Modeling Of Distributed Thermal Process In Lithium-Ion Power Battery Based On Limited Knowledge

Lithium-ion batteries are the core energy storage components for in key areas such as modern electric vehicles.However,large-scale power batteries’ safety performance and cycle life are closely related to their operating temperature.Overheating or severely uneven temperature distribution can cause battery performance degradation or safety incidents.Thus,designing advanced battery thermal management systems with temperature field regulation and local thermal runaway detection is crucial for developing the electric vehicle industry.Developing effective thermal management strategies require access to accurate thermal models of the battery system.The battery systems of electric vehicles often operate in variable environments.Due to the lack of real-time update mechanism,the temperature prediction performance of traditional physics-based models may be severely limited.As a result,advanced in-vehicle thermal management systems place higher demands on the reliability of battery thermal models.However,due to unknown internal states and random external disturbances,often only partially known system information can be used for modeling.Especially in some electric vehicle energy storage systems with dense battery elements,the unknown heat flow leads to an unusually complex heat transfer mechanism,making it more difficult to capture the heat transfer dynamics of the battery.Thus,fusing limited mechanistic model knowledge and sampled data to construct a battery temperature field prediction model with both efficiency and reasonable accuracy is not only a engineering challenge with practical value,but also a scientific one.The key problem addressed in this dissertation is how to develop effective modeling techniques for the thermal processes of lithium-ion power batteries that exhibit spatiotemporal dynamic characteristics,using limited knowledge in the absence of accurate mathematical models.The goal is to achieve precise temperature field predictions.The main research efforts include the following:First,this dissertation proposes a novel efficient optimization framework for multi-scale parameter identification of the battery electrochemical model.Unlike the traditional methods,this framework can significantly reduce the number of solution iterations of full-order physical models by prescreening candidate parameters through data-driven surrogate models,thus addressing the problem of lengthy computation caused by massive candidate parameter evaluations.Battery experiments and ideal-state physical simulations provide a data basis that reflects the dynamic characteristics of the battery system and establish the necessary theoretical foundation for subsequent studies on thermal modeling methods.Given the complex electrochemical reactions inside the battery system,it is difficult to accurately calculate the battery heat generation rate in practice.Thus,this dissertation proposes a novel adaptive reduced-order modeling method to address the problem of unknown heat sources in batteries.Unlike traditional methods,this work uses the spectral method to extract low-order physics-based models that dominate the system dynamics and embeds neural networks for adaptive estimation of unknown heat sources.This approach can accurately predict the internal heat generation rate of the battery in real-time and demonstrates good modeling performance.Additionally,by using analytic orthogonal eigenfunctions to expand the spatiotemporal variable,the developed adaptive model can predict the temperature field of continuous spatial domains with limited sensing.Due to the influence of practical working environments,the uncertainty in the battery thermal process may lead to parameter drift and unknown dynamics.To address this,this dissertation proposes a fusion-driven modeling method based on dynamic error compensation.The method innovatively integrates multi-source information from physical principles and sensor observations,and constructs a data-driven compensation model to reduce nonlinear prediction errors caused by process/model mismatches.The proposed fusion-driven model can accurately predict the battery temperature distribution with strong robustness and generalization performance,providing new ideas and methods for optimal design and real-time control of battery systems.Given the complex heat transfer mechanisms that are difficult to establish clear dynamic equations,this dissertation proposes two novel purely data-driven methods for modeling battery thermal processes.To meet the rapid response requirements of industrial applications,a spatiotemporal feature adaptation-based incremental learning modeling approach is proposed.The method uses only the latest measurement data for model updates,which provides outstanding computational efficiency compared to traditional methods.In addition,to reduce the number of online sensors,a sparse sensing modeling approach based on the reconstruction of temporal dynamics is proposed.The method innovatively transfers the spatial distribution features extracted from the offline data to the online stage,and only partial-node information is required for prediction of the full-node temperature field.These two data-driven methods provide novel solutions for modeling complex battery thermal processes,with significant theoretical and practical value.