A data-driven framework for forecasting transient vehicle thermal performances

Transient vehicle dynamics are deeply affected by the spatial distribution of tire temperature, heat generation, and heat dissipation capacity. However, the tire testing process involves numerous parameters, adding the complexity to monitoring the tire's thermal properties variation. To address this issue, this paper proposes a data-driven framework that combines the well-known Magic Formula with scaling factors and a long- and short-term memory (LSTM) neural network. The framework forecasts tire thermal properties and functionality under given conditions by learning from time-series data on tire operating conditions and mechanical characteristics. It predicts future spatially varying thermal properties of the tire, including temperatures, heat generation, and heat dissipation. This is achieved with a high degree of forecasting ability quantified by train size/test size ratios equaling 22 and 38% with an impressive Relative Root Mean Square Error (RRMSE) around 1-2%. Here, this framework is applied to quantify the impacts of various operating conditions, including vertical load, slip angle, and vehicle speed, on heat transfer for three crucial radial positions on the tire. Our results show that the vertical load is the primary influencing parameter among all operating conditions. The knowledge about transient tire dynamics will be advantageous to their performance optimization.