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This work provides a real-time power allocation algorithm to address uncertain actual driving situations for fuel cell hybrid vehicles. To predict the vehicle speed under nondeterministic driving conditions, a fusion prediction model is developed based on the advantages of the Markov chain and neural network. The optimal power splitting decision in each receding horizon is then solved using the Pontryagin's minimum principle (PMP) method, considering fuel consumption, State of Charge (SOC), and performance degradation. A degradation model of electrochemical active surface area (ECSA) based on Pt catalyst dissolution was developed. Then the effect of the energy management algorithm on fuel cell degradation was evaluated using the degradation model. Compared with the two conventional real-time power splitting strategies, the approach suggested in this research can better reduce the fuel consumption and maintain the stability of battery SOC with a lower fluctuation while taking into account the degradation of the fuel cell. • Real-time power allocation for FC hybrid vehicle under non-deterministic conditions. • Model fusion velocity prediction based on historical datas. • Power optimization in rolling prediction horizon using PMP method. • Using ECSA to assess the effect of power allocation strategies on FC durability. [ABSTRACT FROM AUTHOR]

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]