Battery system charging and discharging control method based on dynamic reconfigurable battery network

Abstract

The invention discloses a battery system charging and discharging control method and device based on a dynamic reconfigurable battery network, and the method comprises the steps: obtaining a load voltage demand in a discharging process or obtaining a charging voltage demand in a charging process, determining the target number of series-connected battery packs according to the voltage demand, and determining a target number of target battery packs according to the state information of each battery pack, determining target single batteries according to the state information of the single batteries in the target battery packs, and connecting the target battery packs containing the target single batteries to a system so as to supply power to a load or charge the battery packs. The discharging strategy can support the requirement of the load for high power, and the charging strategy can solve the balance problem between the monomers.

Description

technical field [0001] The present invention relates to the field of battery systems, in particular to a method and device for controlling charge and discharge of a battery system based on a dynamically reconfigurable battery network. Background technique [0002] With the integration of large-scale intermittent new energy sources such as wind power and photovoltaics, the stable operation of the power grid faces new challenges, and energy storage technology has become an indispensable part of the new power system because it can stabilize fluctuations and improve power quality. missing part. Among them, electrochemical energy storage has received extensive attention due to its advantages such as fast response speed and mature technical route. [0003] The core of electrochemical energy storage is a large-scale battery system and its management. In order to meet the requirements of load voltage and current, traditional battery systems usually connect a large number of batter...

AI Technical Summary

Problems solved by technology

Dynamic programming decomposes complex problems into sub-problems and solves them sequentially, but this method is very dependent on the battery model and parameters; deep reinforcement learning generates corresponding control methods through continuous training, but this method requires a large amount of calculation and requires a lot of The training data has high requirements on the hardware and is difficult to be used in real-time control occasions; the control method of N selecting k can achieve the balance between the monomers, but the existing strategy does not distinguish the difference between the charging and discharging process, and cannot take into account both charging and discharging optimum performance of the process

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