Application of artificial intelligence in long-duration redox flow batteries storage systems

doi:10.19799/j.cnki.2095-4239.2024.0709

Abstract

Abstract:

In recent years, artificial intelligence (AI) has made significant advancements in battery design and optimization, showing particular promise in the study of redox flow batteries (RFBs). RFBs are attractive for their low cost, scalability, long cycle life, and high safety, positioning them as critical in advancing new energy storage systems. However, traditional experimental and simulation methods have been inefficient in navigating the design space of RFBs and uncovering their complex physicochemical processes. Our research team presents a novel approach that synergizes computational simulation with data-driven AI technologies to develop a highly interpretable, multiphysics-driven model. This model is further refined through machine learning enhancements, improving the analysis and optimization of RFB designs. Our findings indicate that machine learning models, especially the Gradient Boosting model, are highly effective in predicting voltage efficiency, coulombic efficiency, and capacity. Key factors influencing these metrics were identified using SHAP analysis and interpreted through electrochemical reaction mechanisms, providing a scientific foundation for optimizing battery performance. Additionally, we developed a large language model tailored specifically for the RFB field. By employing refined prompt engineering and text analysis techniques, this model reduces errors typically known as "hallucinations", thus significantly improving the accuracy of information processing. This research underscores the transformative potential of AI-driven simulation and optimization in enhancing the design and performance of RFBs, with the ongoing evolution of computational capabilities and algorithms likely to broaden AI applications in RFBs and other energy storage technologies significantly.

Key words: artificial intelligence, redox flow batteries, machine learning, large language model

CLC Number:

TM 911

Ziyu LIU, Zekun JIANG, Wei QIU, Quan XU, Yingchun NIU, Chunming XU, Tianhang ZHOU. Application of artificial intelligence in long-duration redox flow batteries storage systems[J]. Energy Storage Science and Technology, 2024, 13(9): 2871-2883.

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输入参数	范围/类别	单位
流速	12.5～200	mL/min
电极类型	CC, CF, CP, GF, SCC, TCC, TGF, TiN-3D GF	—
电极尺寸	4～50	cm²
膜类型	IEM, N212, N115, N117	—
反应物(FeCl₂, CrCl₃)浓度	0.5～1.5	mol/L
HCl浓度	1～4	mol/L
催化剂——Bi³⁺的浓度	0～5	mmol/L
催化剂——In³⁺的浓度	0～15	mmol/L
电流密度	40～320	mA/cm²
循环数	每五步测量一次	—

模型	电压效率			库仑效率			容量
模型	R²	MSE	MAE	R²	MSE	MAE	R²	MSE	MAE
Linear Regression	0.8843	2.9735	1.3319	0.4061	1.1773	0.8998	0.8520	0.0321	0.1495
Extra Trees	0.9764	0.5179	0.5450	0.7989	0.4382	0.5060	0.9890	0.0023	0.0318
Random Forest	0.9608	0.8015	0.6477	0.8642	0.2691	0.4275	0.9854	0.0026	0.0342
Gradient Boosting	0.9859	0.3608	0.4309	0.9212	0.1522	0.2924	0.9940	0.0011	0.0235

模型	电压效率			泵基电压效率
模型	R²	MSE	MAE	R²	MSE	MAE
Linear Regression	0.8843	2.9735	1.3319	0.9061	2.7833	1.1167
Extra Trees	0.9764	0.5179	0.5450	0.9689	0.5232	0.5560
Random Forest	0.9608	0.8015	0.6477	0.9642	0.7742	0.7275
Gradient Boosting	0.9977	0.2344	0.1145	0.9987	0.2119	0.1918

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