基于深度学习的纯电动汽车经济驾驶系统

Deep Learning–based Eco-driving System for Battery Electric Vehicles

基于深度学习的纯电动汽车经济驾驶系统

A National Center for Sustainable Transportation Research Report
国家可持续交通中心研究报告，加州大学

EXECUTIVE SUMMARY

The uninterrupted growth in transportation activities, for both people and goods movement, has been exerting significant pressure on our socio-economics and environment. However, emerging technologies such as connected and automated vehicles (CAVs), transportation electrification, and edge computing have been stimulating increased efforts by engineers, researchers, and policymakers to tackle transportation-related problems, including those focused on energy and the environment. The eco-driving strategies based on CAV technology particularly have attracted significant interest from all over the world due to its potential to save energy and reduce tail-pipe emissions. Among all CAV based eco-driving strategies, the Eco-Approach and Departure (EAD) application at Signalized Intersections has shown the most significant promise. In this system, an equipped vehicle can take advantage of the signal phase and timing (SPaT) and geometric intersection description (GID) information from the upcoming signalized intersection and calculate the optimal speed to pass on a green light or to decelerate to a stop in the most eco-friendly manner. Speed recommendations may be provided to the driver using a driver-vehicle-interface (DVI) or to the vehicle systems that support automated longitudinal control capabilities.
人员和货物运输的运输活动的不间断增长给我们的社会经济和环境带来了巨大的压力。 然而，联网和自动驾驶汽车 (CAV)、交通电气化和边缘计算等新兴技术一直在刺激工程师、研究人员和政策制定者加大力度解决交通相关问题，包括那些关注能源和环境的问题。 基于 CAV 技术的环保驾驶策略因其节省能源和减少尾气排放的潜力而引起了世界各地的极大兴趣。 在所有基于 CAV 的环保驾驶策略中，信号交叉口的环保进场和离场 (EAD) 应用显示出最显着的前景。 在该系统中，配备的车辆可以利用即将到来的信号交叉口的信号相位和定时（SPaT）以及几何交叉口描述（GID）信息，并计算通过绿灯或减速停车的最佳速度。 最环保的方式。 可以使用驾驶员车辆接口（DVI）向驾驶员或支持自动纵向控制功能的车辆系统提供速度建议。
In this project, the research team conducted a thorough literature review of EAD algorithms, and identified a major research gaps in the following areas: (1) the balance between system optimality and computational efficiency; (2) designated algorithms for electric vehicles (e.g., consideration of regenerative braking); and (3) taking into account downstream traffic information (e.g., prediction of preceding vehicle’s state). To address these gaps, the research team proposed a deep learning–based trajectory-planning algorithm (DLTPA) for EAD application, which can be considered as an approximation of a global optimal algorithm (called a graph-based trajectory planning algorithm or GTPA) that the research team previously developed. The proposed DLTPA has two processes: offline (training) and online (implementation), and it is composed of two major modules: 1) a solution feasibility checker that identifies whether there is a feasible trajectory subject to all the system constraints, e.g., maximum acceleration or deceleration; and 2) a regressor to predict the speed of the next time step.
在本项目中，研究团队对EAD算法进行了全面的文献回顾，发现了以下几个方面的主要研究空白：（1）系统最优性和计算效率之间的平衡； (2) 电动汽车的指定算法（例如考虑再生制动）； （3）考虑下游交通信息（例如，预测前车状态）。 为了解决这些差距，研究团队提出了一种用于 EAD 应用的基于深度学习的轨迹规划算法（DLTPA），该算法可以被视为全局最优算法（称为基于图的轨迹规划算法或 GTPA）的近似 研究小组之前开发的。 所提出的 DLTPA 有两个过程：离线（训练）和在线（实现），它由两个主要模块组成：1）解决方案可行性检查器，用于识别是否存在受所有系统约束（例如最大约束）的可行轨迹。 加速或减速； 2）一个回归器来预测下一个时间步的速度。
Preliminary simulation study in microscopic traffic modeling software PTV VISSIM showed that the proposed DLTPA can achieve a great balance of energy savings vs. computational efforts when compared to the baseline scenario where no EAD was implemented and the optimal solution (in terms of energy savings) was provided by GTPA.
微观交通建模软件 PTV VISSIM 中的初步模拟研究表明，与未实施 EAD 且最优解决方案（在节能方面）为 由 GTPA 提供。

Introduction

The uninterrupted growth in transportation activities, for both people and goods movement, has exerted significant pressure on our socio-economics and environment. The transportation sector in the United States consumed approximated 27.5 quadrillion BTUs (British thermal unit) of energy in 2016, 92.2% percent of which came from petroleum [1]. In addition, the latest annual report by the U.S. Environmental Protection Agency (USEPA) estimated that surface transportation modes (such as passenger cars, trucks, buses, and motorcycles) contributed 1,556 MMT CO2eq to greenhouse gas (GHG) emissions in 2016, accounting for 28.5% of nationwide GHG emissions [2]. According to the same report, transportation just slightly surpassed the electric power industry (28.4%) and became the largest source of GHG across all U.S. economic sectors in 2016.
人员和货物运输的运输活动的不间断增长给我们的社会经济和环境带来了巨大的压力。 2016年，美国交通运输部门消耗了约27.5万亿BTU（英国热量单位）能源，其中92.2%来自石油[1]。 此外，美国环境保护署（USEPA）最新年度报告估计，2016年地面交通方式（如乘用车、卡车、公共汽车和摩托车）的温室气体（GHG）排放量为1,556 MMT CO2eq，占 占全国温室气体排放量的28.5%[2]。 根据同一份报告，2016年交通运输业略超过电力行业（28.4%），成为美国所有经济部门最大的温室气体来源。
On the other hand, emerging technologies such as connected vehicles (CV), transportation electrification, and edge computing have stimulated increased efforts from engineers, researchers and policymakers to tackle transportation-related energy and environmental problems. Good examples include the Applications for the Environment: Real-Time Information Synthesis (AERIS) Program initiated by the U.S. Department of Transportation [3], and the eCoMove project funded by the European Commission [4]. A variety of environmentallyfriendly CV applications, in particular those related to eco-driving strategies, have been proposed, developed, and validated [5]. Among all environmentally-friendly eco-driving strategies, the Eco-Approach and Departure (EAD) at Signalized Intersections application has shown significant promise [6–10]. In this system, a vehicle can take advantage of the signal phase and timing (SPaT) and geometric intersection description (GID) information from the upcoming signalized intersection and calculate the optimal speed to pass on a green light or to decelerate to a stop in the most eco-friendly manner. Speed recommendations may be provided to the driver using a driver-vehicle-interface (DVI) or to the vehicle systems that support automated longitudinal control capabilities.
另一方面，互联汽车（CV）、交通电气化和边缘计算等新兴技术刺激了工程师、研究人员和政策制定者加大力度解决与交通相关的能源和环境问题。 很好的例子包括美国交通部发起的环境应用：实时信息综合（AERIS）计划[3]，以及欧盟委员会资助的eCoMove项目[4]。 各种环保的CV应用，特别是与生态驾驶策略相关的应用，已经被提出、开发和验证[5]。 在所有环保的生态驾驶策略中，信号交叉口的生态进场和离场（EAD）应用已显示出巨大的前景[6-10]。 在该系统中，车辆可以利用来自即将到来的信号交叉口的信号相位和定时（SPaT）以及几何交叉口描述（GID）信息，并计算通过绿灯或减速停车的最佳速度。 最环保的方式。 可以使用驾驶员车辆接口（DVI）向驾驶员或支持自动纵向控制功能的车辆系统提供速度建议。
Due to the benefits of EAD-like eco-driving algorithms, numerous studies have focused on their development and testing [11–20]. However, many of these algorithms are not flexible enough to effectively handle customized powertrain characteristics, interaction with other traffic, road grade, and travel through multiple intersections [33]. This project aims to address some of these gaps, and the uniqueness of this research includes:
由于类似 EAD 的生态驾驶算法的优点，许多研究都集中在其开发和测试上 [11-20]。 然而，其中许多算法不够灵活，无法有效处理定制的动力系统特性、与其他交通的交互、道路坡度以及穿过多个交叉路口的行驶[33]。 该项目旨在解决其中一些差距，这项研究的独特性包括：

• Customized electric powertrain. Based on real world data, an electric vehicle (EV) energy consumption model is developed and integrated into a new eco-driving algorithm and the regenerative braking effect is taken into account.
• 定义电动动力总成。 基于现实世界数据，开发了电动汽车（EV）能耗模型，并将其集成到新的节能驾驶算法中，并考虑了再生制动效应。
• Prediction of downstream vehicle’s trajectory. Machine learning technique is applied to a snippet of a vehicle’s downstream trajectory (which may be obtained from an onboard sensor, such as radar) to predict its movement (e.g., stopping, acceleration, cruising). This information may help the vehicle better plan its trajectory for saving energy.
• 下游车辆轨迹预测。 机器学习技术应用于车辆下游轨迹的片段（可以从雷达等车载传感器获得）来预测其运动（例如停止、加速、巡航）。 这些信息可以帮助车辆更好地规划其轨迹以节省能源。
• Deep learning–based EAD algorithm. This algorithm can achieve a balance between solution optimality and computational efficiency.
• 基于深度学习的EAD算法。 该算法能够实现解最优性和计算效率之间的平衡。

Literature Review

In this section, we first review previous research on Eco-Approach and Departure (EAD) applications and then give a brief introduction on the powertrain model used for fuel/energy consumption estimation in this study.
在本节中，我们首先回顾了之前关于经济接近和远离（EAD）应用的研究，然后简要介绍了本研究中用于燃料/能耗估计的动力系统模型。

The State-of-the-Art on Eco-Approach and Departure

In the past decade, a variety of studies have been conducted on EAD, especially from the perspective of an isolated intersection. Mandava et al. [11] proposed a piecewise lineartrigonometric function–based vehicle trajectory planning algorithm for eco-driving along an urban arterial road. The algorithm was extensively evaluated and validated in simulations [21] and field testing [22], in the form of an advanced driver assistance system [23] and partially automated control [24]. It showed excellent real-time performance and substantial benefits in reducing fuel consumption and tailpipe emissions. However, significant efforts may be necessary to modify the algorithm to adapt it to customized powertrain models and rolling terrain. Based on the VT-Micro1 model, Rakha and Kamalanathsharma [13] developed a constant deceleration based eco-driving strategy to avoid full stops at signals. They later improved upon this, using multi-stage dynamic programming and recursive path-finding principles, as well as evaluation with an agent-based model [25]. Asadi and Vahidi [14] proposed a two-step predictive cruise control concept, aiming to reduce fuel use and trip time by using traffic signal status information. The first step is to determine the target speed based on an available green window, and the second step is to perform the optimal tracking of the target speed. Katsaros et al. [15] developed a Green Light Optimized Speed Advisory (GLOSA) system designed to minimize average fuel consumption and average stop delay at a traffic signal. By taking into account the queue discharging process, Chen et al. [16] developed an ecodriving algorithm for a vehicle approaching and leaving a signalized intersection to minimize a linear combination of emissions and travel time, without taking into account roadway grade information. Jin et al. [17] formulated the power-based optimal longitudinal connected ecodriving problem into a 0-1 Binary Mixed Integer Linear Programming (MILP), which is applicable to signalized intersections, non-signalized intersections, and freeways. The approach can take into account road grade effects and powertrain dynamics, but has relatively low computational efficiency. Li et al. [18] used the Legendre Pseudo-Spectral method and knotting technique to overcome the discrete gear ratio issue in the optimal control for eco-driving at signalized intersections. Huang and Peng [19] adopted a simplified powertrain model and applied the Sequential Convex Optimization approach to optimize vehicle speed trajectory at signalized intersections, which aimed to keep a balance between the optimality and real-time performance.
在过去的十年中，人们对 EAD 进行了各种各样的研究，特别是从孤立交叉口的角度进行了研究。 曼达瓦等人。 [11]提出了一种基于分段线性三角函数的车辆轨迹规划算法，用于城市主干道的经济驾驶。 该算法以先进的驾驶员辅助系统[23]和部分自动化控制[24]的形式在模拟[21]和现场测试[22]中进行了广泛的评估和验证。 它显示出出色的实时性能，并在降低油耗和尾气排放方面具有显着的效益。 然而，可能需要付出巨大的努力来修改算法以使其适应定制的动力系统模型和起伏的地形。 基于 VT-Micro1 模型，Rakha 和 Kamalanathsharma [13] 开发了一种基于恒定减速的经济驾驶策略，以避免在信号灯处完全停车。 他们后来对此进行了改进，使用多阶段动态规划和递归寻路原理，以及基于代理的模型进行评估[25]。 Asadi 和 Vahidi [14] 提出了一种两步预测巡航控制概念，旨在通过使用交通信号状态信息来减少燃料使用和行程时间。 第一步是根据可用的绿色窗口确定目标速度，第二步是对目标速度进行最优跟踪。 卡萨罗斯等人。 [15]开发了绿灯优化速度咨询（GLOSA）系统，旨在最大限度地减少交通信号灯处的平均油耗和平均停车延迟。 通过考虑队列卸载过程，Chen 等人。 [16] 为车辆接近和离开信号交叉口开发了一种经济驾驶算法，以最大限度地减少排放和行驶时间的线性组合，而不考虑道路坡度信息。 金等人。 [17]将基于功率的最优纵向连通经济驾驶问题表述为0-1二元混合整数线性规划（MILP），适用于信号交叉口、非信号交叉口和高速公路。 该方法可以考虑道路坡度效应和动力总成动力学，但计算效率相对较低。 李等人。 [18]利用勒让德伪谱方法和knoting技术克服了信号交叉口环保驾驶优化控制中的离散齿轮比问题。 Huang和Peng[19]采用简化的动力系统模型，并应用顺序凸优化方法来优化信号交叉口的车辆速度轨迹，旨在保持最优性和实时性之间的平衡。
When considering the application of an Eco-Approach and Departure system in a more realistic environment, many studies took a “reactive” approach to cope with the disturbance from the downstream traffic (e.g., switching to the car-following mode control if the subject vehicle was too close to its predecessor) or assumed traffic signals were running in a fixed-time mode [20, 26, 27]. To address these issues, some researchers specifically focused on tackling the queuing effects for Eco-Approach and Departure at Signalized Intersections (EADSI) by applying the shockwave theory [28] or data-driven techniques [29] to predict the queue length or, in essence, the trajectory of the subject vehicle’s predecessor. Other approaches were dedicated to dealing with uncertainties in traffic signal operation such as countdown information by improving the prediction of SPaT [30] or developing more robust eco-driving strategies [31, 32].
当考虑在更现实的环境中应用经济接近和驶离系统时，许多研究采取了“反应式”方法来应对下游交通的干扰（例如，如果目标车辆 与其前身太接近）或假设交通信号以固定时间模式运行 [20,26,27]。