Energy systems, climate change, and public health are among the primary
reasons for moving toward electrification in transportation. Transportation
electrification is being promoted worldwide to reduce emissions. As a result,
many automakers will soon start making only battery electric vehicles (BEVs).
BEV adoption rates are rising in California, mainly due to climate change and
air pollution concerns. While great for climate and pollution goals, improperly
managed BEV charging can lead to insufficient charging infrastructure and power
outages. This study develops a novel Micro Clustering Deep Neural Network
(MCDNN), an artificial neural network algorithm that is highly effective at
learning BEVs trip and charging data to forecast BEV charging events,
information that is essential for electricity load aggregators and utility
managers to provide charging stations and electricity capacity effectively. The
MCDNN is configured using a robust dataset of trips and charges that occurred
in California between 2015 and 2020 from 132 BEVs, spanning 5 BEV models for a
total of 1570167 vehicle miles traveled. The numerical findings revealed that
the proposed MCDNN is more effective than benchmark approaches in this field,
such as support vector machine, k nearest neighbors, decision tree, and other
neural network-based models in predicting the charging events.

本研究开发了一种新的微聚类深度神经网络（MCDNN）算法，通过学习电动汽车行程和充电数据来预测充电事件，为电力负荷聚合器和电力管理者提供充电站和电力容量的有效分配。数值发现表明，所提出的 MCDNN 在预测充电事件方面比支持向量机、k 最近邻、决策树和其他基于神经网络的模型更有效。

电池电动汽车充电行为的预测：一种采用微聚类和 SMOTE 技术的深度学习方法

Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning  Approach Equipped with Micro-Clustering and SMOTE Techniques

In recent years, significant progress has been made in transportation
electrification. And lithium-ion batteries (LIB), as the main energy storage
devices, have received widespread attention. Accurately predicting the state of
health (SOH) can not only ease the anxiety of users about the battery life but
also provide important information for the management of the battery. This
paper presents a prediction method for SOH based on Vision Transformer (ViT)
model. First, discrete charging data of a predefined voltage range is used as
an input data matrix. Then, the cycle features of the battery are captured by
the ViT which can obtain the global features, and the SOH is obtained by
combining the cycle features with the full connection (FC) layer. At the same
time, transfer learning (TL) is introduced, and the prediction model based on
source task battery training is further fine-tuned according to the early cycle
data of the target task battery to provide an accurate prediction. Experiments
show that our method can obtain better feature expression compared with
existing deep learning methods so that better prediction effect and transfer
effect can be achieved.

本研究提出了一种基于 Vision Transformer 模型的电池健康预测方法，并采用迁移学习的方法进行优化，实验结果表明该方法能够取得更好的预测效果。