Developing a general pretrained multimodal battery model with small parameters based on semantic detection

doi:10.19799/j.cnki.2095-4239.2024.0092

Abstract

Abstract:

The advent of ChatGPT signifies the birth of a new scientific research paradigm centered around "pretraining+fine-tuning". Companies such as OpenAI will lead the path toward artificial general intelligence (AGI) models. This indicates that artificial intelligence can surpass human intelligence and solve universal problems. AGI represents a model that is not designed for solving specific problems and even has the ability of self-learning. However, ChatGPT and other models still use texts combined with images as inputs. For a battery system, text information is less and most data as input is multimodal, such as temperature and voltage-current curve. The results related to a battery include the state of the battery including its charge and health, remaining useful life, whether there is a turning point in battery performance diving, and even the assessment of secondary (gradient) use of the battery without previous data. This means that ChatGPT can also help solve the battery system problem; however, its method can involve extra and complex solutions for minor problems even if AGI may solve the current battery problems in the future. Simultaneously, AGI can have huge parameters that are not suitable for offline operation of electric vehicles. We anticipate that AGI for a battery must have its own language and understand the physical and chemical processes during the operation of the battery. If AGI for batteries can understand why batteries become bad for example the lithium dendrites, they should predict all types of battery including all solid state battery. This review discusses how to redesign a battery model, including character representation, data distribution, pretrained methods and strategies, and fine-tuning for various tasks. In addition, minor parameters for the model should be concentrated for offline prediction and under international conditions. We will introduce the stages, problems, and evaluation indexes for developing a pretrained multimodal battery general model with minor parameters based on semantic detection (PBGM). We also present the three-step development strategy in PBGM by the Institute of Physics, Chinese Academy of Sciences (PBGM-IOPCAS).

Key words: multimodal, artificial general intelligence, pretrained, state of battery, semantic detect

CLC Number:

TP 391.77

Siyuan WU, Hong LI. Developing a general pretrained multimodal battery model with small parameters based on semantic detection[J]. Energy Storage Science and Technology, 2024, 13(4): 1216-1224.

Figures/Tables 5

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等级	指标
1	传统机器学习模型，能预测单一体系的单一目标
2	通用模型构建，能够初步理解电池电化学反应，能够在跨体系预测、跨工况预测和跨尺度预测中一个达到设定的要求(见后文解释)
3	在2的基础上能够在跨体系预测、跨工况预测和跨尺度预测中两个达到设定的要求(见后文解释)
4	在2的基础上同时在跨体系预测、跨工况预测和跨尺度预测中达到设定的要求(见后文解释)
5	通用性大模型，在4的基础上不仅仅在二次电池能够达到设定的要求，同时可以扩展到一次电池、燃料电池等也有较强的泛化能力(见后文解释)