Investigation of dynamic programming for optimization of hybrid drive trains

Dynamic programming is an effective technique for the evaluation of the potential of optimal fuel consumption of drive trains, as it guarantees a globally optimal solution. This paper investigates two major problems associated with the application of dynamic programming. The first problem is the high computational complexity. Iterative dynamic programming is proposed as an alternative to dynamic programming in order to reduce the computational complexity, and issues with its implementation to discretized problems are addressed. The second problem is the loss of optimality due to the discretization of the continuous drive train model. The introduction of different errors and their propagation through the optimization process is investigated. The study of iterative dynamic programming and discretization errors is carried out for two test cases of hybrid marine drive trains models.