Kalman Filter‐Enhanced Grey Wolf Optimization for SVR‐Based Engine Compartment Temperature Prediction in Intelligent Transportation Vehicles

ABSTRACT

Engine‐compartment thermal design requires economical prediction and physical assessment of candidate modifications. This study integrates field‐synergy‐guided input selection, support vector regression and Kalman‐smoothed grey‐wolf hyperparameter search for coolant outlet temperature and drive‐shaft sheath temperature . All 249 CFD cases are assessed using five outer and three inner folds, with scaling, covariance or EMA settings and SVR parameters selected within the inner procedure. KF‐GWO‐SVR achieves pooled out‐of‐fold RMSEs of 0.5678 and 1.6051, respectively. Equal‐budget comparisons with tuned EMA and nine‐start GWO show target‐dependent differences: KF has slightly higher errors and lower RMSEs than these two controls. The gain analysis characterises the transition from initial covariance‐dependent smoothing towards a steady EMA‐like weight. Frozen deployment models provide consistent conditional interaction surfaces, a finite‐candidate Pareto approximation and a selected design. Its rounded implementation is assessed against a matched geometry control in 12 independent paired vehicle‐test blocks, comprising 24 complete thermal cycles. Mean reductions are 1.183 for (95% CI 1.054–1.313) and 5.450 for (4.963–5.937), and every reported endpoint is below the project temperature limit. The frozen models predict larger reductions of 2.277 and 8.230. The study combines controlled predictive evaluation, explicit search‐mechanism analysis and a paired engineering assessment, with KF used as an optional search‐guidance strategy.