Green Energy and Intelligent Transportation最新文献_第2页

The effect of lead-based catalyst in-situ electrodeposition on the performance of iron-chromium redox flow batteries 铅基催化剂原位电沉积对铁铬氧化还原液流电池性能的影响

IF 16.4

Green Energy and Intelligent Transportation

Pub Date : 2025-07-03 DOI: 10.1016/j.geits.2025.100331

Yingchun Niu , Wenjie Lv , Yinping Liu , Ziyu Liu , Ruichen Zhou , Xuan Zhou , Weiwei Guo , Wei Qiu , Chunming Xu , Quan Xu

The performance of iron-chromium redox flow batteries is significantly influenced by the electrochemical activity of chromium and iron ions, with a particular emphasis on the reactivity of chromium. However, the impact of the chemical properties of chromium ions on the efficiency of electrochemical reactions remains largely unexplored. In this study, we introduced PbCl₂ into the electrolyte and achieved in-situ electrodeposition of the lead-based catalyst. Our findings indicate that the incorporation of lead ions effectively enhances the chromium half-reaction while inhibiting hydrogen evolution. Experimental analyses and molecular dynamics simulations reveal that PbCl₂ does not significantly affect the electrochemical performance of the electrolyte, its influence is mainly due to the electrochemical deposition on the electrode surface. The observed performance improvement is ascribed to the combined effects of Pb and Pb(ClO₃)₂, which catalyze the redox reaction of Cr³⁺/Cr²⁺. In situ differential electrochemical mass spectrometry monitoring of the hydrogen evolution signal demonstrates a clear inhibition of the hydrogen evolution reaction. Notably, the addition of 40 mM Pb²⁺ significantly reduces the overpotential of the reaction, allowing the energy efficiency of the battery to reach 83.90% at a current density of 140 mA/cm², which represents a 5.68% increase compared to the original electrolyte (78.22%). Furthermore, this configuration enables long-term stable operation over 400 cycles. This research presents an innovative approach to enhancing the performance of iron-chromium redox flow batteries, characterized by its simplicity and cost-effectiveness.

铁铬氧化还原液流电池的性能受铬离子和铁离子的电化学活性的显著影响，其中铬离子的反应性尤为重要。然而，铬离子的化学性质对电化学反应效率的影响在很大程度上仍未被探索。在本研究中，我们将PbCl2引入电解液中，实现了铅基催化剂的原位电沉积。我们的研究结果表明，铅离子的掺入有效地促进了铬半反应，同时抑制了析氢。实验分析和分子动力学模拟表明，PbCl2对电解质的电化学性能没有显著影响，其影响主要是由于电极表面的电化学沉积。Pb和Pb(ClO3)2的共同作用促进了Cr3+/Cr2+的氧化还原反应。原位差分电化学质谱法监测析氢信号表明，析氢反应有明显的抑制作用。值得注意的是，40 mM Pb2+的加入显著降低了反应的过电位，在电流密度为140 mA/cm2时，电池的能量效率达到83.90%，比原电解质（78.22%）提高了5.68%。此外，这种配置可以实现超过400个周期的长期稳定运行。本研究提出了一种创新的方法来提高铁铬氧化还原液流电池的性能，其特点是简单和成本效益。

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引用次数: 0

Cycle-efficient modeling for degradation staging and early life prediction of lithium batteries 锂电池退化分期和早期寿命预测的循环效率建模

IF 16.4

Green Energy and Intelligent Transportation

Pub Date : 2025-06-27 DOI: 10.1016/j.geits.2025.100338

Can Wang , Renjie Wang , Jianming Li , Zhuangzhuang Li , Quanqing Yu

An effective and time-saving early life prediction model is crucial for rapid battery assessment. However, existing models face a dilemma: they either rely heavily on extensive historical data or provide limited predictive insights into battery degradation. To address this, this study proposes a cycle-efficient battery life assessment framework integrating data-driven and empirical models. The framework consists of two components: degradation stage detection relying solely on data from one cycle and early life prediction using five-cycle data. The early life prediction model is capable of achieving joint prediction of the battery's remaining useful life and the cycle to knee point. Experimental results demonstrate that the degradation staging model achieves an accuracy of 0.977,6 for lithium iron phosphate batteries. Meanwhile, the early life prediction model yields mean absolute percentage errors of 10.5% for remaining useful life and 12.8% for the cycle to knee predictions. The model's accuracy and generalizability have been validated across diverse battery types, health states, and operating conditions. This proposed framework exhibits excellent generalizability capability under all evaluated scenarios, establishing a robust foundation for rapid battery design assessment and retirement decisions.

有效、省时的早期寿命预测模型对于电池的快速评估至关重要。然而，现有的模型面临着一个困境：它们要么严重依赖于大量的历史数据，要么对电池退化提供有限的预测见解。为了解决这个问题，本研究提出了一个循环高效的电池寿命评估框架，该框架集成了数据驱动模型和经验模型。该框架由两个部分组成：仅依赖于一个周期数据的退化阶段检测和使用五个周期数据的早期寿命预测。早期寿命预测模型能够实现对电池剩余使用寿命和循环至膝点的联合预测。实验结果表明，该模型对磷酸铁锂电池的降解分级精度为0.977,6。同时，早期寿命预测模型对剩余使用寿命的平均绝对百分比误差为10.5%，对周期到膝盖预测的平均绝对百分比误差为12.8%。该模型的准确性和通用性已经在不同的电池类型、健康状态和操作条件下进行了验证。该框架在所有评估情景下都表现出出色的通用性，为快速电池设计评估和退役决策奠定了坚实的基础。

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引用次数: 0

Dynamic range compression dual-domain attention network for tunnel extreme exposure image enhancement in transportation visual systems 交通视觉系统中隧道极端曝光图像增强的动态范围压缩双域注意网络

IF 16.4

Green Energy and Intelligent Transportation

Pub Date : 2025-06-26 DOI: 10.1016/j.geits.2025.100337

Bu Xu , Jingyi Tang , Jue Li , Shuai Zhou , Chen Liu

The rapid expansion of road transportation infrastructure has led to an increased prevalence of tunnel scenarios, which are often characterized by extreme lighting conditions. These “black hole” and “white hole” effects, caused by stark brightness contrasts between tunnel entrances and interiors, severely compromise the image acquisition capabilities of transportation visual systems. To address these challenges, this study proposes a dynamic range compression dual-domain attention network (DRC-DFANet) for real-time and high-precision enhancement of tunnel images. The core architecture integrates a dynamic frequency-domain attention module (DFAM) and a spatial self-calibrated convolution (SCConv) module to concurrently optimize global illumination coordination and local detail restoration. The DFAM employs wavelet transform to decouple features into low-frequency and high-frequency components, enabling dynamic brightness adjustment and enhanced detail preservation. The SCConv module establishes interdependencies between channel and spatial dimensions to adaptively calibrate local contrast. Experiments on benchmark tunnel datasets demonstrate the superior performance of DRC-DFANet, with peak signal-to-noise ratio improvements of up to 8.8% and significant enhancements in high-frequency energy ratio, subband correlation, and exposure error metrics compared to state-of-the-art methods. Qualitative analyses validate the model's effectiveness in mitigating the “black hole” and “white hole” effects, preserving critical details such as vehicle contours, lane markings, and traffic signs. The transferability of DRC-DFANet is further confirmed on related transportation scenarios, underscoring its potential for wide-ranging applications in visual systems for autonomous driving, traffic monitoring, and other transportation-related tasks.

道路交通基础设施的快速扩张导致隧道场景的增加，这些场景通常以极端照明条件为特征。这些“黑洞”和“白洞”的效果，是由隧道入口和内部鲜明的亮度对比造成的，严重损害了交通视觉系统的图像采集能力。为了解决这些挑战，本研究提出了一种动态范围压缩双域注意网络（DRC-DFANet），用于隧道图像的实时和高精度增强。核心架构集成了动态频域注意模块（DFAM）和空间自校准卷积模块（SCConv），可同时优化全局照明协调和局部细节恢复。DFAM采用小波变换将特征解耦为低频和高频分量，实现动态亮度调节和增强细节保存。SCConv模块建立通道和空间维度之间的相互依赖关系，以自适应校准局部对比度。在基准隧道数据集上的实验证明了DRC-DFANet的卓越性能，与最先进的方法相比，峰值信噪比提高了8.8%，高频能量比、子带相关性和曝光误差指标也有了显著提高。定性分析验证了该模型在减轻“黑洞”和“白洞”效应方面的有效性，并保留了车辆轮廓、车道标记和交通标志等关键细节。DRC-DFANet的可转移性在相关交通场景中得到进一步证实，强调了其在自动驾驶、交通监控和其他交通相关任务的视觉系统中的广泛应用潜力。

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引用次数: 0

Toward smart railway maintenance: AI-enhanced Non-Destructive Evaluation using Vision Transformers and CNNs for fastener defect detection 走向智能铁路维修：利用视觉变压器和cnn进行紧固件缺陷检测的人工智能增强无损评估

IF 16.4

Green Energy and Intelligent Transportation

Pub Date : 2025-06-26 DOI: 10.1016/j.geits.2025.100332

Samira Mohammadi , Sasan Sattarpanah Karganroudi , Mehdi Adda , Hussein Ibrahim

Predictive health management and maintenance of transport infrastructure are critical for preventing accidents and service disruptions. Applying Non-Destructive Evaluation (NDE) and imaging techniques is essential for identifying irregularities without causing harm. This research utilizes pre-trained models and incorporates transfer learning concepts to overcome dataset constraints. This study assesses the effectiveness of various machine learning models, including the Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), VGG19, VGG16, and ResNet50, in enhancing NDE for railway track fasteners. ViT and DeiT, both transformer-based models, emerged as the top performers due to their superior learning efficiencies and generalization capabilities, augmented by precise hyperparameter tuning. VGG models are a reliable alternative, while ResNet50 is better suited for applications prioritizing computational efficiency over accuracy.

运输基础设施的预测性健康管理和维护对于防止事故和服务中断至关重要。应用无损评价（NDE）和成像技术对于识别不规则而不造成伤害至关重要。本研究利用预训练模型并结合迁移学习概念来克服数据集约束。本研究评估了各种机器学习模型的有效性，包括视觉变压器（ViT）、数据高效图像变压器（DeiT）、VGG19、VGG16和ResNet50，以增强铁路轨道紧固件的无损检测。ViT和DeiT都是基于变压器的模型，由于其优越的学习效率和泛化能力，以及精确的超参数调整，它们成为了表现最好的模型。VGG模型是一种可靠的替代方案，而ResNet50更适合优先考虑计算效率而不是准确性的应用程序。

引用次数: 0

Integrated bidirectional electric vehicle battery network for sustainable communities: A planning framework 可持续社区的综合双向电动汽车电池网络：规划框架

IF 16.4

Green Energy and Intelligent Transportation

Pub Date : 2025-06-25 DOI: 10.1016/j.geits.2025.100333

Srisanthosh Sekar , Alampratap Singh Tiwana , Kuljeet Singh Grewal

The transition towards sustainable and net-zero energy communities has become imperative in addressing the challenges of climate change and ensuring a resilient energy future. This work proposes an innovative planning framework through the development of an integrated bidirectional electric vehicle (EV) battery storage network for net-zero communities. The proposed framework is intended for neighborhood planning and integrates a bidirectional charging infrastructure that allows EV batteries to seamlessly contribute to the grid during periods of high demand or store excess renewable energy during off-peak hours. To analyze various EV microgrid integration scenarios, a combined Matlab-Simulink and EnergyPlus simulation environment is proposed to simulate EV battery networks in neighborhood settings. This study examines state of charge (SoC), and energy exchange characteristics based on specific user behaviors and charging scenarios. A neighborhood archetype of 48 single-family detached houses is considered along with five EV use profiles (EVPs) for the demonstration of the proposed method. For the considered neighborhood, in winter, EVPs have eliminated the peak loads during early morning hours (1 am–6 am) by discharging stored energy. In spring, loads exceeding the base load are observed from 1 am to 10 am, with all EVPs discharging energy until 9 am and then recharging during off-peak hours. Summer required strategic charging management, with EVPs supporting peak loads from 7 am to 6 pm. In the fall, EVPs discharged energy from 12:01 am–6 am and recharged from 10 am to 6 pm. The study introduces the EVP peak support index facilitating real-time charging adjustments and incentivizing greater participation. By leveraging this index, smart charging systems can develop algorithms to control charging times based on grid needs, ensuring efficient energy distribution and enhanced grid stability. This framework offers a robust approach to scenario generation for energy and urban planners during the neighborhood planning stages predicting energy performance and management.

向可持续和净零能源社区过渡已成为应对气候变化挑战和确保弹性能源未来的必要条件。这项工作提出了一个创新的规划框架，通过开发一个集成的双向电动汽车（EV）电池存储网络为净零社区。拟议的框架旨在进行社区规划，并集成双向充电基础设施，允许电动汽车电池在高需求期间无缝地为电网做出贡献，或在非高峰时段存储多余的可再生能源。为了分析各种电动汽车微电网集成场景，提出了Matlab-Simulink和EnergyPlus相结合的仿真环境，对邻域环境下的电动汽车电池网络进行仿真。本研究考察了基于特定用户行为和充电场景的充电状态（SoC）和能量交换特性。48户独立住宅的社区原型以及5个EV使用剖面（evp）被考虑用于演示所提出的方法。对于考虑的社区，在冬季，evp通过释放储存的能量，消除了清晨（凌晨1点至6点）的高峰负荷。在春季，从上午1点到上午10点观察到负荷超过基本负荷，所有evp放电到上午9点，然后在非高峰时段充电。夏季需要战略充电管理，evp支持早上7点到下午6点的高峰负荷。在秋季，evp在上午12:01 - 6点放电，并在上午10点至下午6点充电。该研究引入了EVP峰值支持指数，便于实时调整收费，激励更多人参与。通过利用该指数，智能充电系统可以根据电网需求开发算法来控制充电时间，从而确保高效的能源分配和增强电网的稳定性。该框架为能源和城市规划者在社区规划阶段预测能源绩效和管理提供了一个强大的方案生成方法。

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引用次数: 0

Energy-saving control of intelligent connected plug-in hybrid electric vehicle via fusing driving intention of front vehicle 基于前置车辆驾驶意图融合的插电式智能互联混合动力汽车节能控制

IF 16.4

Green Energy and Intelligent Transportation

Pub Date : 2025-06-24 DOI: 10.1016/j.geits.2025.100330

Guanying Liu , Shiquan Shen , Yonggang Liu , Yuanjian Zhang , Yu Liu , Zheng Chen , Fengxiang Guo

Velocity variation of front vehicle substantially influences the driving performance and energy consumption of the following vehicle, particularly for hybrid electric vehicles (HEVs). Fusing the intention identification of front vehicle into the control of following HEV can facilitate speed optimization, energy saving and operation efficiency promotion. Inspired by this, an effective speed optimization and energy management approach is designed for the following plug-in HEV (PHEV) with the support of front vehicle's driving style identification. To this end, an improved K-means clustering approach and the support vector machine algorithm are respectively employed to cluster and distinguish the driving intention. Next, the next step velocity of front vehicle is predicted by Elman neural network, based on the current velocity and the identified driving intention. Subsequently, a multi-objective speed optimization problem is formulated with the consideration of powertrain efficiency, vehicle comfort, tracking capability and vehicle safety. Then, a power distribution strategy is designed based on model predictive control with a clipped double Q-learning algorithm to allocate the energy flow in different energy sources. The simulation findings demonstrate that the proposed strategy not only achieves preferable following effect and comfort, but also leads to 97.01% energy economy optimality supplied by dynamic programming.

前车的速度变化对后车的行驶性能和能耗影响很大，尤其是混合动力汽车。将前车意图识别融合到后续混合动力汽车的控制中，有利于速度优化、节能和运行效率提升。受此启发，在前车驾驶风格识别的支持下，针对以下插电式混合动力汽车（PHEV）设计了一种有效的速度优化和能量管理方法。为此，分别采用改进的K-means聚类方法和支持向量机算法对驾驶意图进行聚类和区分。然后，基于当前车速和识别出的驾驶意图，利用Elman神经网络预测前车下一步车速；在此基础上，建立了考虑动力系统效率、车辆舒适性、跟踪能力和车辆安全性的多目标速度优化问题。然后，设计了一种基于模型预测控制的功率分配策略，采用剪短双q学习算法对不同能源的能量流进行分配。仿真结果表明，该策略不仅具有较好的跟随效果和舒适性，而且具有97.01%的动态规划能源经济最优性。

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引用次数: 0

A joint time-frequency analysis of the mechanical-electrochemical coupling mechanism from particles to electrodes for the Li-ion battery 锂离子电池从颗粒到电极的机械-电化学耦合机制时频联合分析

IF 16.4

Green Energy and Intelligent Transportation

Pub Date : 2025-06-02 DOI: 10.1016/j.geits.2025.100322

Zihan Meng , Yuxuan Bai , Fangzhou Zhang , Jiujun Zhang , Qiu-An Huang

Diffusion-induced stress (DIS) originates from the shrinkage/expand during Li extraction/insertion from/into the active particle for the Li-ion battery (LIB). Till today, the two-way coupled mechanical-electrochemical mechanism is still unclear. The above challenge can be decomposed into 2W + lH as follows: (i) Why need to reveal the two-way coupled mechanical-electrochemical mechanism? (ii) What is the two-way coupled mechanical-electrochemical mechanism? (iii) How to reveal the two-way coupled mechanical-electrochemical mechanism. In the process of answering the above 2W + lH, the following contributions have been made in this work: (i) An electro-chemo-mechanical (ECM) model is established for the LIB, in which the mechanical-electrochemical coupling is two-way; (ii) The mechanical-electrochemical responses are solved for the ECM model in the time/frequency domain, respectively; (iii) The time-domain analysis shows that DIS enhances Li diffusion at the early and middle stages of discharge, while DIS inhibits Li diffusion at the end of discharge; (iv) The frequency-domain analysis shows that stress mainly affects solid-phase diffusion instead of electrolyte-phase diffusion. In a word, the multi-scale analysis quantitatively analyzes the impact of DIS on Li diffusion on the particle scale and reveals the two-way coupled mechanical-electrochemical mechanism on the electrode scale. The above results provide theoretical support for the battery manufacture and stress monitoring.

扩散诱发应力（diffusion induced stress， DIS）是锂离子电池（LIB）活性粒子在锂离子离子萃取/插入/进入过程中的收缩/膨胀引起的。到目前为止，机械-电化学的双向耦合机理仍不清楚。上述挑战可以分解为2W + lH: (i)为什么需要揭示双向耦合的机械-电化学机理？（ii）什么是双向耦合的机械-电化学机制？（三）如何揭示双向耦合的机械-电化学机理。在回答上述2W + lH问题的过程中，本工作做出了以下贡献：(i)建立了LIB的电化学-机械（ECM）模型，其中机械-电化学耦合是双向的；（ii）分别在时域和频域中求解ECM模型的力学-电化学响应；（iii）时域分析表明，DIS在放电前期和中期增强了锂离子的扩散，而在放电后期抑制了锂离子的扩散；（iv）频域分析表明，应力主要影响固相扩散而非电解相扩散。总之，多尺度分析定量分析了DIS在颗粒尺度上对Li扩散的影响，揭示了电极尺度上双向耦合的机械-电化学机理。上述结果为电池制造和应力监测提供了理论支持。

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引用次数: 0

A comprehensive review on deep learning applications in advancing biodiesel feedstock selection and production processes 深度学习在推进生物柴油原料选择和生产过程中的应用综述

Green Energy and Intelligent Transportation

Pub Date : 2025-06-01 DOI: 10.1016/j.geits.2025.100260

Olugbenga Akande , Jude A. Okolie , Richard Kimera , Chukwuma C. Ogbaga

Biodiesel as a renewable alternative to conventional diesel is a growing topic of interest due to its potential environmental benefits. It is typically produced from oilseed crops such as soybean, rapeseed, palm oil, or animal fats. However, its sustainability is debated, primarily because of the reliance on edible oil feedstocks and associated economic and environmental concerns. This study explores alternative, non-edible feedstocks, such as algae and jatropha, that do not compete with food production, offering increased sustainability. Despite their potential, these feedstocks are hindered by high production costs. To address these challenges, innovative approaches in feedstock assessment are imperative for ensuring the long-term viability of biodiesel as an alternative fuel. This review examines explicitly the application of deep learning techniques in selecting and evaluating biodiesel feedstocks. It focuses on their production processes and the chemical and physical properties that impact biodiesel quality. Our comprehensive analysis demonstrates that ANNs provide significant insights into the feedstock assessment process, emerging as a potent tool for identifying new correlations within complex datasets. By leveraging this capability, ANNs can significantly advance biodiesel research, producing more sustainable and efficient feedstock production. The study concludes by highlighting the substantial potential of ANN modeling in contributing to renewable energy strategies and expanding biodiesel research, underscoring its vital role in accelerating the development of biodiesel as a sustainable fuel alternative.

生物柴油作为传统柴油的可再生替代品，由于其潜在的环境效益而日益受到关注。它通常由油籽作物如大豆、油菜籽、棕榈油或动物脂肪制成。然而，其可持续性存在争议，主要是因为对食用油原料的依赖以及相关的经济和环境问题。这项研究探索了替代的、不可食用的原料，如藻类和麻疯树，它们不会与粮食生产竞争，从而提高了可持续性。尽管具有潜力，但这些原料受到高生产成本的阻碍。为了应对这些挑战，创新的原料评估方法对于确保生物柴油作为替代燃料的长期可行性至关重要。本文综述了深度学习技术在生物柴油原料选择和评价中的应用。它侧重于它们的生产过程以及影响生物柴油质量的化学和物理性质。我们的综合分析表明，人工神经网络为原料评估过程提供了重要的见解，成为识别复杂数据集中新相关性的有力工具。通过利用这一能力，人工神经网络可以显著推进生物柴油的研究，生产更可持续、更高效的原料。该研究最后强调了人工神经网络模型在促进可再生能源战略和扩大生物柴油研究方面的巨大潜力，强调了其在加速生物柴油作为可持续燃料替代品的发展方面的重要作用。

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引用次数: 0

A novel metaheuristic approach for simultaneous loss minimization and torque ripple reduction of DTC- IM driven EV 一种新的元启发式方法，用于同时减小直接转矩控制电机的损耗和转矩脉动

Green Energy and Intelligent Transportation

Pub Date : 2025-06-01 DOI: 10.1016/j.geits.2025.100254

Anjan Kumar Sahoo

The efficiency and torque ripple of an electric vehicle (EV) determine its performance and driving range. An optimum reference flux increases efficiency and decreases torque ripple and harmonics. This strategy used in the current literature is based on either a lookup table or a search control approach. However, these methods have convergence issues at optimal values, require large memory spaces, have higher computational complexity, and are difficult to implement. In the recent literature, efforts have been made to improve either the efficiency or the ripple, whereas in this paper, a multi-objective dynamic reference flux selection algorithm based on teamwork optimization is used to improve the efficiency and ripples simultaneously for a wide range of operating scenarios. The proposed dynamic reference flux selection algorithm is evaluated numerically and compared using standard drive cycles, and the amount of energy a vehicle uses during different drive cycles is compared. The results obtained justify the effectiveness and feasibility of the proposed algorithm over a wide range of driving conditions.

电动汽车的效率和转矩脉动决定着电动汽车的性能和续驶里程。最佳参考磁通可提高效率，减少转矩脉动和谐波。当前文献中使用的这种策略是基于查找表或搜索控制方法。然而，这些方法在最优值处存在收敛问题，需要较大的内存空间，具有较高的计算复杂度，并且难以实现。在最近的文献中，已经努力提高效率或波纹，而在本文中，采用基于团队优化的多目标动态参考通量选择算法来同时提高效率和波纹，适用于广泛的操作场景。采用标准驱动循环对所提出的动态参考通量选择算法进行了数值评价和比较，并比较了车辆在不同驱动循环下的能耗。仿真结果证明了该算法在多种工况下的有效性和可行性。

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引用次数: 0

The investigation of reinforcement learning-based end-to-end decision-making algorithms for autonomous driving on the road with consecutive sharp turns 基于强化学习的连续急转弯道路自动驾驶端到端决策算法研究

Green Energy and Intelligent Transportation

Pub Date : 2025-06-01 DOI: 10.1016/j.geits.2025.100288

Tongyang Li, Jiageng Ruan, Kaixuan Zhang

Learning-based algorithm attracts great attention in the autonomous driving control field, especially for decision-making, to meet the challenge in long-tail extreme scenarios, where traditional methods demonstrate poor adaptability even with a significant effort. To improve the autonomous driving performance in extreme scenarios, specifically consecutive sharp turns, three deep reinforcement learning algorithms, i.e. Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic policy gradient (TD3), and Soft Actor-Critic (SAC), based decision-making policies are proposed in this study. The role of the observation variable in agent training is discussed by comparing the driving stability, average speed, and consumed computational effort of the proposed algorithms in curves with various curvatures. In addition, a novel reward-setting method that combines the states of the environment and the vehicle is proposed to solve the sparse reward problem in the reward-guided algorithm. Simulation results from the road with consecutive sharp turns show that the DDPG, SAC, and TD3 algorithms-based vehicles take 367.2, 359.6, and 302.1 s to finish the task, respectively, which match the training results, and verifies the observation variable role in agent quality improvement.

基于学习的算法在自动驾驶控制领域备受关注，特别是在决策方面，以应对长尾极端场景的挑战，传统方法即使付出很大努力也表现出较差的适应性。为了提高极端情况下的自动驾驶性能，特别是连续急转弯，本研究提出了三种深度强化学习算法，即深度确定性策略梯度（deep Deterministic Policy Gradient， DDPG）、双延迟深度确定性策略梯度（Twin Delayed deep Deterministic Policy Gradient, TD3）和基于软行为者评论（Soft Actor-Critic， SAC）的决策策略。通过比较各算法在不同曲率曲线上的行驶稳定性、平均速度和消耗的计算量，讨论了观测变量在智能体训练中的作用。此外，针对奖励引导算法中存在的奖励稀疏问题，提出了一种结合环境状态和车辆状态的奖励设置方法。连续急转弯道路的仿真结果表明，基于DDPG、SAC和TD3算法的车辆完成任务的时间分别为367.2秒、359.6秒和302.1秒，与训练结果吻合，验证了观察变量在智能体质量提升中的作用。

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引用次数: 0