Performed 3Delectrochemical-thermal modeling of four battery cooling methods 2)Thermal performance of direct air cooling, direct liquid cooling, indirect (jacket) liquid and fin coolingare compared 3) Merits and limitations of each cooling method for occupying a fixed volume are summarized 4) Temperaturerise for a fixed load is lower with direct or indirect liquid cooling lower than air and fin cooling. Abstract Choosing a proper cooling method for a lithium-ion (Li-ion) battery pack for electric drive vehicles (EDVs) and making an optimal cooling control strategy to keep the temperature at a optimal range of 15°C to 35°C is essential to increasing safety, extending the pack service life, and reducing costs. When choosing a cooling method and developing strategies, trade-offs need to be made among many facets such as costs, complexity, weight, cooling effects, temperature uniformity, and parasitic power. This paper considers four cell-cooling methods: air cooling, direct liquid cooling, indirect liquid cooling, and fin cooling. To evaluate their effectiveness, these methods are assessed using a typical large capacity Li-ion pouch cell designed for EDVs from the perspective of coolant parasitic power consumption, maximum temperature rise, temperature difference in a cell, and additional weight used for the cooling system. We use a state-of-the-art Li-ion battery electro-chemical thermal model. The results show that under our assumption an air-cooling system needs 2 to 3 more energy than other methods to keep the same average temperature; an indirect liquid cooling system has the lowest maximum temperature rise; and a fin cooling system adds about 40% extra weight of cell, which weighs most, when the four kinds cooling methods have the same volume. Indirect liquid cooling is more practical form than direct liquid cooling though it has slightly lower cooling performance.
Complex physics and long computation time hinder the adoption of computer aided engineering models in the design of largeformat battery cells and systems. A modular, efficient battery simulation model-the multiscale multidomain (MSMD) model-was previously introduced to aid the scale-up of Li-ion material & electrode designs to complete cell and pack designs, capturing electrochemical interplay with 3-D electronic current pathways and thermal response. This paper enhances the computational efficiency of the MSMD model using a separation of time-scales principle to decompose model field variables. The decomposition provides a quasi-explicit linkage between the multiple length-scale domains and thus reduces time-consuming nested iteration when solving model equations across multiple domains. In addition to particle-, electrode-and cell-length scales treated in the previous work, the present formulation extends to bus bar-and multi-cell module-length scales. In cutting-edge industries such as automotive and aviation, computer models are valuable tools for reducing the cost of product development, improving manufacturing processes, optimizing designs, and implementing advanced controls. Although the global electricdrive-vehicle market is growing rapidly, the lack of a model that can accurately predict a battery's behavior is recognized as a threat to the automotive industry that has been enhancing its dependence on computer models. In a lithium-ion battery, which is the preeminent candidate powering electric-drive vehicles, physiochemical processes take place in intricate geometries over a wide range of time and length scales. The device response of a battery results from complex nonlinear interplays among material characteristics, design variables, and environmental and operational conditions. The multiscale nonlinear nature of battery physics even more critically affects the device behavior as the size of a battery increases. Without understanding the interplays among the interdisciplinary physicochemical processes occurring across varied scales, it is costly to design long-lasting, highperforming, safe, large batteries.The U.S. Department of Energy's Computer Aided Engineering for Electric Drive Vehicle Battery (CAEBAT) program has supported development of modeling capabilities to help industries accelerate mass-market adoption of electric-drive vehicles and their batteries. In support of the U.S. Department of Energy, National Renewable Energy Laboratory developed the multiscale multidomain (MSMD) model, overcoming challenges in modeling the highly nonlinear multiscale response of battery systems.1,2 The MSMD model introduces separate model domains at particle, electrode, and cell levels, while tightly coupling the physics across the scales. The separation of a model domain and the adoption of local homogeneity assumption are enabled by the intrinsic nature of typical battery systems where substantial time-and length-scale segregation occurs. The MSMD particle-domain models (PDMs) solve collective response of ele...
This work aims to bridge the gap between materials modeling, usually carried out at the sub-continuum scale, and the Multi-Scale-Multi-Domain (MSMD) models. In FY12, we developed component models for the electrodes, interfaces, electrolytes, etc., that incorporate the material properties calculated from micro-scale simulations to establish this connection. In FY13, these component models were integrated into cell-level simulations. As the first set of case studies to demonstrate the utility of these models, venting in-pouch format lithium-ion cells under different abuse scenarios was simulated. The build-up and distribution of pressure within the cell is calculated from the component-level models. Gas evolution at the electrode/electrolyte interface as a function of parameters like surface roughness and electrolyte viscosity are included. The cell-level response is then calculated using these estimates. Some preliminary experimental data collected in collaboration with Sandia National Laboratories and the Johnson Space Center at NASA are compared to the model predictions.
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