To address technical challenges in capacity configuration, safety design, energy management, and energy-saving evaluation for urban rail transit regenerative braking energy utilization systems, this paper proposes the new-generation stored regenerative braking energy utilization system, eRAM. We develop: (1) capacity optimization model and algorithm based on parameter decomposition; (2) a multi-level safety architecture considering rail transit application scenarios; (3) an interval energy dynamic allocation algorithm based on key parameter identification; (4) distributed cooperative control strategy based on deep reinforcement learning; and (5) data-driven condition monitoring and life prediction of energy storage components based on data driven. We present the basic principles and key technologies of each major component and evaluate their demonstration performance on the Beijing Metro Batong Line, providing a useful reference for advancing regenerative braking energy recovery and utilization in urban rail transit.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |