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This paper presents an optimized situation-based power management strategy (SB-PMS) for multi-source electric vehicles. The presented SB-PMS is based on defining optimal control strategies offline for different vehicles states. In such control schemes, multiple characteristic variables, i.e. vehicle speed and power demand, are implemented to define vehicle states. The selection and discretization of characteristic variables have a significant impact on the solution optimality of SB-PMSs. Based on a comprehensive analysis of literature, the variables vehicle speed, power demand, speed dynamics, and on-board state-of-charge have been selected to investigate their impact solution optimality of SB-PMS. These variables are depicted as axes in a multi-dimensional space (grid-space), whereinto vehicle states can be mapped. Multiple structures of grid-space are generated based on different constellations of axes and different discretization levels for each axis. Comparative evaluation of grid-space structures is based on two aspects: first, the optimality of situated solutions related to vehicle states; second, the ability to develop predictive models using the statistics of state transitions in grid-space. Results analysis, compared to rule-based (RB) and dynamic programming (DP), reveals the significant impact of certain grid-space structures to achieve near optimal results in both energy saving and prediction accuracy at different trip conditions. [ABSTRACT FROM AUTHOR]

This paper presents a novel method, advisory dynamic programming (AD-DP), for power management of a fuel cell hybrid vehicle (FCHEV). The presented method embraces a new understanding of vehicle states as driver-dependent and -independent states in time domain to define a suitable state space to be used for dynamic programming. Driver-dependent states are defined in terms of multiple characteristic parameters for vehicle speed and power demand, corporately. Driver-independent states are defined in terms of discrete values for supercapacitor's state of charge $SoC_{sc}$. Transition costs between all states in the state space are calculated offline and tabulated in look-up tables for online implementation. A state predictive model is developed based on the transition statistics of driver-dependent states for a suitable number of driving cycles. Backward calculation of the total transition cost for the predicted horizon in state space is used to define optimal power split strategy for the powertrain. The formulation of optimal control problem, in terms of situation-based solutions related to vehicle states, enables a significant reduction of computational steps and hence addresses the main challenge of real-time applications. The algorithm is adapted in terms of optimization horizon and number of discrete states for DIS to suit the real-time application. Experimental application of AD-DP, using an emulation test-rig, is conducted over different driving cycles. The obtained results reveal an improvement in energy efficiency up to 29% compared to the adaptive rule-based method. The contribution of this paper can be identified as: first, development of a corporate definition for vehicle states, that can be further implemented in optimization-based power management methods. Second, the formulation of an adaptive DP that requires lower computational steps and hence suits real-time applications in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]