An Adaptive Sub-Optimal Energy Management Strategy for Hybrid Drive-Trains

Typically the energy management problem of a hybrid vehicle is formulated as an optimization problem, where the optimal power split between the prime mover and the secondary power converter is calculated off line based on a given driving cycle and solved numerically with dynamic programming techniques. An important constraint is that the energy level of the secondary power source at the end is the same as in the beginning. In real live the future driving cycle is not known a priori, making it difficult to calculate the exact optimal power split beforehand. To arrive at a practical real time control algorithm, a sub-optimal control law can be applied, where the end-point constraint is replaced by a term in the cost function that accounts for the change in energy; in case of a hybrid electric vehicle it represents the fuel equivalence of the stored reversible energy. In this paper it is reasoned that the reversible energy contains also kinetic and potential energy of the vehicle as well as energy stored in the secondary power source. By feedback control of the state of energy of the secondary power source, the amount of stored energy can be kept on a trajectory, such that the total amount of reversible energy remains constant. Kinetic and potential energy is proportional with vehicle mass, therefore this trajectory is adaptive to vehicle loading. In this paper simulations of an on-line strategy are included that show fuel consumption improvements of a distribution truck, close to those obtained with dynamic programming, validating the reasoning.