Iterative dynamic programming is a powerful method that is often used to solve finite-dimensional nonlinear constrained global optimal control problems. However, multi-dimensional problems are often computationally complex, and in some cases an infeasible result is generated despite the existence of a feasible solution. A new iterative multi-pass method is presented that reduces the execution time of multi-dimensional, loosely-coupled, dynamic programming problems, where some state variables exhibit dynamic behavior with time scales significantly smaller than the others. One potential application is the optimal control of a hybrid electrical vehicle, where the computational burden can be reduced by a factor on the order of 100 -- 10000. Furthermore, new regularization terms are introduced that typically improve the likelihood of generating a feasible optimal trajectory. Though the regularization terms may generate suboptimal solutions in the interim, with successive iterations the generated solution typically asymptotically approaches the true optimal solution.
