Stochastic dynamic programming in the real-world control of hybrid electric vehicles

Stochastic dynamic programming (SDP) is applied to the optimal control of a hybrid electric vehicle in a concerted attempt to deploy and evaluate such a controller in the real world. Practical considerations for robust implementation of the SDP algorithm are addressed, such as the choice of discount factor used and how charge sustaining characteristics of the SDP controller can be examined and adjusted. A novel cost function is used incorporating the square of battery charge (C-rate) as an indicator of electrical powertrain stress, with the aim of lessening the affliction of real-world concerns such as battery health and motor temperature, while allowing short spells of operation toward the system peak power limits where advantageous. This paper presents the simulation and chassis dynamometer results over the LA92 drive cycle, as well as the results of testing on open roads. The hybrid system is operated at several levels of aggressivity, allowing the tradeoff between fuel savings and electrical powertrain stress to be evaluated. In dynamometer testing, this approach yielded a 13% reduction in electrical powertrain stress without sacrificing any fuel savings, compared with a controller that does not consider aggressivity in its optimization.