With the rapid acceleration of consumer battery electric vehicles, motorsport applications have similarly accelerated to fill this market segment. Alongside Formula E, series such as Extreme E, eSkootr, and MotoE World Cup are showcasing the fast-paced advancements in high-performance electrified vehicles. The international engineering competition, Formula Student currently leads many of these advancements due to the open rule structure, diverse powertrain availability, and world-wide competition structure. Expansions in electrified powertrain, and lithium-ion energy storage have resulted large diversity in the selection designs, and resulting operating conditions for this world-wide competition. Furthermore, few competitions are currently transitioning to purely electric vehicle entries , while others such as, Formula SAE Michigan are further expanding electric vehicle entries. To deliver a competitive, optimal design analysis with a structured system integration direction is required; however, due to the varying levels of university support, human resources, and available funding, access to relevant lithium-ion cell data and analysis is diﬀicult to obtain. While open-source datasets for lithium-ion energy storage have seen massive development in recent years [2–4] there is still a gap in high-performance and high-power applications. In this work we present an open-source dataset to provide relevant information on prospective lithium-ion solutions for this application. This dataset aims to provide prospective researchers, students, and end users with the required information to characterise predictive electrochemical models, insight into expected lifetime, and bulk thermal properties. This paper will cover the experimental methods, generation of the novel drive cycles, and analysis of prospective lithium-ion batteries in this high- performance application. In addition, this paper will present key performance indicators for design of next-generation lithium-ion energy storage in motorsport applications.