Peneklioglu, KaganBilen, KemalAltunay, FurkanYildirim, Ahmet Emre2025-04-132025-04-1320251359-43111873-5606https://doi.org/10.1016/j.applthermaleng.2025.126191https://hdl.handle.net/20.500.13091/9967Accurate prediction of battery heat generation is vital for the thermal management of electric vehicles, as it directly impacts performance, safety, and efficiency. Despite the abundance of experimental studies in literature, existing efforts often focus on specific battery chemistries, leaving a significant gap in universal heat generation models applicable across multiple cathode materials. This study addresses this gap by developing novel correlations for battery heat generation, resistance, and entropy coefficient using over 1 000 data from experimental studies spanning 2003 to 2023. Data extraction involved digitizing published graphs and applying curve-fitting, and regression techniques have been used to obtain correlations. The developed fourth-order expanded correlations predict battery heat generation for lithium-ion batteries with LCO, NMC, and NCA cathode materials under discharge rates of 0.5C, 1C, and 2C and temperatures between 293.15 K and 303.15 K, demonstrating very good agreement with experimental data and outperforming widely known correlations in the literature. Additionally, the correlations have been validated for average battery surface temperature, further proving their reliability for numerical modelling and thermal management applications. This study provides the first comprehensive and universal heat generation correlations for the most widely used cathode materials in lithium-ion batteries, addressing a critical need in literature and equipping researchers with a robust tool for thermal modelling and cooling system design.eninfo:eu-repo/semantics/closedAccessElectric VehicleLi-Ion BatteryBattery ChemistryBattery Heat GenerationCorrelationRegressionArticle10.1016/j.applthermaleng.2025.1261912-s2.0-105000461155