Unraveling Electrochemical–Thermal Synergy in Lithium-Ion Batteries: A Predictive Framework Based on 3D Modeling and SVAR

Energy shortage and environmental pollution have accelerated the adoption of lithium-ion batteries (LIBs) as efficient energy storage solutions. However, their performance and safety challenges under extreme temperatures highlight the urgent need for effective temperature control during charging and discharging, making a comprehensive understanding of electrochemical and thermal behaviors crucial. This paper develops a 3D electrochemical–thermal coupled model for 150 Ah lithium iron phosphate (LFP) batteries to investigate thermal behavior at varying charge–discharge rates. An integrated learning regression prediction system, incorporating a structured vector autoregression (SVAR) model, is subsequently proposed to analyze the interactions among multiple electrochemical and thermal variables. The temperature difference exceeds 5 °C at higher charging rates (1.3C, 1.5C), driven primarily by accelerated heat generation—especially reversible heat. Complex interactions exist between electrochemical and thermal parameters. When charging at 0.5C, voltage, current density, battery capacity, and the maximum temperature difference (MTD) are all significantly and positively correlated (p < 0.001). Under 1C discharge conditions, voltage exhibits a strong positive correlation with most thermal characteristic variables, and correlation coefficients across different operating conditions range from −0.9731 to 0.973. Finally, the proposed ensemble learning system exhibits excellent prediction accuracy, strong generalization, and robust trend analysis, with practical guiding value.