This invention discloses a method, device, and
system for optimizing maintenance decisions in
energy storage systems based on
reinforcement learning-based multi-component lifetime collaborative modeling. The method constructs a collaborative lifetime decay model for key power devices (including IGBTs and
DC bus capacitors) in the
energy storage converter, collects operational data in real time, and establishes a multi-state fusion method for characterizing operational health. Furthermore, it utilizes
reinforcement learning algorithms to optimize maintenance strategies. By jointly modeling the lifetime
recovery effect, cost,
failure risk, and
system stability of different maintenance behaviors, a multi-objective reward function is constructed to achieve
adaptive optimization of maintenance decisions. This invention can automatically determine the timing of overhaul or preventative maintenance based on the equipment's operating status and aging condition, eliminating the need for fixed-cycle manual maintenance, effectively reducing failure rates and maintenance costs. This invention is applicable to intelligent operation and maintenance and
state management scenarios for
energy storage converters.