The service life of equipment is generally linked to degradation factors depending on its operating conditions, including the rate of use and the frequency of the switching modes. The novel operating mode management proposed in this paper takes into account equipment lifetime in addition to all the previously mentioned requirements. This algorithm does not rely only on real-time data, as is traditionally presented in the literature, but also integrates predictive operating data. Therefore, it can be considered as a hybrid operating mode management as it embeds both predictive and event data, which yields improved results with respect to traditional event-driven management. This allows to optimize a criterion over a finite horizon, and, hence, an optimal sequence of the switching times of the different components of an energy system are generated. While the proposed approach is considered to be generic, it is illustrated by the production of green hydrogen from renewable sources. In order to ensure the operating safety and energy efficiency of the system, the objective is to maximize the life duration of the electrolyzer and the batteries by avoiding excessive stored quantities. Simulations using data obtained from a laboratory platform which replicates the process at a smaller scale highlight the effectiveness of the proposed approach.