Optimal Energy Management for a Li-Ion Battery/Supercapacitor Hybrid Energy Storage System Based on a Particle Swarm Optimization Incorporating Nelder–Mead Simplex Approach

Combining a high-power source like a supercapacitor with a lithium-ion battery for electric vehicle applications results in performance improvements, high efficiency, long lifetime, lightweight design, and relatively modest cost of the overall source. A hybrid energy storage system controlled by a smart energy management strategy can play a key role in the design and development of multisource electric vehicles. In this study, an optimal energy management strategy based on particle swarm optimization incorporating the Nelder–Mead simplex method is proposed. The goal of the proposed strategy is to minimize the battery power stress and improve its lifetime. This is achieved by coupling a rule-based method based on the knowledge of the battery and supercapacitor efficiency operating with a hybrid Particle Swarm–Nelder–Mead optimization algorithm. This latter approach is proposed to optimize the control parameters of the rule-based energy management strategy. Once the offline optimization algorithm is completed, the control method can be implemented online. The developed strategy is tested in a simulation environment and in an experimental platform using a power emulator test bench of a lithium-ion battery/supercapacitor hybrid energy storage system. The results, in terms of battery power stress and lifetime, are compared with a conventional rule-based method and a monosource with a single high-power lithium-ion battery. Obtained results show the effectiveness of the proposed strategy allowing the satisfaction of the requested performance with better battery usage. The evaluation results also demonstrate significant lifetime enhancements for the Li-ion battery, an increase of up to 20% as compared to the monosource based on a regular single battery.


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  • Accession Number: 01644212
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 29 2017 10:07AM