A perspective on DeepSeek application in energy storage research

doi:10.19799/j.cnki.2095-4239.2025.0189

Abstract

Abstract:

During the global energy system's transition to renewable energy, energy storage technology has emerged as the core regulatory unit of new power systems, yet it faces multifaceted challenges, including inefficient material development, complex system optimization, lagging safety management, and imperfect market mechanisms. The DeepSeek large language model, with its low energy consumption, high efficiency, and exceptional reasoning capabilities, proffers an innovative pathway to address critical bottlenecks in energy storage. Through core technologies such as multi-head latent attention, DeepSeek mixture-of-experts models, and multi-token prediction, DeepSeek significantly reduces energy costs in both model training and inference. Its broad application prospects in energy storage research are expected to drive a paradigm shift from “trial-and-error” to “intelligent design” in materials development, establish multi-scale coupled digital twin frameworks for system optimization, transform safety management from passive response to proactive early warning, and create data-driven dynamic market evaluation systems for policy analysis. The “system symbiosis and energy-efficiency co-evolution” development paradigm provides a technological foundation for the deep integration of artificial intelligence with clean energy technologies, potentially accelerating the construction of carbon-neutral computing infrastructure and ushering energy storage technology into an intelligent new era.

Key words: DeepSeek, large language model, artificial intelligence, energy storage technology

CLC Number:

TP 18

Yuchen GAO, Weilin LI, Xiang CHEN, Yuhang YUAN, Yilin NIU, Qiang ZHANG. A perspective on DeepSeek application in energy storage research[J]. Energy Storage Science and Technology, 2025, 14(2): 467-478.

Figures/Tables 8

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Fig. 6

Table 2

Comparison of design scheme for 600 Wh/kg all solid state lithium metal battery proposed by DeepSeek and GPT-4o"

设计角度		DeepSeek-R1		GPT-4o
核心材料	正极材料	富锂锰基氧化物 $≥$ 300 mAh/g		富锂镍钴铝氧化物镍钴锰氧化物钴酸锂
	负极材料	锂金属负极 3860 mAh/g		锂金属负极
	电解质	硫化物电解质(Li₁₀GeP₂S₁₂) $≥$ 300 mS/cm		硫化物电解质氧化物电解质聚合物电解质

界面优化		正极	三维互穿 Li₃PO₄-Li₂S梯度涂层	界面涂层优化固态电解质-电极的界面
界面优化		负极	锂合金缓冲层	界面涂层优化固态电解质-电极的界面

电池结构		双极堆叠		薄膜电池设计提高电导率
		超薄集流体
		质量分配优化正极活性物质占比 $≥$ 70%

理论能量密度计算		610 Wh/kg		无

Table 2

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Question	DeepSeek-R1	DeepSeek-V3	GPT-o1	GPT-4o	Qwen-plus
total	89	81	83	71	76
Q1	9	8	8	9	8
Q2	9	7	8	7	8
Q3	9	8	7	7	7
Q4	9	9	8	8	9
Q5	9	9	9	7	8
Q6	9	9	8	8	8
Q7	8	8	6	6	8
Q8	8	7	7	7	7
Q9	9	8	8	7	8
Q10	10	8	9	5	5

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