Autonomous driving paper index

Finite Element Assessment of Single-Track E-Cargo Bike Frames Under Standard-Inspired Fatigue and Impact Loading Conditions

2026-08-04 · Machines

autonomous driving

One-line summary

E-cargo bikes have emerged as a promising solution for sustainable urban mobility and last-mile logistics.

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充:本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

E-cargo bikes have emerged as a promising solution for sustainable urban mobility and last-mile logistics. However, their structural design must ensure durability and safety under demanding cargo transport and daily operating conditions. This study evaluates the structural performance of three single-track E-cargo bike frame typologies, Urban, Long John and Long Tail, using finite element analysis under fatigue and impact loading conditions derived from EN 15194:2020 and EN 17860-2:2024. Numerical models of aluminum 6061-T6 frames were developed to simulate cyclic pedaling, horizontal, seat-post and vertical cargo loading forces, together with falling-frame and falling-mass impact tests. Structural performance was assessed through fatigue life, stress distribution, damage initiation, plastic strain and permanent wheelbase deformation. The Urban and Long John frames satisfied the adopted fatigue-life requirements, whereas the Long Tail frame failed the vertical loading-area fatigue test with a predicted fatigue life of 5.22 × 104 cycles, below the required 2 × 105 cycles. The maximum von Mises stresses during the falling-frame impact test reached 384 MPa, 326 MPa and 356 MPa for the Urban, Long John and Long Tail frames, respectively, while the corresponding permanent wheelbase deformations remained limited to 2.07 mm, 1.97 mm, and 1.43 mm, all below the acceptance criterion. These results highlight the influence of frame geometry and cargo location on structural behavior and support future frame optimization.

5.0Engineering value
7.0Research novelty
5.0Business relevance

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