Adaptive receding horizon control for battery energy storage management with age-and-operation-dependent efficiency and degradation

Abstract

A receding horizon controller for optimal, adaptive scheduling of a battery energy storage system is proposed. Batteries degrade as a function of their operation and age; operation also affects battery energy storage system efficiency. Many state-of-the-art energy management techniques simplify the relationship between state of charge (SoC) and degradation, assume the efficiency to be constant, and neglect the impact of state of health and variable efficiency on the SoC progression used to quantify the degradation. This results in significant deviations from optimal operation, over-estimates of remaining capacity, and inaccuracies in the calculation of further degradation. The controller developed in this paper adapts the operating schedule of a battery energy storage system to its age, condition, and state of charge. This is achieved by identifying the factors which affect the degradation processes and efficiency in lithium-ion battery systems and quantifying their impacts, resulting in a system-level model which incorporates all the pertinent parameters. Integrating the developed model into the problem formulation of the receding horizon controller enables online evaluation of age and operation-dependant efficiency and degradation, which allows the controller to make adaptive decisions, minimising operating costs. The proposed controller was tested using data from a real-world case study and the results shows the controller outperforms two existing energy management techniques, whilst delivering the same network services.