Recent advancements in battery state of power estimation technology: A comprehensive overview and error source analysis

As a crucial indicator of lithium-ion battery performance, state of power (SOP) characterizes the peak power capability that can be delivered or absorbed within a short period of time. Accurate SOP estimation is therefore essential for electric vehicles to ensure their safe and efficient operations during power-intensive driving tasks. This article provides a comprehensive overview of the current SOP estimation technology, structuring a systematic exploration of all key steps in the entire development flow alongside recent advancements. First, we investigate the design of battery safe operation area, detailing various safety constraints from macro scale to micro scale. Second, we analyze the electrical behaviors of batteries under diverse peak discharge and charge modes, illustrating their impacts on peak power performance and discussing potential application scenarios. We then extensively review critical techniques covering battery modelling and SOP estimation algorithms, emphasizing their significant contributions and specific considerations. Moreover, we present an in-depth error source analysis to unveil associated error propagation pathways, shedding light on how each type of error impacts the SOP estimation performance. Our findings indicate that while progress has been made, significant challenges still persist, highlighting the need for continued research into more robust, scalable, and intelligent SOP estimation technology. Looking forward, SOP estimation technology is poised to make great headway for next-generation battery management systems. This progress will encompass multiple facets, such as the extensive integration of artificial intelligence and digital twin technologies, as well as the development of a collaborative architecture that spans cloud, edge, and endpoint systems. • An extensive overview of current SOP estimation technology for LIBs in EVs • A discussion of relevant factors defining battery SOA over a wide operational scale • An exploration of various peak operational modes with their advantages and limitations • A survey of state-of-the-art battery models and algorithms for online SOP estimation • An dissection of all error sources and their propagation pathways in online SOP estimation