  One of the major limitations of optimization-based strategies for allocating
the power flow in hybrid powertrains is that they rely on predictions of future
power demand. These predictions are inherently uncertain as they are dependent
on complex human behaviours that are challenging to model accurately. This
paper proposes a data-based scenario model predictive control framework, where
the inputs are determined at each control update by optimizing the power
allocation over multiple previous examples of a route being driven. The
proposed energy management optimization is convex, and results from scenario
optimization are used to bound the confidence that the one-step-ahead
optimization will be feasible with given probability. It is shown through
numerical simulation that scenario model predictive control (MPC) obtains the
same reduction in fuel consumption as nominal MPC with full preview of future
driver behaviour, and that the scenario MPC optimization can be solved
efficiently using a tailored optimization algorithm.
