Collaborative Research: Model Reformulation for Lithium-ion Batteries -- Parameter Estimation and Dynamic Optimization

CBET-0828002SubramanianPI将在锂离子电池的建模、参数估计和动态优化方面开展广泛的活动。虽然基于物理的模型已经被广泛地开发和研究，但严格的模型还没有被用于参数估计或运行条件的动态优化。这是一个尚未探索的领域，需要重新建立模型和进行近似，以便有效地模拟耦合偏微分方程组。沿着这些思路，深入分析了模型重构，以便于(1)用于了解锂离子电池容量衰减的参数估计和(2)动态优化技术，以优化未来电源的可用性和效率。具体的研究目标包括：*重构基于物理的高效锂离子电池模型：通过对严格的数值模型进行仔细的分析和分析/近似方法，借助于各种先进的数学方法，包括带状矩阵方程的解析解，耦合方程的解耦，*通过跟踪运输和动力学参数随循环次数的变化来预测锂离子电池的容量衰减。*最佳操作条件：开发、验证、并通过对重新制定的高效模型进行动态优化，实现理想的操作条件，以最大限度地减少锂离子电池的利用率损失，最大限度地提高能源效率(从而降低比重)。智能优点*重新制定的基于物理的锂离子电池模型的CPU时间将比当前最先进的模型低两个数量级。*重新制定的模型更适合进行参数估计和动态优化，而当前最先进的运输现象模型在计算上效率低下。*这项工作将通过跟踪参数随循环的变化来帮助预测和了解锂离子电池的容量衰减。这将有助于为未来设计更好的电池。拟议的工作将优化电池的运行条件，以获得高能量和利用效率。广泛的影响*模型重构技术将适用于广泛的工程问题，如整体反应堆、燃料电池、生物反应器等。培训和开发广泛的电池/燃料电池专业人员，从工程师和研究人员到教育工作者。对保持国家在该领域的卓越地位至关重要。*增加代表不足的群体对工程研究的参与。通过同行评议的研究文章、演讲和研究模块网站传播结果将对世界各地的学生和研究社区产生影响。*开发用户友好的模块，以帮助实验研究人员对电化学电源进行建模。*在现有的研究生课程中增加一项新的内容，培训学生重新制定电化学电源的模型。

CBET-0828002SubramanianThe PIs will pursue a wide range of activities in modeling, parameter estimation and dynamic optimization of Lithium-ion batteries. While physics-based models have been widely developed and studied for these systems, the rigorous models have not been employed for parameter estimation or dynamic optimization of operating conditions. This is an unexplored area requiring model reformulation and approximations for efficient simulation of coupled partial differential equations. Along these lines, this is an in-depth analysis of model reformulation to facilitate (1) parameter estimation for understanding capacity fade of Lithium-ion batteries and (2) dynamic optimization technique to optimize the usability and efficiency of future power sources.Research ObjectivesSpecific research objectives include:* Reformulated efficient physics-based models for Lithium-ion batteries: reformulate and develop efficient models by careful analysis and analytical/approximate methods for rigorous numerical models with the aid of various advanced mathematical methods including analytical solution of banded matrix equations, decoupling coupled equations, etc.* Prediction of capacity fade in Lithium-ion batteries by keeping track of the change of transport and kinetic parameters with cycle numbers.* Optimum operating conditions: develop, validate, and implement ideal operating conditions to minimize utilization loss and maximize energy efficiency (and hence reduce the specific weight) of Lithium-ion batteries by performing dynamic optimization on reformulated-efficient models.Intellectual Merit* The reformulated physics-based models for Lithium-ion batteries will have CPU times two orders-of-magnitude lower than the current state-of-the-art.* The reformulated models are more amenable for parameter estimation and dynamicoptimization while the current state-of-the-art transport phenomena models are computationally inefficient.* The work will help predict and understand capacity fade in Lithium-ion batteries by tracking changes in the parameters with cycles. This will help design better batteries for the future. The proposed work will optimize the operating conditions of batteries for high energy and utilization efficiency.Broader Impacts* The model reformulation technique will be applicable for a wide range of engineering problems like monolith reactors, fuel cells, bioreactors, etc. Training and development of a wide range of battery/fuel cell professionals, ranging from engineers and researchers to educators. Crucial to maintaining national preeminence in the field.* Increased participation of under-represented groups in engineering research. Dissemination of results through peer-reviewed research articles, presentations and website for research modules will have an impact on student and research community worldwide.* Develop user-friendly modules to aid experimental researchers in modeling electrochemical power sources.* A new addition to the existing graduate course that trains the students in model reformulation of electrochemical power sources.