Real-time Lithium-ion (Li-ion) battery state estimation based on electro-thermal model

基于电热模型的实时锂离子电池状态估计

• 批准号: RGPIN-2019-05329
• 负责人: Désilets, Martin
• 金额: $ 2.84万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687424
• 关键词: Real; time; Lithium; ion; Li

Lithium-ion (Li-ion) batteries are essential components for storing electricity and delivering electric power on demand in different applications, such as electronics, electric vehicles, and power grid storage. The reliability of these storage systems is strongly dependent on the safety and robustness of the batteries. It rests on the use of efficient battery management systems (BMSs) that are usually employed to monitor, control, and manage the thermal performance of the batteries, as well as check their safety and integrity. The BMSs perform these activities based on the assessment of some battery internal state conditions, like State-Of-Charge (SOC) and State-Of-Health (SOH), that are not measurable but estimated based on measurable signals (like voltage, current, and temperature) as the battery is charged or discharged. Nowadays, almost all commercial battery state estimators rely on a battery empirical model and Kalman Filter (KF). The empirical-based models are fast, but they are not accurate enough as the battery ages, especially in harsh ambient conditions wherein the physical parameters representing its behavior change rapidly. Furthermore, such models cannot estimate the maximum battery temperature, which is an important factor for battery safety.******The proposed research program aims at developing diagnostic and control tools for the sustainable management of energy storage systems. The short-term objective is to develop an improved Li-ion battery state estimator based on a more representative physic-based model. The internal battery states like SOC or SOH will be estimated by using a reduced model coupled to an extended Kalman Filter (EKF), due to the non-linearity of the governing equations. In this program, the battery electrical signals and the surface temperature are measured to update the battery internal states in the EKF and for estimating internal temperature distribution.******The methodology relies on a combination of mathematical modeling, battery testing, and material characterisation. The work is divided into 6 steps: 1- development of a preliminary physic-based model, 2- design, construction and use of a battery testing experimental bench, 3- model optimisation and estimation of its thermo-physical parameters, 4- battery post-mortems and electrode characterisation, 5- development of aging equations to consider the impact of degradation mechanisms, 6- coupling the new model with EKF-based estimators to assess battery's SOC and SOH.******After 5 years, 7 HQPs will have been trained in a domain where Canada has important needs. It will bring the development of new knowledge and technologies for the blossoming Canadian energy storage industry. The new tools developed to improve the energy management of Li-ion batteries can also be applied to boost the economic development of clean energy industries in Canada, ranging from electric automotive engines to electricity storage needed in the production and distribution of renewable energies

锂离子（Li-ion）电池是在不同应用中存储电力和按需输送电力的重要组件，例如电子产品、电动汽车和电网存储。这些存储系统的可靠性在很大程度上取决于电池的安全性和耐用性。它依赖于使用高效的电池管理系统（BMS），通常用于监测，控制和管理电池的热性能，以及检查其安全性和完整性。BMS基于对一些电池内部状态条件（如充电状态（SOC）和健康状态（SOH））的评估来执行这些活动，所述电池内部状态条件不可测量，而是基于电池充电或放电时的可测量信号（如电压、电流和温度）来估计。目前，几乎所有的商用电池状态估计器都依赖于电池经验模型和卡尔曼滤波器（KF）。基于电池的模型速度很快，但随着电池老化，它们不够准确，特别是在恶劣的环境条件下，其中代表其行为的物理参数迅速变化。此外，这些模型无法估计最高电池温度，这是电池安全的重要因素。拟议的研究计划旨在开发用于能源存储系统可持续管理的诊断和控制工具。短期目标是基于更有代表性的物理模型开发改进的锂离子电池状态估计器。由于控制方程的非线性，SOC或SOH等内部电池状态将通过使用耦合到扩展卡尔曼滤波器（EKF）的简化模型来估计。在该程序中，测量电池电信号和表面温度，以更新EKF中的电池内部状态，并估计内部温度分布。**该方法依赖于数学建模、电池测试和材料表征的组合。这项工作分为6个步骤：1-开发基于物理的初步模型，2-电池测试实验台的设计、构造和使用，3-模型优化和其热物理参数的估计，4-电池事后分析和电极表征，5-开发老化方程以考虑退化机制的影响，6-将新模型与基于EKF的估计器相结合，以评估电池的SOC和SOH。** 5年后，将有7名高级合格人员在加拿大有重要需求的领域接受培训。它将为蓬勃发展的加拿大储能行业带来新知识和技术的发展。为改善锂离子电池能源管理而开发的新工具也可用于促进加拿大清洁能源产业的经济发展，从电动汽车发动机到生产和分配可再生能源所需的电力存储。