To minimize battery aging of electric vehicles (EVs), it is paramount to manage efficiently their energy consumption. An energy management strategy (EMS) has recently been developed, where batteries and supercapacitors (SCs) are coordinated as a function of the driving mode which is determined manually by the driver. In the present article, we improve the EMS by developing a driving mode predictor (DMP) that determines automatically in real-time the driving mode simply from the speed history of the vehicle. The DMP is designed using supervised learning (SL), a branch of machine learning (ML). A strength of our approach is that it is applicable to predict the driving modes of any EV. To predict the driving modes during a trip made by a given EV, the DMP needs to follow the speed evolution of the EV and know its maximum reachable speed. The integration of the DMP into the EMS results in an enhanced EMS called DMP-based EMS that determines automatically in real-time the power to take from each source of energy of the EV as a function of its speed history. The results obtained from real driving cycles confirm the prediction quality of the DMP and the energy efficiency of the proposed DMP-based EMS.

中文翻译：

---

基于驾驶模式预测器的双源电动汽车实时能量管理

为了最大限度地减少电动汽车 (EV) 的电池老化，有效管理其能源消耗至关重要。最近开发了一种能源管理策略 (EMS)，其中电池和超级电容器 (SC) 作为由驾驶员手动确定的驾驶模式的函数进行协调。在本文中，我们通过开发驾驶模式预测器 (DMP) 来改进 EMS，该预测器仅根据车辆的速度历史记录自动实时确定驾驶模式。DMP 是使用机器学习 (ML) 的一个分支——监督学习 (SL) 设计的。我们方法的优势在于它适用于预测任何电动汽车的驾驶模式。为了预测给定 EV 旅行期间的驾驶模式，DMP 需要跟踪 EV 的速度演变并知道其最大可达速度。将 DMP 集成到 EMS 中会产生称为基于 DMP 的 EMS 的增强型 EMS，它根据其速度历史自动实时确定从 EV 的每个能源获取的功率。从实际驾驶循环中获得的结果证实了 DMP 的预测质量和所提出的基于 DMP 的 EMS 的能源效率。