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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715466
• 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

在电子产品、电动汽车和电网存储等不同应用中，锂离子(Li-ion)电池是存储电能和按需提供电力的基本组件。这些存储系统的可靠性在很大程度上依赖于电池的安全性和坚固性。它依赖于使用高效的电池管理系统(BMS)，这些系统通常用于监测、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的评估来执行这些活动，如充电状态(SOC)和健康状态(SOH)，这些状态是不可测量的，但基于电池充电或放电时的可测量信号(如电压、电流和温度)进行估计。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(KF)。基于经验的模型速度很快，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，代表其行为的物理参数迅速变化。此外，这种模型无法估计电池的最高温度，这是电池安全的一个重要因素。 拟议的研究方案旨在为能源储存系统的可持续管理开发诊断和控制工具。短期目标是开发一种基于更具代表性的物理模型的改进的锂离子电池状态估计器。由于控制方程的非线性，电池内部状态，如SOC或SOH，将通过与扩展卡尔曼滤波(EKF)耦合的简化模型来估计。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-设计、建造和使用电池测试实验台，3-模型优化和估计其热物理参数，4-电池验后和电极表征，5-建立考虑退化机制影响的老化方程，6-将新模型与基于EKF的估计器耦合以评估电池的SOC和SOH。 5年后，7名HQP将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新技术的发展。为改善锂离子电池的能量管理而开发的新工具也可以用于促进加拿大清洁能源行业的经济发展，从电动汽车发动机到生产和分配可再生能源所需的电力储存。