A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach

In this paper, an integrated rule-based meta-heuristic optimization approach is used to deal with a multi-level energy management system for a multi-source electric vehicle for sharing energy and power between two sources with different characteristics, namely one with high specific energy (battery) and other with high specific power (SuperCapacitors). A first (long-term) management level dynamically restricts the search space based on a set of rules (strategic decisions). A second (short-term) management level implements the optimization strategy based on a meta-heuristic technique (tactical decisions). The solutions to the optimal power sharing problem are be used to generate the power references for a lower (operational) level DC–DC converters controller. The Simulated Annealing meta-heuristic is used to define an optimized energy and power share without prior knowledge of power demand. The proposed scheme has been simulated in Matlab®, with models of energy sources for several driving cycles. Illustrative results show the effectiveness of this multi-level energy management system allowing to fulfill the requested performance with better source usage and much lower installed capacities.

► An integrated rule-based meta-heuristic approach for energy management is presented. ► An energy level and a power level are implemented as an optimization problem. ► We develop energy level with rule-sets and power level with Simulated Annealing. ► We model and simulate the approach for several cycles for EV with battery and SCs. ► The proposed approach is validated with different initial SCs’ SoC.