collaborative optimization method for fatigue life and lightweighting was then implemented. The aluminum alloy truck frame achieved a 35.38% weight reduction while significantly enhancing mechanical performance, and fatigue life, compared to the steel baseline. This study provides valuable insights into lightweighting and user-based fatigue life optimization of truck frames, with significant practical value.