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As an emerging power source for new energy vehicles, the proton exchange membrane fuel cell-lithium battery hybrid power system still faces challenges such as difficulty in remaining life prediction and unreasonable energy allocation management. This study investigates how electrochemical surface area degradation and carbon corrosion in the catalyst layer affect the proton exchange membrane fuel cell output power. An integrated assessment system combining electrochemical surface area degradation and carbon corrosion, which could effectively evaluate degradation in the proton exchange membrane fuel cell degradation is proposed. To assess the lifespan of proton exchange membrane fuel cells, the electrochemical surface area degradation and carbon corrosion are utilized as key indicators, which in turn lead to the development of a semi-empirical model. To solve the problem of energy allocation management, this paper proposes a predictive-feedback optimization method that combines data-driven and model-driven approaches. The method includes a hybrid prediction model of a long short-term memory-convolutional neural network, an improved rapid optimization strategy of grey wolf optimizer-particle swarm optimization, and an attention-enhanced fuzzy neural network controller. The inference capability of the convolutional neural network is utilized to correct the larger prediction error of long short-term memory during short-term drastic changes, realizing the maximization of energy utilization efficiency, minimal hydrogen consumption, minimization of operation cost, and optimization objectives for information containing a wide time horizon. Tests of experiments show that the implementation of this control scheme effectively postponed the deterioration of the proton exchange membrane fuel cell-lithium battery hybrid power system, resulting in an extended lifespan. [Display omitted] • Integrating ECSA and carbon corrosion data for a holistic system understanding. • Pioneering a PEMFC model, merging ECSA degradation and carbon corrosion. • Introducing predictive optimization for broad-time performance maximization. [ABSTRACT FROM AUTHOR]