A Power Distribution Strategy for Hybrid Energy Storage System Using Adaptive Model Predictive Control

Abstract

Management strategy of the hybrid energy storage system (HESS) is a crucial part of the electric vehicles, which can ensure the safety and efficiency of the electric drive system. The adaptive model predictive control (AMPC) is employed to the management strategy for the HESS in this article. First, an improved continuous power-energy method is applied in configuration of the system. The battery and supercapacitor (SC) models are described by the equivalent-circuit technique. Second, a novel predictive model considering the dc load under a semiactive topology is proposed. The AMPC with the proposed model can handle the strong nonlinearity and time-varying property of the HESS. Third, in order to lengthen battery life span and improve system efficiency, the energy loss of the system, the rate of battery current, and average energy of the SC are considered in the cost function. Moreover, control action of each step can be obtained by minimizing proposed cost function in the AMPC rolling horizon. Fourth, the process of deriving the cost function into standard quadratic programming problem is demonstrated. Finally, in order to prove the superiority of the proposed method, three different driving load cycle tests are performed for verification. The results illustrate that the AMPC has better performance in system efficiency and battery conservancy, where the peak current of the battery cell can be reduced by at least 24.4%, and the total energy loss can be reduced by at least 6.4% with the proportional integral (PI) and model predictive control methods. The ampere-hour throughput of battery and the root mean square of battery current can be reduced by up to 16.2% and 29.8%, respectively.