# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 1 Introduction  
The power battery has played an essential role in many intelligent systems. Taking battery electric vehicles as an example, power batteries provide driving power for the whole vehicle, which is the power source of electric vehicles. The driving range, economy, and safety of electric vehicles are all determined by the performance of power batteries. The development of power batteries is also a key constraint for electric vehicles' large\(-\)scale and rapid development. As the core component of various power systems, power batteries must function stably to ensure the reliability and security of the entire system. However, with recycling, battery degradation and battery failure are very common. The ability of a battery to provide energy gradually decreases as its lifespan increases. Once the power battery degrades below the necessary operating threshold, it no longer performs its expected functions and is prone to failure; this will not only affect the regular operation, resulting in high maintenance costs, but also may pose significant security risks, resulting in disastrous consequences. Therefore, it is exceedingly essential to accurately and reliably evaluate the aging situation of batteries and create timely scheduling management strategies [1]. Many studies have been conducted to improve battery performance by proposing various equalization means to solve battery inconsistencies or to monitor the battery aging state. Battery equalization hopes to bring the energy of the battery to a balanced state and guarantee that the difference between the batteries in the operating condition is in asafe range, with a total of three methods of battery selection, passive equalization, and active balancing, which are achieved: by designing the circuit [2]; from the perspective of a single battery; the battery state of capacity (SOC) estimation [3]; the state of health (SOH) representation [4]; and the remaining useful life (RUL) [5] are monitored and predicted from various perspectives. However, the battery will only be replaced once it reaches the level of obsolescence. The aging process will not process the battery, so dynamic planning cannot be achieved. Therefore, from the perspective of decision\(-\)making, in the context of battery inconsistency, the optimization of power battery management in different scenarios is automatically achieved through a method with simple models, rapid feedback, and uncomplicated dynamic adjustment using relevant data. It can effectively prolong battery life, reduce system complexity, and improve computing efficiency. At present, the literature related to power battery scheduling optimization can be divided into three categories: hardware circuit design [6], heuristic algorithm [7], and reinforcement learning algorithm [8]. The battery management system is vital to the battery power supply, and its technology is relatively complex. Some scholars design circuits and optimize communication protocols from the perspective of hardware. [PERSON] et al. [9] proposed a distributed battery management system, created the central control module and sampling module, analyzed the health status, developed a test platform, and verified the compatibility and stability of the design. [PERSON] et al. [10] proposed a novel power management strategy to manipulate the power control unit (PCU) of the battery by fuzzy logic control (FLC) and validated and experimented with a simulation using MATLAB software. The proposed strategy can reduce energy loss and improve battery life. [PERSON] et al. [11] designed and simulated a two\(-\)stage hybrid structure of a battery supercapacitor (SC). An easy\(-\)to\(-\)implement control algorithm based on the decision was adopted to share the power delivery capacity of the hybrid battery supercapacitor in proportion, effectively dealing with light load changes and realizing economic advantages in the industry.