The invention relates to the technical field of intelligent charging management, and discloses a charging pile management method based on artificial intelligence, and the method comprises the steps: S1, charging pile scheduling modeling: building a charging pile scheduling mathematical model based on a charging demand and a power grid load, and setting a state space, an action space and a reward function; s2, reinforcement learning optimization scheduling: a multi-agent reinforcement learning algorithm is utilized to train charging pile agents, and a charging power distribution and queuing strategy is optimized; and S3, optimizing power distribution through a game theory: constructing a game model among the charging piles, and solving an optimal power distribution scheme through Nash equilibrium. The long-short-term memory self-encoder is adopted for equipment health state analysis, potential faults can be recognized in advance, early warning can be given out in advance, sudden faults of equipment are avoided, and compared with a regular inspection and manual intervention mode in the prior art, through data-driven intelligent prediction, the maintenance cost is greatly reduced, and the equipment downtime is greatly shortened.
本发明涉及智能充电管理技术领域,公开一种基于人工智能的充电桩管理方法,包括:步骤S1,充电桩调度建模:基于充电需求、电网负载,构建充电桩调度数学模型,设定状态空间、动作空间及奖励函数;步骤S2,强化学习优化调度:利用多智能体强化学习算法训练充电桩智能体,优化充电功率分配和排队策略;步骤S3,博弈论优化功率分配:构建充电桩间博弈模型,通过纳什均衡求解最优功率分配方案。采用长短时记忆自编码器进行设备健康状态分析,能提前识别潜在故障,提前发出预警,避免设备的突发性故障,相比于现有技术中定期检查和人工干预的方式,本发明通过数据驱动的智能预测,大大降低维护成本和设备停机时间。
Charging pile management method based on artificial intelligence
一种基于人工智能的充电桩管理方法
2025-05-16
Patent
Electronic Resource
Chinese
IPC: | B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen |
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