Electric Vehicle Battery Disassembly Using Interfacing Toolbox for Robotic Arms

<p>Visual servoing (VS) is a technique which utilizes information acquired from a visual sensor to control the motion of a robot. Despite the advantages of the classic VS techniques in controlling of the robots- specifically in unstructured environments- they suffer from some system’s shortcomings such as the lack of an effective mechanism for handling the existing constraints within the control procedure. To alleviate these issues, among the several approaches proposed in the literature, the receding horizon or model predictive control (MPC) technique is an appropriate candidate to be employed by the system. Any combination of the model predictive control and visual servoing is called visual predictive control (VPC) which is categorized as the classic and hybrid methods. The classic VPC schemes are developed based on the classic VS approaches and the hybrid ones employ the hybrid VS schemes to be integrated by MPC. On the other hand, to guarantee the convergence of the system in presence of the noises and uncertainties, the proposed VPC schemes are embedded in internal model control (IMC) framework.</p> <p>This work contributes by utilizing the available VPC scheme in control of different robots, such as 3 DOF catheter as a continuum robot and a 6 DOF manipulator from Denso robotics as a robot arm, and by developing two hybrid predictive controllers, conjugated VPC and depth-based VPC, with higher constraint handling capabilities. By utilizing the proposed VPC methods, some of the available limitations could be resolved in the single-robot control level. In addition, to enable the system to perform more sophisticated tasks under difficult conditions such as unexpected occlusions, a multi-agent system controlled by the mentioned VPC methods is employed. Under these conditions, by utilizing the data sharing ability among neighboring agents inside the network, the obstructed one could be provided with enough information to complete the task. At each stage, the performance of the proposed algorithm is verified through numerous simulations and experiments to show their capability in handling existing constraints while accounting for system uncertainties.</p>

Repurposed electric vehicle battery performance in second-life electricity grid frequency regulation service

Game-based system dynamics simulation of deposit-refund scheme for electric vehicle battery recycling in China

In view of the existing problems in DC traction power supply system and electric vehicle (EV) batteries charging system, a DC micro-grid is proposed: EV batteries are connected to DC traction power supply system via off-board bidirectional chargers, forming a ring DC micro-grid in a sub-region, in which EV batteries can be charged by DC traction power supply system and voltage of overhead contact system can be stabilized by the EV batteries charging and discharging system. A method that place ultra-capacitors in parallel with EV batteries is designed to improve system performance. Voltage droop control is adopted by parallel bidirectional chargers, which can achieve doublequadrant operation and avoid the circulating current as well. In the end, simulation results are presented to demonstrate the feasibility of this system.

This study proposes a hybrid visual servoing technique that is optimised to tackle the shortcomings of classical 2D, 3D and hybrid visual servoing approaches. These shortcomings are mostly the convergence issues, image and robot singularities, and unreachable trajectories for the robot. To address these deficiencies, 3D estimation of the visual features was used to control the translations in Z-axis as well as all rotations. To speed up the visual servoing (VS) operation, adaptive gains were used. Damped Least Square (DLS) approach was used to reduce the robot singularities and smooth out the discontinuities. Finally, manipulability was established as a secondary task, and the redundancy of the robot was resolved using the classical projection operator. The proposed approach is compared with the classical 2D, 3D and hybrid visual servoing methods in both simulation and real-world. The approach offers more efficient trajectories for the robot, with shorter camera paths than 2D image-based and classical hybrid VS methods. In comparison with the traditional position-based approach, the proposed method is less likely to lose the object from the camera scene, and it is more robust to the camera calibrations. Moreover, the proposed approach offers greater robot controllability (higher manipulability) than other approaches.

This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.

Current status and perspectives on recycling of end-of-life battery of electric vehicle in Korea (Republic of)

Second use of electric vehicle (EV) batteries used in the battery swapping station (BSS) is an effective way to improve economic benefits of the life span of the BSS and EV batteries. In this paper, we propose the physical structure of BSS for installing the energy storage system (ESS) of second use of EV batteries, and then establish the operation model of a BSS according to the operation mechanism between the internal components. In order to maximize the annual net present value of a BSS, the economic benefit model is established under the full consideration of the time of use (ToU) electricity price mechanism and the EV batteries charging demand. Finally, the proposed method is simulated, and the results show that according to the configuration results and operation strategies obtained by this paper, the economic benefits of BSS can be improved, and meanwhile, the volatility of the original load can be reduced.

Electromobility is associated with the ever faster development and introduction of new electric vehicles to the market. These vehicles use an electric motor to drive the wheels of the vehicle and the necessary electrical energy is stored in traction batteries. Electric vehicles have a different construction method than traditional vehicles powered by internal combustion engines. For this reason, they are used, maintained and serviced differently. Becoming more familiar with selected operational issues of electric vehicles positively affects the reliability of their use as well as the safety and comfort of driving them. One important component of electric vehicles is the traction battery. Its proper operation influences the long-term preservation of the initial energy capacity and thus the range of the vehicle. The article presents test results concerning the state of traction batteries of a small electrically powered city vehicle. The vehicle, the batteries used and the diagnostic devices used to assess the condition of the battery are all described in detail. Based on literature analysis and the observation of market trends, a fast and effective method of assessment of the technical condition of batteries in electric vehicles was proposed. The method was tested on the selected vehicle. The technical condition of the battery in the vehicle was assessed after 30,000 km and 4.5 years of operation.

State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation

The increase in global population has resulted in increased demand for stationary and portable energy, as well as increases in carbon dioxide emissions. The increases in greenhouse gases at the present trajectory will increase the global warming potential above the 2° threshold. At this threshold, it is anticipated that climate changes will adversely affect the land and sea masses. To minimize the potential of global warming and meet the increased energy demands, high-energy capacity lithium-ion (Li-ion) batteries in portable electronics and electric vehicles offer one potential solution. Current Li-ion chemistries focus on lithium cobalt oxide (LCO), lithium nickel cobalt manganese oxide (NCM), lithium nickel cobalt aluminum oxide (NCA), lithium iron phosphate (LFP), and lithium titanium oxide (LTO) as cathodes and lithium metal (Li-metal) as an anode. Other semiconductor materials are used as alloying anodes (Si, Sn) that also include chalcogenides (S), metal halides (Br), and polymers. These materials enable the cell to maintain a high charge density through enhanced Li and electron transport, lesser volumetric expansion, material dissolution, and surface reactions between electrode and electrolyte generating heat. In part, battery thermal management is controlled using passive cooling in addition to liquid cooling. However, the widespread usage of Li-ion batteries in electric vehicles also presents a problem in end-of-life disposal and adverse effects on leaching of active materials into the water table. A life cycle assessment of Li material extraction and usage indicates that every watt-hour (Wh) of storage capacity utilizes 328 Wh of energy and generates 110 g carbon dioxide equivalent (CO2-eq.) emissions. The other components in the cathode, such as Ni and Co, may also pose toxicological adverse effects if released prematurely into the environment. The trend in battery design and evolution appears to indicate size and cost reduction; increased power density; recycling and transition to Li-S, Li-Air, or organic polymers to meet the anticipated energy demands; lower CO2-eq emissions; and electric vehicle demand by 2030.

This paper presents a method to combine voice control and hybrid visual servoing for a continuum robot. In this design, the continuum robot autonomously tracks the element of interest and maintains its position in the safe region based on image information. A method of hybrid visual servoing is proposed to integrate 2D image-based visual servoing (IBVS) to ensure the elements of interests under visibility constraints and 3D position-based visual servoing (PBVS) to adjust the robot to a desired pose in the constraint region. The robotic camera thus provides stable image while tracking the element of interest. In this design, the user can move the continuum robot to acquire the desired view by voice commands. In voice control, the system can recognize connecting words to control the robot. All software modules are integrated using Robot Operating System (ROS) for controlling the continuum robot. Experimental results obtained from a prototype continuum robot verify the effectiveness of the proposed method.

Improved DETR-based visual servoing for robotic arm satellite tracking

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

It is difficult for existing methods to solve the real-time accuracy problem of battery module-level state of charge (SoC) and the impact of single battery inconsistency at the same time under dynamic operating conditions. The integration of data-driven technology and traditional algorithms is insufficient, resulting in limited error compensation effect. In view of the accuracy of the SoC estimation of electric vehicle (EV) battery packs under dynamic driving conditions, this paper proposes a hybrid SoC estimation method for battery management system (BMS) based on cloud master-slave architecture. The hybrid framework combines direct measurement methods (Coulomb counting method, open circuit voltage method), state estimation algorithms (extended Kalman filtering, traceless Kalman filtering), and data-driven technologies (neural networks, NARMA L-2 models), and verifies its effectiveness through hardware-in-the-loop experiments. The research results show that under dynamic operating conditions, the hybrid Coulomb counting+neural network (CC+NN) method has the fastest error convergence speed and is better than other methods. In addition, the proposed cloud master-slave BMS architecture significantly improves system reliability through real-time cross-verification of the SoC data of the advanced algorithms of the on-board BMS (slave device) and the master device. The experiment is based on the FTP-75 driving cycle and verifies the high efficiency of this method in practical applications.The final analysis shows that the CC+NN combination exhibits optimal error suppression ability in complex scenarios, and provides a high-precision solution for electric vehicle battery management.