Electrified vehicles users may expect their vehicle to have a steady autonomy range and available power throughout the lifetime of their cars. The health assessment of Lithium-ion batteries (LIBs), in that regard, represents a critical point for performance evaluation and lifetime prediction. Reliable state-of-health (SoH) assessment is essential to ensure cautious and suitable use of LIBs. To that end, several embedded solutions are proposed in the literature. In this paper, two new aging indicators are developed to enrich the existing diagnosis-based (DB-SoH) solutions. These indicators are based on collected data during charging (CDB-SoH) and driving (DDB-SoH) events overtime. The data are comprised of variables such as distance, speed, temperature, charging power, and more. Both solutions produce reliable state-of-health $SoH$ assessment with a significantly good estimation error. Additionally, a data-driven battery aging prediction using the random forest (RF) algorithm is introduced using actual users’ behavior and ambient conditions. The proposed solution produced an $SoH$ estimation error of 1.27%. Finally, a method for aging factors ranking is proposed. The obtained order is consistent with known aging root causes in the literature and can be used to mitigate fast LIB aging for electrified vehicle applications.

电动车辆的用户可能希望他们的车辆在其汽车的整个使用寿命期间具有稳定的自主范围和可用功率。在这方面，锂离子电池（LIB）的健康评估是性能评估和寿命预测的关键点。可靠的健康状况（SoH）评估对于确保谨慎和适当地使用LIB至关重要。为此，文献中提出了几种嵌入式解决方案。本文中，开发了两个新的老化指标，以丰富现有的基于诊断的（DB-SoH）解决方案。这些指标基于充电（CDB-SoH）和驾驶（DDB-SoH）事件超时期间收集的数据。数据由变量组成，例如距离，速度，温度，充电功率等。两种解决方案均可产生可靠的健康状态$\mathrm{小号}ØH$评估具有明显好的估计误差。此外，还使用实际用户的行为和环境条件介绍了使用随机森林（RF）算法的数据驱动的电池老化预测。拟议的解决方案产生了$\mathrm{小号}ØH$估计误差为1.27％。最后，提出了一种老化因子排序的方法。所获得的顺序与文献中已知的老化根本原因一致，可用于减轻电动汽车应用中的LIB快速老化。