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Numerous control strategies of temperature regulation have been carried out for proton-exchange membrane fuel cell systems including a cooling fan in order to ensure operation at the desired condition and extend the lifetime of the fuel cell stack. However, most existing control strategies are developed without considering the efficiency limitation of the cooling system such that the cooling fan may be unable to eliminate the additional heat. Moreover, there are unknown modelling errors, external disturbance and noise during modelling and experiment processes for fuel cells. Due to those unknown dynamics, the conventional control strategies may fail to achieve the expectant results. To address this issue, an alternative control strategy is proposed in this paper, which consists of a composite proportional-integral (PI) controller with an unknown system dynamics estimator. First, the control strategy is developed by reducing the temperature of input air through the humidifier and simultaneously increasing the mass flow of air in order to eliminate the excess heat that a cooling fan cannot remove. Moreover, an unknown system dynamics estimator is proposed in order to compensate the effect of the unknown dynamics. The construction of the estimator is designed through finding an invariant manifold which implies the relation between known variables and the unknown manifold. The invariant manifold is derived by applying a simple low-pass filter to the system which is beneficial to avoid the requirement of the unmeasurable state derivative. Furthermore, the proposed estimator is easily merged into the proposed PI control strategy and ensures the exponential convergence of estimated errors. Besides, the estimator is further modified such that the derivative of the desired temperature is not required in the controller. Finally, numerical simulations of the PEMFC system are provided and the results illustrate the efficacy of the proposed control strategy., This work was partially funded by the Spanish National Project DOVELAR (ref. RTI2018-096001-B-C32), the AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482), and the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656). )is work was partially funded by the Chinese Scholarship Council (CSC) under grant (201808390007). )is work was partially funded by National Natural Science Foundation of China under Grants 61922037 and 61873115.