A distributed optimization approach for complete vehicle energy management

In this paper, a distributed optimization approach is proposed to solve the complete vehicle energy management (CVEM) problem of a hybrid truck with several controllable auxiliaries. The first part of the approach is a dual decomposition, which allows the underlying optimal control problem to be solved for every subsystem separately. For the second part of the approach, the optimal control problem for every subsystem is further decomposed by splitting the control horizon into several smaller horizons. Two methods for splitting the control horizon are used; the first method uses alternating direction method of multipliers and divides the horizon a priori, while the second method divides the horizon iteratively by solving unconstrained optimization problems analytically. We demonstrate the approach by solving the CVEM problem of a hybrid truck with a refrigerated semitrailer, an air supply system, an alternator, a dc-dc converter, a low-voltage battery, and a climate control system. Simulation results show that the fuel consumption can be reduced up to 0.52% by including smart auxiliaries in the energy management problem. More interestingly, the computation time is reduced by a factor of 64 up to 1825, compared with solving a centralized convex optimization problem.