Mulpuri Sai Krishna, Sah Bikash, Kumar Praveen
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Assam 781039, India.
Department of Electrical Engineering, Mechanical Engineering and Technical Journalism, Hochschule Bonn-Rhein-Seig, 53757 Sankt Augustin, North Rhine-Westphalia, Germany.
iScience. 2023 Aug 29;26(10):107770. doi: 10.1016/j.isci.2023.107770. eCollection 2023 Oct 20.
Battery lifespan estimation is essential for effective battery management systems, aiding users and manufacturers in strategic planning. However, accurately estimating battery capacity is complex, owing to diverse capacity fading phenomena tied to factors such as temperature, charge-discharge rate, and rest period duration. In this work, we present an innovative approach that integrates real-world driving behaviors into cyclic testing. Unlike conventional methods that lack rest periods and involve fixed charge-discharge rates, our approach involves 1000 unique test cycles tailored to specific objectives and applications, capturing the nuanced effects of temperature, charge-discharge rate, and rest duration on capacity fading. This yields comprehensive insights into cell-level battery degradation, unveiling growth patterns of the solid electrolyte interface (SEI) layer and lithium plating, influenced by cyclic test parameters. The results yield critical empirical relations for evaluating capacity fading under specific testing conditions.
电池寿命估计对于有效的电池管理系统至关重要,有助于用户和制造商进行战略规划。然而,由于与温度、充放电速率和静置时间等因素相关的各种容量衰减现象,准确估计电池容量很复杂。在这项工作中,我们提出了一种创新方法,将实际驾驶行为整合到循环测试中。与缺乏静置时间且涉及固定充放电速率的传统方法不同,我们的方法涉及针对特定目标和应用定制的1000个独特测试循环,捕捉温度、充放电速率和静置时间对容量衰减的细微影响。这为电池在细胞层面的退化提供了全面的见解,揭示了受循环测试参数影响的固体电解质界面(SEI)层和锂镀层的生长模式。结果得出了用于评估特定测试条件下容量衰减的关键经验关系。