•Systematically elaborate and compare the current mainstream management methods for dealing with retired batteries.•Present state-of-the-art academia and industry improvement based on pyro-, hydro-, ...bio-hydrometallurgical, and direct recycling process.•Present a thorough investigation on the key technical challenges and other obstacles of lithium-ion battery reuse.•Highlights future development trends–artificial intelligence and cloud technologies will be integrated into the management of retired batteries.
As attractive energy storage technologies, Lithium-ion batteries (LIBs) have been widely integrated in renewable resources and electric vehicles (EVs) due to their advantages such as high energy/power densities, high reliability and long service time. Although EVs basically do not produce pollution, the end-of-life (EOL) issues of LIBs cannot be ignored due to their potential economic benefits and environmental risks. Current methods for the retired batteries mainly include disposal, recycling and reuse. EV LIBs can be reused in a variety of applications with less demanding. Compared with recycling and disposal, reuse process can obtain better economic and environmental benefits. Many second life EV LIBs projects have been undertaken and demonstrated the great potential of reuse. However, the reuse should consider economic, environmental, technical, and various market perspectives. Technical challenges that must be faced include safety issues, assessment methods, screening and restructuring technologies, and comprehensive management during the reuse process. Economic feasibility issues, comprehensive supply chains, and the lack of relevant regulations also hinder large-scale development of reuse. It is foreseeable that improvements including standardization, big data and cloud-based technologies are desperately needed to maximize the industrialization of reuse and recycling.
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•Challenges and opportunities in battery modelling and control are reviewed.•Battery diagnostic approaches are reviewed and emerging new data types identified.•Application of machine learning towards ...batteries are identified and reviewed.•A perspective and framework on the integration of models, data and artificial intelligence is presented towards the creation of a battery digital twin.
Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.
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Lithium-ion batteries (LIBs) have been widely used in electric vehicles due to the advantages of high energy/power densities, high reliability and long service life. However, considering that a ...massive number of LIBs will likely retire and enter the waste stream in the near future, the handling of end-of-life LIBs must be taken carefully. The effective utilization of retired LIBs, which still remain about 70–80% of the initial capacity, can extend battery life, conserve natural resources and protect the environment. Herein, this review provides a systematic discussion on the circular value chain (CVC) of spent LIBs, and proposes a 5R principle entailing reduce, redesign, remanufacturing, repurpose and recycling in the CVC process. Then the state-of-the-art technologies for remanufacturing, and a thorough summary of key issues and applications of repurpose process, are presented in detail. Subsequently, this article presents a comprehensive discussion on the recycling process, including pre-treatments and mainstream recycling technologies, from the prospects of technical, economic and regulation perspectives. Advanced technologies such as big data, block chain and cloud-based services, as well as the improvement of regulation and standardization processes, are required to solve the issues. Finally, the future challenges and prospects for sustainable CVC are highlighted.
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•Systematic summary of the sustainable value chain of lithium-ion batteries.•A 5R principle in the circular value chain process is demonstrated.•Comprehensive analysis of reuse and recycling from different perspectives.•Remaining challenges and perspectives of circular value chain are discussed.
•Cathode electrolyte interphase plays important role in determining the life of lithium-ion batteries.•The formation of cathode electrolyte interphase is discussed.•The modification strategies of ...cathode electrolyte interphase are also presented.
Cathode electrolyte interphase (CEI) has obtained increasing attention due to the importance in sustaining full cell life. However, it has rarely been overviewed. Therefore, it is timely and necessary to write this mini-review on CEI. This mini-review will involve the formation mechanism and modification methods of CEI. The perspective on CEI is also given out. We expect that this mini-review will provide rational guidelines for the CEI design of highly-performance lithium-ion batteries (LIBs).
An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS ...consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.
Accurate estimation of the state of charge (SOC) of batteries is crucial in a battery management system. Many studies on battery SOC estimation have been investigated recently. Temperature is an ...important factor that affects the SOC estimation accuracy while it is still not adequately addressed at present. This paper proposes a SOC estimator based on a new temperature-compensated model with extended Kalman Filter (EKF). The open circuit voltage (OCV), capacity, and resistance and capacitance (RC) parameters in the estimator are temperature dependent so that the estimator can maintain high accuracy at various temperatures. The estimation accuracy decreases when applied in high current continuous discharge, because the equivalent polarization resistance decreases as the discharge current increases. Therefore, a polarization resistance correction coefficient is proposed to tackle this problem. The estimator also demonstrates a good performance in dynamic operating conditions. However, the equivalent circuit model shows huge uncertainty in the low SOC region, so measurement noise variation is proposed to improve the estimation accuracy there.
The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and ...changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.
The solid‐electrolyte interphase (SEI) generated between the electrode and the electrolyte strongly influences the performance of batteries. As the most attractive next‐generation energy storage ...system with ultrahigh energy density, the development of lithium metal batteries (LMBs) has been greatly plagued by the uncontrollable lithium (Li) dendrite and serious electrolyte decomposition resulting from the self‐derived unstable SEI with poor properties. In this perspective, the recent progress of regulating the nature and composition of the SEI to stabilize the Li metal in LMBs is summarized, followed by a discussion of the formation mechanism and the property of the SEI. The strategies for constructing a stable SEI are summarized, for example, design of a compatible electrolyte with the anode, adding self‐sacrificing additives or solvation control additives, and the regulation of nonfaradaic electric adsorption and desorption progress. Finally, the guideline for the rational design of the SEI is proposed.
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In this perspective, the properties of ideal solid electrolyte interphase are discussed, and the corresponding strategies for solid electrolyte interphase regulation in lithium metal batteries are also proposed.
The rapid development of intelligent networked vehicles (ICVs) has brought many positive effects. Unfortunately, connecting to the outside exposes ICVs to security threats. Using secure protocols is ...an important approach to protect ICVs from hacker attacks and has become a hot research area for vehicle security. However, most of the previous studies were carried out on V2X networks, while those on in-vehicle networks (IVNs) did not involve Ethernet. To this end, oriented to the new IVNs based on Ethernet, we designed an efficient secure scheme, including an authentication scheme using the Scalable Service-Oriented Middleware over IP (SOME/IP) protocol and a secure communication scheme modifying the payload field of the original SOME/IP data frame. The security analysis shows that the designed authentication scheme can provide mutual identity authentication for communicating parties and ensure the confidentiality of the issued temporary session key; the designed authentication and secure communication scheme can resist the common malicious attacks conjointly. The performance experiments based on embedded devices show that the additional overhead introduced by the secure scheme is very limited. The secure scheme proposed in this article can promote the popularization of the SOME/IP protocol in IVNs and contribute to the secure communication of IVNs.
•a layered cloud to things framework with end sensing, edge computing, cloud computing and knowledge repository.•a cloud battery management system with functions of state estimation.•multi-scale data ...visualization from cell-battery system-vehicle-transportation system.•hierarchical functional display leveraging from the cyber hierarchy and interactional network (CHAIN) framework.
An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management.
We proposed a cloud to things framework with four subsystems: end, edge, cloud and knowledge by combining digital twin and with deep learning approaches, complex detection, prediction and optimization functions. Further, we demonstrated an overall framework utilizing an end-edge-cloud architecture for a cloud-based BMS with multi-scale hierarchical data visualization leveraging from the Cyber Hierarchy and Interactional Network (CHAIN) framework. Display omitted
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