Tire lateral forces have an important impact on vehicle stability and vehicle ride comfort. In general, the intelligent tire lateral forces are estimated using lateral nodal displacements or data-based methods, which are easily disturbed by measurement noises and lack accuracy in the case of small test numbers. This paper proposes a model-based lateral force estimator for intelligent tires, which includes a mathematical model, a signal processing algorithm, and a Kalman Filter. As the core of the lateral force estimator, the mathematical model is proposed based on the three-dimensional ring model and can describe the analytical relationship between the lateral acceleration signal and the lateral force. Based on the mathematical model, the optimal state observation of the intelligent tire lateral forces is realized using a Kalman Filter. The performance of the lateral force estimator is validated through lateral slip tests under two different vertical loads performed on the MTS Flat Trac III test bench. The results show that the lateral force estimator’s average normalized root mean square (NRMS) error is 5.83%. Compared with the data-based and model-based methods in previous studies, this model-based lateral force estimator provides an efficient method to estimate the intelligent tire lateral forces using fewer tests.
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