Jennifer Brucker, Rainer Gasper, Wolfgang G. Bessler
{"title":"锂离子电池慢电压动态神经常微分方程灰盒模型:单细胞实验应用","authors":"Jennifer Brucker, Rainer Gasper, Wolfgang G. Bessler","doi":"10.1016/j.jpowsour.2024.234918","DOIUrl":null,"url":null,"abstract":"<div><p>Lithium-ion batteries exhibit a complex, nonlinear and dynamic voltage behaviour. Modelling their slow dynamics is a challenge due to the multiple potential causes involved. We present here a neural equivalent circuit model for lithium-ion batteries including slow voltage dynamics. The model uses an equivalent circuit with voltage source, series resistor, and diffusion element. The series resistance is parameterized using neural networks. The diffusion element is based on a discretized form of Fickian diffusion, parameterized using a neural network and learnable parameters. It is flexible to represent not only Warburg behaviour, but also resistor-capacitor-type dynamics. Mathematically, the resulting model is given by a differential–algebraic equation system combining ordinary and neural differential equations. Therefore, the model combines concepts of both physical theory (white-box model) and artificial intelligence (black-box model) to a combined framework (grey-box model). We apply this approach to a lithium iron phosphate based lithium-ion battery cell. The experimental voltage behaviour during constant-current cycles as well as the dynamics during pulse tests are well reproduced by the model. Only at very high and very low states of charge the simulation significantly deviates from experiments, which might result from insufficient training data in these regions.</p></div>","PeriodicalId":377,"journal":{"name":"Journal of Power Sources","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S037877532400870X/pdfft?md5=993688349be6082d6ded4cf1fa78795a&pid=1-s2.0-S037877532400870X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A grey-box model with neural ordinary differential equations for the slow voltage dynamics of lithium-ion batteries: Application to single-cell experiments\",\"authors\":\"Jennifer Brucker, Rainer Gasper, Wolfgang G. 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Therefore, the model combines concepts of both physical theory (white-box model) and artificial intelligence (black-box model) to a combined framework (grey-box model). We apply this approach to a lithium iron phosphate based lithium-ion battery cell. The experimental voltage behaviour during constant-current cycles as well as the dynamics during pulse tests are well reproduced by the model. 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A grey-box model with neural ordinary differential equations for the slow voltage dynamics of lithium-ion batteries: Application to single-cell experiments
Lithium-ion batteries exhibit a complex, nonlinear and dynamic voltage behaviour. Modelling their slow dynamics is a challenge due to the multiple potential causes involved. We present here a neural equivalent circuit model for lithium-ion batteries including slow voltage dynamics. The model uses an equivalent circuit with voltage source, series resistor, and diffusion element. The series resistance is parameterized using neural networks. The diffusion element is based on a discretized form of Fickian diffusion, parameterized using a neural network and learnable parameters. It is flexible to represent not only Warburg behaviour, but also resistor-capacitor-type dynamics. Mathematically, the resulting model is given by a differential–algebraic equation system combining ordinary and neural differential equations. Therefore, the model combines concepts of both physical theory (white-box model) and artificial intelligence (black-box model) to a combined framework (grey-box model). We apply this approach to a lithium iron phosphate based lithium-ion battery cell. The experimental voltage behaviour during constant-current cycles as well as the dynamics during pulse tests are well reproduced by the model. Only at very high and very low states of charge the simulation significantly deviates from experiments, which might result from insufficient training data in these regions.
期刊介绍:
The Journal of Power Sources is a publication catering to researchers and technologists interested in various aspects of the science, technology, and applications of electrochemical power sources. It covers original research and reviews on primary and secondary batteries, fuel cells, supercapacitors, and photo-electrochemical cells.
Topics considered include the research, development and applications of nanomaterials and novel componentry for these devices. Examples of applications of these electrochemical power sources include:
• Portable electronics
• Electric and Hybrid Electric Vehicles
• Uninterruptible Power Supply (UPS) systems
• Storage of renewable energy
• Satellites and deep space probes
• Boats and ships, drones and aircrafts
• Wearable energy storage systems