Development and Comprehensive Analysis of a Synthetic Power Thermal Dataset for AI-Driven ECU Design Optimization and Risk Classification
Ricardo RodrÃ­guez Jorge
Journal of Electronic Systems, Vol. 16, No. 3, 2026.
DOI: https://doi.org/10.6025/jes/2026/16/3/129-145
Link: https://www.dline.info/jes/fulltext/v16n3/jesv16n3_1.pdf
Abstract:
The increasing complexity of automotive Electronic Control Units (ECUs) demands integrated design
approaches that simultaneously address electrical performance, thermal reliability, functional safety, and
real time validation. However, the scarcity of public multi-physics datasets has hindered the development of
AI-driven optimization frameworks capable of unifying these competing objectives. This study presents the
development and comprehensive analysis of a synthetic power thermal dataset for AI-driven ECU design
optimization and risk classification. The dataset, generated through physics-informed simulations, comprises
over 15 electrical, thermal, and composite variables representing realistic ECU operating conditions. Through
extensive statistical testing, regression modeling, and correlation analysis including Pearson and Spearman
correlations, linear and polynomial regression, and multi-collinearity assessment we identify the dominant
physical drivers of thermal stress and risk behavior. Results reveal that while voltage and electrical resistance
exhibit negligible influence on current variation and IR drop (RÂ² = 0.000461, p = 0.0205), Power Density,
Current, and total Power are the paramount factors dictating temperature rise and thermal stress, with
Current exhibiting a very strong correlation with Thermal Stress (r = 0.810). The proposed closed loop
Hardware in the Loop (HIL) integrated AI pipeline incorporating multi-task learning, FPGA accelerated
inference, and physics informed neural networks enables simultaneous multi class risk classification and
predictive thermal modeling. This work provides a foundational benchmark for next generation ECU design,
indicating that physics aware synthetic datasets, combined with hybrid AI architectures, can effectively
support real time, safety compliant optimization while reducing development costs and physical prototyping
requirements.