A Supervisory Energy Management Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System

Abstract

One of the major challenges in a battery /ultracapacitor hybrid energy storage system (HESS) is to design a supervisory controller for real-time implementation that can yield good power split performance. This manuscript presents the design of a supervisory energy management strategy that optimally addresses this issue. In this work, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. In this HESS energy management problem, a detailed dc-dc converter model is considered to include both the conduction losses and the switching losses. The optimization problem is numerically solved for various drive cycle datasets using dynamic programming (DP). Trained using the DP results, an effective and intelligent online implementation of the optimal power split is realized based on neural networks (NN). The proposed online intelligent energy management controller is applied to a midsize EV. A rule-based control strategy is also implemented in this work for comparison with the proposed energy management strategy. The proposed online energy management controller effectively splits the load demand and achieves excellent result of the energy efficiency. It is also estimated that the proposed online energy management controller can extend the battery life by over 60%, which greatly outperforms the rule-based control strategy.Index Terms -Electric vehicle, hybrid energy storage system, multi-objective optimization, neural networks, ultra-capacitor.

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