This paper suggests evolving Takagi-Sugeno-Kang (T-S-K) fuzzy models that characterize the nonlinear dynamics phenomena occurring in the longitudinal slip of Anti-lock Braking Systems (ABSs). The rule bases and the parameters of the T-S-K fuzzy models are evolved by an incremental online identification algorithm (IOIA). A set of real-time experiments is conducted in order to validate the evolving T-S-K fuzzy models that describe the dynamics of the longitudinal slip in an ABS laboratory equipment setup aiming the longitudinal slip control. The experimental results prove the very good performance of the T-S-K fuzzy models in terms of fast output responses and small root mean square error values. The performance comparison with similar T-S-K fuzzy models evolved by another IOIA and three nature-inspired optimization algorithms is included.