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Mr. Takumi Yamanaka
Kobelco Research Institute, Inc.

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Research Keywords & Expertise

0 Li-ion battery
0 Fluid Mechanics
0 Li-ion battery safety
0 Thermal engineering
0 Machine and Deep Learning

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Journal article
Published: 01 May 2021 in Journal of The Electrochemical Society
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ACS Style

Takumi Yamanaka; Yoichi Takagishi; Tatsuya Yamaue. An Electrochemical-Thermal Model for Lithium-Ion Battery Packs during Driving of Battery Electric Vehicles. Journal of The Electrochemical Society 2021, 168, 050545 .

AMA Style

Takumi Yamanaka, Yoichi Takagishi, Tatsuya Yamaue. An Electrochemical-Thermal Model for Lithium-Ion Battery Packs during Driving of Battery Electric Vehicles. Journal of The Electrochemical Society. 2021; 168 (5):050545.

Chicago/Turabian Style

Takumi Yamanaka; Yoichi Takagishi; Tatsuya Yamaue. 2021. "An Electrochemical-Thermal Model for Lithium-Ion Battery Packs during Driving of Battery Electric Vehicles." Journal of The Electrochemical Society 168, no. 5: 050545.

Journal article
Published: 29 August 2020 in Batteries
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Lithium (Li)-ion battery thermal management systems play an important role in electric vehicles because the performance and lifespan of the batteries are affected by the battery temperature. This study proposes a framework to establish equivalent circuit models (ECMs) that can reproduce the multi-physics phenomenon of Li-ion battery packs, which includes liquid cooling systems with a unified method. We also demonstrate its utility by establishing an ECM of the thermal management systems of the actual battery packs. Experiments simulating the liquid cooling of a battery pack are performed, and a three-dimensional (3D) model is established. The 3D model reproduces the heat generated by the battery and the heat transfer to the coolant. The results of the 3D model agree well with the experimental data. Further, the relationship between the flow rate and pressure drop or between the flow rate and heat transfer coefficients is predicted with the 3D model, and the data are used for the ECM, which is established using MATLAB Simulink. This investigation confirmed that the ECM’s accuracy is as high as the 3D model even though its computational costs are 96% lower than the 3D model.

ACS Style

Takumi Yamanaka; Daiki Kihara; Yoichi Takagishi; Tatsuya Yamaue. Multi-Physics Equivalent Circuit Models for a Cooling System of a Lithium Ion Battery Pack. Batteries 2020, 6, 44 .

AMA Style

Takumi Yamanaka, Daiki Kihara, Yoichi Takagishi, Tatsuya Yamaue. Multi-Physics Equivalent Circuit Models for a Cooling System of a Lithium Ion Battery Pack. Batteries. 2020; 6 (3):44.

Chicago/Turabian Style

Takumi Yamanaka; Daiki Kihara; Yoichi Takagishi; Tatsuya Yamaue. 2020. "Multi-Physics Equivalent Circuit Models for a Cooling System of a Lithium Ion Battery Pack." Batteries 6, no. 3: 44.

Journal article
Published: 28 May 2020 in Journal of The Electrochemical Society
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Numerical physics-based models for Li-ion batteries under abuse conditions are useful in understanding failure mechanisms and deciding safety designs. Since battery design is generally required to decrease the failure risks while increasing the performance, multi-objective optimization methods are useful. Nevertheless, these usually require huge computational costs because these models targeting abuse battery conditions generally have many input physical parameters and computational costs for calculating one result are high. Therefore, we develop a framework for performing multi-objective optimization at a reasonable computational cost using machine learning methods. With this framework, an inverse analysis of optimal Li-ion battery design conditions, including safety conditions, is performed. Nail penetration simulations on different input conditions are performed so as to build a database for battery design conditions/test conditions (descriptors) and safety/performance (predictors). As a result of analyzing the relationship between descriptors and predictors, a high correlation between fire spread and negative electrode active material diameter is confirmed. Furthermore, a regression model to predict the database is created with a Gaussian process model. Using the model and a genetic algorithm, optimal design conditions are searched, and the design conditions that offer higher safety and better performance are identified under the assumed conditions.

ACS Style

Takumi Yamanaka; Yoichi Takagishi; Tatsuya Yamaue. A Framework for Optimal Safety Li-ion Batteries Design using Physics-Based Models and Machine Learning Approaches. Journal of The Electrochemical Society 2020, 1 .

AMA Style

Takumi Yamanaka, Yoichi Takagishi, Tatsuya Yamaue. A Framework for Optimal Safety Li-ion Batteries Design using Physics-Based Models and Machine Learning Approaches. Journal of The Electrochemical Society. 2020; ():1.

Chicago/Turabian Style

Takumi Yamanaka; Yoichi Takagishi; Tatsuya Yamaue. 2020. "A Framework for Optimal Safety Li-ion Batteries Design using Physics-Based Models and Machine Learning Approaches." Journal of The Electrochemical Society , no. : 1.

Journal article
Published: 01 August 2019 in Batteries
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We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

ACS Style

Yoichi Takagishi; Takumi Yamanaka; Tatsuya Yamaue. Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes. Batteries 2019, 5, 54 .

AMA Style

Yoichi Takagishi, Takumi Yamanaka, Tatsuya Yamaue. Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes. Batteries. 2019; 5 (3):54.

Chicago/Turabian Style

Yoichi Takagishi; Takumi Yamanaka; Tatsuya Yamaue. 2019. "Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes." Batteries 5, no. 3: 54.

Journal article
Published: 06 February 2019 in Journal of Power Sources
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A numerical model denoted the “Tri-bred model” is developed to accurately reproduce Li-ion battery nail penetration tests. The model considers the movement of the nail as well as the thermal decomposition reaction. Moreover, the “combustion volume” was defined to quantitatively evaluate the degree of combustion risk in a unique manner. The validity of the model was verified by comparison with experimental results. The model suitably described the experimental phenomena; the behavior of the “combustion volume” indicated a similar tendency to the gas emission degree during the experiment. To investigate the relationship between the experimental conditions and the degree of risk, a parametric study was performed and the results of “combustion degree” were compared. As a result, it was found that the nail speed was more strongly correlated with combustion risk than penetration position.

ACS Style

Takumi Yamanaka; Yoichi Takagishi; Yasufumi Tozuka; Tatsuya Yamaue. Modeling lithium ion battery nail penetration tests and quantitative evaluation of the degree of combustion risk. Journal of Power Sources 2019, 416, 132 -140.

AMA Style

Takumi Yamanaka, Yoichi Takagishi, Yasufumi Tozuka, Tatsuya Yamaue. Modeling lithium ion battery nail penetration tests and quantitative evaluation of the degree of combustion risk. Journal of Power Sources. 2019; 416 ():132-140.

Chicago/Turabian Style

Takumi Yamanaka; Yoichi Takagishi; Yasufumi Tozuka; Tatsuya Yamaue. 2019. "Modeling lithium ion battery nail penetration tests and quantitative evaluation of the degree of combustion risk." Journal of Power Sources 416, no. : 132-140.