###### Abstract
However, its preset equivalence factor cannot adapt to the real-time changing vehicle state, so the ECMS is often combined with prediction algorithms to realize the real-time regulation of the equivalence factor [22]. [PERSON] and [PERSON] [23] proposed a fuzzy-inference-based adaptive equivalent consumption minimization strategy (A-ECMS), which dynamically adjusts the equivalence factor based on the state of charge (SOC) as well as the power demand, which improves the fuel economy of hybrid vehicles. [PERSON] et al. [24] introduced a predictive ECMS framework combined with a long short-term memory (LSTM) neural network model to predict the vehicle speed with an ECMS. This combination enables the anticipation of the short-term SOC trajectory, thus facilitating the continuous adjustment of the equivalence factor in real time. Furthermore, [PERSON] et al. [25] utilized LSTM neural networks to predict the future vehicle speed and established an A-ECMS considering real driving conditions, which improved the overall vehicle economy and improved the battery charge maintenance. While the A-ECMS reduces energy consumption to some extent, it overlooks the issue of the energy source lifetime. Frequent charging and discharging lead to the rapid degradation of the energy source lifespan. The cost of the energy source lifetime degradation is usually higher than the cost of saved energy consumption. Consequently, many scholars have initiated efforts to integrate the energy system lifetime issue into ECMS frameworks. [PERSON] et al. [21] investigated a three-energy-source fuel cell hybrid vehicle, adjusting the tuning equivalence factor and dynamic current change rate of the fuel cell based on the state of health (SOH) of both the fuel cell and the battery. This adjustment ensured battery charge maintenance and prolonged the service life of the fuel cell. [PERSON] et al. [26] integrated fuel consumption and battery aging terms into the cost function, utilizing the Ah-throughput method to quantify battery aging, thereby enhancing the battery lifetime with a minimal fuel economy loss. [PERSON] et al. [27] utilized neural networks to identify driving styles, associating them with equivalence factors, and incorporated hydrogen consumption costs and fuel cell degradation costs into the objective function to effectively extend the fuel cell lifetime. [PERSON] et al. [28] proposed an A-ECMS that integrated fuel cell degradation, start-stop events, and the SOC into the cost function. This approach combined the state and dynamic characteristics of both the fuel cell and the battery with their respective weighting factors. The equivalence factors were adjusted online using a fuzzy controller. Although the energy system lifetime degradation of FCHEVs has gradually attracted attention, most scholars have only considered the lifetime degradation of a single energy source, ignoring the lifetime impact on other energy sources. In order to bridge the gaps of the above research, an adaptive equivalent consumption minimization strategy considering performance degradation (DA-ECMS) was proposed in this study to balance the fuel economy with the fuel cell and battery lifetime. The primary contributions of this paper can be summarized as follows:  
* The performance degradation of the fuel cell and battery was incorporated into the optimization objective of the A-ECMS to achieve a balance between the fuel economy and the energy source lifetime. * An optimal covariate predictor based on an LSTM neural network was proposed, which enabled the equivalence factors of the ECMS to vary in real time under operating conditions. * Performance degradation indices of the fuel cell and battery were selected, and the corresponding performance degradation models were developed. The remainder of this study is organized as follows. The FCHEV is modeled in Section 2. In Section 3, the DA-ECMS is proposed, which combines speed prediction and an optimal covariate predictor based on an LSTM neural network.