Multi-Objective Optimization of Energy Management Strategy on Hybrid Energy Storage System Based on Radau Pseudospectral Method - Yanwei Liu; Zhenye Li; Ziyue Lin; Kegang Zhao; Yunxue Zhu (IEEE Access, 2019)
In this study, a multi-objective optimization method based on the Radau pseudospectral method is proposed for the energy management strategy in the hybrid energy storage system (HESS). In the proposed method, by approximating state and control variables in the system with global interpolating polynomials, the optimal control problem (OCP) is transformed into a nonlinear programming problem (NLP) and solved by a sparse nonlinear optimizer. Further, the Pareto solution set is obtained by taking the energy consumption of the HESS and the equivalent life of the battery as objective functions. Three solutions representing different tradeoffs were selected for comparative analysis: minimum system energy consumption (5819.60 kJ), with battery life 68368 cycles; maximum battery life (76227 cycles), with energy consumption 5865.68 kJ; and the balanced tradeoff optimal solution with battery life 72488 cycles and energy consumption 5841.96 kJ. The results showed that for every additional 5 kJ in system energy consumption, the battery Ah-throughput was reduced by 0.053 Ah and its equivalent life extended by 876 cycles. Further, compared with the single-cell energy source, the balanced tradeoff optimal solution increased the battery life by 29.92% and decreased the system energy consumption by 1.79%. Thus, this work provides a fast and stable multi-objective optimization method for the energy management strategy of HESS and lays the foundation for obtaining optimal system parameters.