Automation

Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. This work was conducted as a case study to demonstrate the concept of the autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported, and future research directions are suggested.

Mo Qu

D. T. Pham

Faraj Altumi

Adeyemisi Gbadebo

Natalia Hartono

Kaiwen Jiang

Mairi Kerin

Feiying Lan

Marcel Micheli

Shuihao Xu

Yongjing Wang

Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study

插电式混合动力电动汽车电池拆解的机器人拆解平台：案例研究

A Mobile ad hoc Network (MANET) is a collection of wireless mobile nodes that temporarily establish a network without centralized administration or fixed infrastructure. Designing the routing of adequate routing protocols is a major challenge given the constraints of battery, bandwidth, multi-hop, mobility, and enormous network sizes. Recently, Swarm Intelligence (SI) methods have been employed in MANET routing due to similarities between swarm behavior and routing. These methods are applied to obtain ideal solutions that ensure flexibility. This paper implements an enhanced Particle Swarm Optimization (EPSO) algorithm that improves MANET performance by enhancing the routing protocol. The proposed algorithm selects the stable path by considering multiple metrics such as short distance, delay of the path, and energy consumption. The simulation results illustrate that the EPSO outperforms other existing approaches regarding throughput, PDR, and number of valid paths.

Ohood Almutairi

Enas Khairullah

Abeer Almakky

Reem Alotaibi

Routing Enhancement in MANET Using Particle Swarm Algorithm

基于粒子群算法的移动自组网路由优化

J. Heinrich

T. Kuhn

Verwaltungsschale Integration von KMU zu Wertschöpfungsnetzwerken mittels virtueller Fabriken

中小企业融入价值创造网络的虚拟工厂管理壳集成

Electric Vehicles (EVs) are set to play a crucial role in the energy transition. Although EVs offer significant environmental benefits, their technology still faces major challenges related to performance optimization, energy efficiency improvement, and cost reduction. A key point to address these challenges is the accurate identification of the speed/torque operating points of the drive systems. However, this identification is generally achieved using mechanical sensors, which are fragile, bulky, and expensive. This paper aims to develop, implement, and validate a speed/torque observer in real time based on the Extended Kalman Filter (EKF) approach for an EV equipped with an Open-End Winding Induction Motor with Dual Inverter (OEWIM-DI). The implementation of the EKF is based on the state modeling of the OEWIM-DI, enabling the observation of the torque and speed using voltage and current measurements. The validation of this approach is conducted experimentally on the FPGA and DS1104 boards. The results show that this approach offers excellent performance in terms of accuracy, stability, and real-time response speed. These results suggest that the proposed method could significantly contribute to the advancement of EV technology by providing a more robust and cost-effective alternative to traditional mechanical sensors while improving the overall efficiency and performance of EV drive systems.

Younes Djellouli

Sid Ahmed El Mehdi Ardjoun

Emrah Zerdali

Mouloud Denai

Houcine Chafouk

The Real-Time Observation of Electric Vehicle Operating Points Using an Extended Kalman Filter

使用扩展卡尔曼滤波的电动汽车运行点实时观测

A method for cooperative transportation, which required formation change in a traveling environment, is gaining interest. Deep reinforcement learning is used in formation changes for multi-robot cases. The MADDPG (Multi-Agent Deep Deterministic Policy Gradient) method is popularly used for recognized environments. On the other hand, re-learning may be required in unrecognized circumstances by using the MADDPG method. Although the development of MADDPG using model-based learning and imitation learning has been applied to reduce learning time, it is unclear how the learning results are transferred when the number of robots changes. For example, in the GASIL-MADDPG (Generative adversarial self-imitation learning and Multi-agent Deep Deterministic Policy Gradient) method, how the results of three robot training can be transferred to the four robots’ neural networks is uncertain. Nowadays, Scaled Dot Product Attention (SDPA) has attracted attention and is highly impactful for its speed and accuracy in natural language processing. When transfer learning is combined with fast computation, the efficiency of edge-level re-learning is improved. This paper proposes a formation change algorithm that allows easy and fast multi-robot knowledge transfer using SDPA combined with MAPPO (Multi-Agent Proximal Policy Optimization), compared to other methods. This algorithm applies SDPA to multi-robot formation learning and performs fast learning by transferring the acquired knowledge of formation changes to a certain number of robots. The proposed algorithm is verified by simulating the robot formation change and was able to achieve dramatic high-speed learning capabilities. The proposed SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization) learned 20.83 times faster than the Deep Dyna-Q method. Furthermore, using transfer learning from a three-robot to five-robot case, the method shows that the learning time can be reduced by about 56.57 percent. A scenario of three-robot to five-robot is chosen based on the number of robots often used in cooperative robots.

Almira Budiyanto

Keisuke Azetsu

Nobutomo Matsunaga

Accelerated Transfer Learning for Cooperative Transportation Formation Change via SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization)

通过SDPA-MAPPO（缩放点积注意力-多智能体近端策略优化）加速合作运输编队变化的迁移学习

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

Mohammad Mohammadzadeh

Gül E. Okudan Kremer

Sigurdur Olafsson

Paul A. Kremer

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

基于深度学习和工程引导数据增强的再制造缸盖AI驱动裂纹检测

Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. The first step is to create a highway traffic environment where the agent can be guided safely through surrounding vehicles. A hierarchical control framework is then provided to manage high-level driving decisions and low-level control commands, such as speed and acceleration. Next, a special DRL-based method called deep deterministic policy gradient (DDPG) is used to derive decision strategies for use on the highway. The performance of the DDPG algorithm is compared with that of the DQN and PPO algorithms, and the results are evaluated. The simulation results show that the DDPG algorithm can effectively and safely handle highway traffic tasks.

Ali Rizehvandi

Shahram Azadi

Arno Eichberger

Decision-Making Policy for Autonomous Vehicles on Highways Using Deep Reinforcement Learning (DRL) Method

高速公路自动驾驶车辆的深度强化学习决策制定策略

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2020，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Journal Fees	$Details
Average review cycle	网友分享经验：7 Weeks

Automation

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Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study

Routing Enhancement in MANET Using Particle Swarm Algorithm

Verwaltungsschale Integration von KMU zu Wertschöpfungsnetzwerken mittels virtueller Fabriken

The Real-Time Observation of Electric Vehicle Operating Points Using an Extended Kalman Filter

Accelerated Transfer Learning for Cooperative Transportation Formation Change via SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization)

Routing Enhancement in MANET Using Particle Swarm Algorithm

The Real-Time Observation of Electric Vehicle Operating Points Using an Extended Kalman Filter

Accelerated Transfer Learning for Cooperative Transportation Formation Change via SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization)

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Decision-Making Policy for Autonomous Vehicles on Highways Using Deep Reinforcement Learning (DRL) Method