Analytics based energy loss optimization for lithium-ion energy storage system clusters with online fault-tolerance functionality

In the design of traditional energy management strategies for energy storage system clusters in response to grid power demand, the influence of cascade converter on systematic energy consumption characteristics are generally ignored, making it unable to achieve energy loss optimization within service life. In this paper, a high-order accurate energy consumption characteristic model is established by comprehensively considering the power efficiency characteristics of cascade converters, and a real-time analytics based optimal energy management strategy is proposed. It is inspired by Taylor expansion based order-reduced iterative solving, which can analytically characterize the correlation between systematic energy loss and power distribution among energy storage systems. Based on the hardware-in-the-loop simulation, the results demonstrate that the accuracy of high-order energy consumption characteristic modeling for energy storage systems is up to 99.8%, and the real-time analytics based systematic energy loss optimization can be ensured. Furthermore, around 28.2% improvement in the steady-state state-of-charge balancing performance and at least 4% service life enhancement can be guaranteed in comparison with the typical state-consensus based energy management strategy. In the meanwhile, the online fault-tolerance functionality is inherent as it is indeed the special case of the normal operation.