Representative energy management strategies for hybrid energy storage systems derived from a meta-review

Abstract

Hybrid energy storage systems integrate diverse storage technologies to enhance system performance, efficiency, and longevity. Despite a plurality of proposed energy management strategies to operate these systems and a significant number of reviews on this topic, the field lacks a systematic, actionable and reusable summary of available energy management strategies.

Therefore, we conducted a meta-review of available review articles to ascertain a joint base for representative energy management strategies for hybrid energy storage systems. In subsequent reviews of each determined class, we extracted, defined, and detailed core concepts, which were then implemented in Python for demonstration and analysis.

We identified four representatives: filter-based, deadzone-based, fuzzy-logic-based, and model-predictive-control-based energy management. Each one is discussed with its operational mechanisms and implementable equations and is illustrated through simulations. Notably, we excluded machine-learning-based candidates due to the limited foundation and generalizability in the current literature.

With the identified representatives, we seek to provide a foundation and framework for further development, including quantitative assessments of energy management performance in various configurations. Also, this work facilitates targeted and effective enhancements in energy management development for each class, accelerating future research and supporting industry stakeholders to develop more efficient renewable energy systems. To allow easy reuse and reproducibility, the source code is available at GitHub.