The growing adoption of software-defined and electrified vehicle architectures has significantly increased the computational burden on electronic control units, leading to dynamic and non-stationary load conditions that can compromise real-time performance and system reliability. Conventional ECU load-management strategies are largely static or address isolated aspects of the problem, such as overload prediction or energy optimization, without providing an end-to-end decision mechanism for runtime load redistribution. This study proposes a leakage-safe, three-stage intelligent ECU load-management model for electric vehicles that jointly performs overload detection, target ECU recommendation, and load-shift magnitude estimation within a gated architecture. The proposed model used ensemble and boosting-based machine learning models with task-specific feature design to prevent data leakage and reduce computational overhead through conditional execution. The performance of the proposed model is measured on a multi-feature ECU dataset characterized by non-stationary operational conditions and significant class imbalance between normal and overload states and addressed using stratified sampling and SMOTE-based augmentation. The proposed model obtained the overload detection rate F1-score of 0.916 and a ROC–AUC of 0.996, the target ECU recommendation obtained the accuracy of 0.935, and load-shift estimation, yielding an R² of 0.988 with low prediction error. This study also conducted the statistical test and ablation analysis, which observed that performance gains were consistent and attributable to key designs such as imbalance-aware learning, leakage control, and gated inference. The final results show that the proposed model is an effective and deployable solution for intelligent ECU load management in next-generation electric vehicles.
Mishra et al. (Thu,) studied this question.