To ensure a reliable result, data used for battery modelling or prediction should be limited to datasets wherein the production methodology is well known. Therefore, only measured data such as time, current, voltage or temperature should be collected from cyclers. The use of data calculated by the test equipment needs to be weighted.
In our increasingly electrified society, lithium–ion batteries are a key element. To design, monitor or optimise these systems, data play a central role and are gaining increasing interest. This article is a review of data in the battery field. The authors are experimentalists who aim to provide a comprehensive overview of battery data.
Besides the cell manufacturing, “macro”-level manufacturing from cell to battery system could affect the final energy density and the total cost, especially for the EV battery system. The energy density of the EV battery system increased from less than 100 to ∼200 Wh/kg during the past decade (Löbberding et al., 2020).
The limited resources and space in the laboratory restrict the research activity on the battery system. Therefore, more collaboration between academic researchers and battery manufacturers could help the development of battery systems. Recycling becomes an inevitable topic with the surging of LIB manufacturing capacity.
As shown in Section 2, battery data production methods suffer from heterogeneity. To minimise the impact of this problem, standard test methods should be preferred. In , Gabbar et al. provide an overview of the various existing standards for battery testing.
Current battery housing designs 4, 5, typically made of solid metallic materials and located at the bottom of the vehicle, are usually heavy to ensure adequate protection. To progress the state-of-the-art battery housing design, efforts have been devoted towards lightweight, high mechanical performance, and efficient thermal management 6.