A two-layer hierarchical optimization framework for the operational management of diesel/battery/supercapacitor hybrid powered vehicular propulsion systems

Abstract

The operational management of vehicular hybrid propulsion systems (HPSs) is critical to its fuel consumption, overall efficiency, and life-cycle cost. With the help from hybrid energy storage systems (HESS), the conventional problem associated with the fluctuating load profile of vehicular system can be addressed. However, the diverse power/energy/efficiency characteristics of different energy sources leads to complex operational management strategies that coordinate them under different operational scenarios. In this paper, a two-layer hierarchal optimization framework is proposed to improve the performance of vehicular HPSs. In the first layer, the fuel consumption of the system is focused and the coordination strategy between power generation and energy storage is optimized by adopting adaptive neural fuzzy inference system based on the dynamic programming (ANFIS-DP) strategy. Further, the operational loss of the HPSs is considered as the secondary optimization target, the coordination strategy of HESS is optimized by adaptive low-pass filtering (ALPF) strategy. To demonstrate the advantage of the proposal, a shipboard HPS is involved as the benchmark study case, a hardware-in-the-loop test is conducted. The results show that the ANFIS-DP strategy reduces fuel consumption by 14.3%, and the suggested ALPF strategy may minimize system loss by up to 11%, when compared with more conventional methodologies.