ABSTRACT

The transition to non-fossil fuels brings with its basic challenges in battery technologies. Due to their efficiency, one of the areas where Li-ion batteries are widely used is electric vehicles (EVs). Range estimation is one of the most important needs in a battery-powered electric vehicle (BEV). The range of BEVs directly depends on battery capacity and powertrain efficiency. Although the electrical performance of Li-ion batteries has significantly improved, it is still not possible to overcome their capacity degradation with aging. State of charge (SoC) and state of health (SoH) are two important measures for a battery. With accurate SoC and SoH estimates, a battery management system can prevent each cell in the battery pack from over-charging or over-discharging, and prolongs the life of the entire pack. The novel idea in this study is to estimate SoH with the data collected during the battery charging process. The most needed moment for SoH is the end of the charging process. With this information, the user can plan the job that the battery will be used with. In order to meet this need, a specially designed deep neural network (stacked LSTM) is trained and tested using measurements only from constant current charging phase of quick charge process. The test results show that this method is effectively applicable to quick chargers.

摘要

向非化石燃料的过渡带来了电池技术的基本挑战。由于其效率，锂离子电池被广泛使用的领域之一是电动汽车（EV）。距离估计是电池供电的电动汽车（BEV）的最重要需求之一。BEV的范围直接取决于电池容量和动力总成效率。尽管锂离子电池的电气性能已得到显着改善，但仍无法克服其容量随老化而降低的问题。充电状态（SoC）和健康状态（SoH）是电池的两个重要衡量指标。利用准确的SoC和SoH估算，电池管理系统可以防止电池组中的每个电池过度充电或过度放电，并延长了整个电池组的寿命。这项研究中的新颖想法是利用电池充电过程中收集的数据来估算SoH。SoH最需要的时刻是充电过程的结束。利用此信息，用户可以计划将要使用电池的作业。为了满足此需求，仅使用快速充电过程中恒定电流充电阶段的测量值对经过特殊设计的深度神经网络（堆叠的LSTM）进行训练和测试。测试结果表明，该方法有效地适用于快速充电器。专门设计的深度神经网络（堆叠的LSTM）仅使用快速充电过程中恒定电流充电阶段的测量值进行训练和测试。测试结果表明，该方法有效地适用于快速充电器。专门设计的深度神经网络（堆叠的LSTM）仅使用快速充电过程中恒定电流充电阶段的测量值进行训练和测试。测试结果表明，该方法有效地适用于快速充电器。