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

锂离子（Li-ion）电池是在电子、电动汽车和电网存储等不同应用中存储电力和按需输送电力的重要组件。这些存储系统的可靠性很大程度上取决于电池的安全性和稳健性。它依赖于高效电池管理系统（bms）的使用，该系统通常用于监测、控制和管理电池的热性能，以及检查电池的安全性和完整性。bms基于对电池内部状态条件（如充电状态（SOC）和健康状态（SOH））的评估来执行这些活动，这些状态条件是不可测量的，而是根据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，几乎所有商用电池状态估计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，但随着电池的老化，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变化迅速。此外，这些模型无法估计电池的最高温度，而最高温度是电池安全的一个重要因素。

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