Qi Liu, Zeqing Duan, Qiongqiong Qi, Xiaolu Yang, Qingshui Xie, Jie Lin
Calender process is important to improve the mechanical and electrochemical properties of cathode materials. To explore pressure effect on structure and resistance of electrode powder, the morphology and surface area of lithium cobalt oxide (LCO) powder under different pressure are investigated. Meanwhile, the real-time stress, density, and conductivity of LCO powder upon compaction are tested by a self-made detection system. Moreover, the battery performance of LCO powder after compaction is compared in coin cells. This work elucidates the relationship between compaction density, powder resistance, and electrochemical performance of cathode materials for lithium-ion batteries.
{"title":"Pressure Effect on Mechanical and Electrochemical Properties of Lithium Cobalt Oxide Powder Materials","authors":"Qi Liu, Zeqing Duan, Qiongqiong Qi, Xiaolu Yang, Qingshui Xie, Jie Lin","doi":"10.1002/batt.202400361","DOIUrl":"10.1002/batt.202400361","url":null,"abstract":"<p>Calender process is important to improve the mechanical and electrochemical properties of cathode materials. To explore pressure effect on structure and resistance of electrode powder, the morphology and surface area of lithium cobalt oxide (LCO) powder under different pressure are investigated. Meanwhile, the real-time stress, density, and conductivity of LCO powder upon compaction are tested by a self-made detection system. Moreover, the battery performance of LCO powder after compaction is compared in coin cells. This work elucidates the relationship between compaction density, powder resistance, and electrochemical performance of cathode materials for lithium-ion batteries.</p>","PeriodicalId":132,"journal":{"name":"Batteries & Supercaps","volume":"7 10","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jochen Stadler, Dr. Johannes Fath, Dr. Madeleine Ecker, Prof. Arnulf Latz
This work compares a state of the art data-driven model to predict the state of health (SoH) in lithium ion batteries with a new prediction model based on the mechanistic framework. The mechanistic approach attributes the degradation to individual components such as loss of available capacity on each electrode as well as loss of cyclable lithium. By combining the mechanistic framework with data-driven models for the component losses based on a design of experiment, we achieve a cycle aging model that can predict capacity degradation as well as degradation-induced changes to the discharge potential curve. Using this cycle aging model alongside with a semi-empirical calendar aging model, we present a holistic aging model that we validate on independent validation tests containing time-variant load profiles. While the purely data-driven model is better at predicting the SoH, the mechanistic model clearly has it advantages in a deeper understanding that can potentially enhance the current methods of tracking and updating the characteristic open-circuit voltage curve over lifetime.
{"title":"Combining a Data Driven and Mechanistic Model to Predict Capacity and Potential Curve-Degradation","authors":"Jochen Stadler, Dr. Johannes Fath, Dr. Madeleine Ecker, Prof. Arnulf Latz","doi":"10.1002/batt.202400211","DOIUrl":"10.1002/batt.202400211","url":null,"abstract":"<p>This work compares a state of the art data-driven model to predict the state of health (SoH) in lithium ion batteries with a new prediction model based on the mechanistic framework. The mechanistic approach attributes the degradation to individual components such as loss of available capacity on each electrode as well as loss of cyclable lithium. By combining the mechanistic framework with data-driven models for the component losses based on a design of experiment, we achieve a cycle aging model that can predict capacity degradation as well as degradation-induced changes to the discharge potential curve. Using this cycle aging model alongside with a semi-empirical calendar aging model, we present a holistic aging model that we validate on independent validation tests containing time-variant load profiles. While the purely data-driven model is better at predicting the SoH, the mechanistic model clearly has it advantages in a deeper understanding that can potentially enhance the current methods of tracking and updating the characteristic open-circuit voltage curve over lifetime.</p>","PeriodicalId":132,"journal":{"name":"Batteries & Supercaps","volume":"7 10","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lucie Denisart, Dr. Javier F. Troncoso, Prof. Dr. Emilie Loup-Escande, Prof. Dr. Alejandro A. Franco
The Cover Feature displays an operator using our mixed-reality holographic notebook to report the manufacturing parameters he is intending to use in a battery pilot line. Our technology paves the way to breaking the barrier between the digital and the real worlds, for maximum efficiency of the operator‘s work. More information can be found in the Concept by A. A. Franco and co-workers (DOI: 10.1002/batt.202400042).