Energy management (EM) strategies are used to decide intelligently on how to provide energy to the total vehicle load from the multiple energy sources involved in HEV powertrains. Traditional approaches can be mainly classified into two types: rule-based and optimization-based methods. However, all these methods have relied on predefined speed profiles, which is not very realistic and is unknown in practice.
The new optimization scheme aims to find optimized power sharing and the vehicle speed for a given journey, specified in terms of the road geometry and average speed. The problem is formulated into an optimal control framework. The fuel consumption is minimized subject to a set constraints, used to keep the operating conditions of the powertrain inside their
admissible range, and to guarantee the driving safety and comfort. The solution is obtained by using an optimal control suite named PINS (see the reference for more details).
A simple example shows the importance of the speed optimization in the EM. For a 1km straight road without traffic, the optimized speed (red) and the regular driving style (blue) are compared.
The optimized fuel consumption variation with average speed for the two driving styles is also shown. It can be observed that the selection of the efficient driving style (instead of the regular one) leads to a remarkable reduction in fuel consumption and hence it is appealing.
It is indeed difficult for a human driver to follow this optimized speed pattern. However, this can be integrated into an Advanced Cruise Control that can easily assist the driver.
 F. B. E. Bertolazzi and M. D. Lio, “Real-time motion planning for multibody systems: Real life application examples,” Multibody System Dynamics, vol. 17, no. 2-3, pp. 119–139, 2007.