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

Electric Vehicle (EV) has become a preferable choice in the modern
transportation system due to its environmental and energy sustainability.
However, in many large cities, EV drivers often fail to find the proper spots
for charging, because of the limited charging infrastructures and the
spatiotemporally unbalanced charging demands. Indeed, the recent emergence of
deep reinforcement learning provides great potential to improve the charging
experience from various aspects over a long-term horizon. In this paper, we
propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning
(Master), for intelligently recommending public accessible charging stations by
jointly considering various long-term spatiotemporal factors. Specifically, by
regarding each charging station as an individual agent, we formulate this
problem as a multi-objective multi-agent reinforcement learning task. We first
develop a multi-agent actor-critic framework with the centralized attentive
critic to coordinate the recommendation between geo-distributed agents.
Moreover, to quantify the influence of future potential charging competition,
we introduce a delayed access strategy to exploit the knowledge of future
charging competition during training. After that, to effectively optimize
multiple learning objectives, we extend the centralized attentive critic to
multi-critics and develop a dynamic gradient re-weighting strategy to
adaptively guide the optimization direction. Finally, extensive experiments on
two real-world datasets demonstrate that Master achieves the best comprehensive
performance compared with nine baseline approaches.

本文提出了一个名为 Master 的框架，它采用深度强化学习方法，以智能化的方式推荐公共可用的电动汽车充电站，并联合考虑各种长期时空因素，包括充电基础设施、未来潜在的充电竞争等等。经过广泛的实验评估，Master 相对于九个基准方法具有最佳的综合性能。

基于多智能体强化学习的智能电动汽车充电建议

Intelligent Electric Vehicle Charging Recommendation Based on  Multi-Agent Reinforcement Learning

With the increase in adoption of Electric Vehicles (EVs), proper utilization
of the charging infrastructure is an emerging challenge for service providers.
Overstaying of an EV after a charging event is a key contributor to low
utilization. Since overstaying is easily detectable by monitoring the power
drawn from the charger, managing this problem primarily involves designing an
appropriate "penalty" during the overstaying period. Higher penalties do
discourage overstaying; however, due to uncertainty in parking duration, less
people would find such penalties acceptable, leading to decreased utilization
(and revenue). To analyze this central trade-off, we develop a novel framework
that integrates models for realistic user behavior into queueing dynamics to
locate the optimal penalty from the points of view of utilization and revenue,
for different values of the external charging demand. Next, when the model
parameters are unknown, we show how an online learning algorithm, such as UCB,
can be adapted to learn the optimal penalty. Our experimental validation, based
on charging data from London, shows that an appropriate penalty can increase
both utilization and revenue while significantly reducing overstaying.

本文提出了一种电动汽车充电的过度停留问题的解决方案，通过设计恰当的 “罚款” 惩罚过度停留者，采用不同的值以满足外部充电需求，从利用率和收入的角度找到最优罚款，实现了通过使用在线学习算法来学习最优罚款的方法，但需要进一步实验验证。