4.8 Article

The EV revolution: The road ahead for critical raw materials demand

期刊

APPLIED ENERGY
卷 280, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2020.115072

关键词

Decarbonisation; Electric vehicles; Metal; Mined commodities

资金

  1. UK EPSRC/Faraday Institution through the research project 'Recycling of Lithium-ion Batteries (ReLIB)' [FIRG005]

向作者/读者索取更多资源

Mass adoption of electric vehicles (EVs) is anticipated in the years ahead, driven primarily by policy incentives, rising incomes, and technological advancements. However, mass adoption is predicated on the availability and affordability of the raw materials required to facilitate this transformation. The implications of material shortages are currently not well understood and previous research tends to be limited by weak representation of technological change, a lack of regional disaggregation, often inflexible and opaque assumptions and drivers, and a failure to place insights in the broader context of the raw materials industries. This paper proposes a CoMIT (Cost, Macro, Infrastructure, Technology) model that can be used to analyse the impact of mass EV adoption on critical raw materials demand and forecasts that, by 2030, demand for vehicles will increase by 27.4%, of which 13.3% will be EVs. The model also predicts large increases in demand for certain base metals, including a 37 and 18-fold increase in demand for cobalt and lithium (relative to 2015 levels), respectively. Without major changes in certain technologies, the cobalt and lithium supply chains could seriously constrain the widespread deployment of EVs. Significant demand increases are also predicted for copper, chrome and aluminium. The results also highlight the importance of China in driving demand for EVs and the critical materials needed to produce them.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Engineering, Civil

Hydropower generation, flood control and dam cascades: A national assessment for Vietnam

Viet Nguyen-Tien, Robert J. R. Elliott, Eric A. Strobl

JOURNAL OF HYDROLOGY (2018)

Article Multidisciplinary Sciences

Financial viability of electric vehicle lithium-ion battery recycling

Laura Lander, Tom Cleaver, Mohammad Ali Rajaeifar, Viet Nguyen-Tien, Robert J. R. Elliott, Oliver Heidrich, Emma Kendrick, Jacqueline Sophie Edge, Gregory Offer

Summary: The study presents a comprehensive techno-economic model to compare recycling locations and processes, providing a key tool for cost optimization in the global battery recycling economy. It shows that recycling can be economically viable, with profitability ranging depending on factors such as transport distances, wages, pack design, and recycling methods. In-country recycling is recommended to reduce emissions and transportation costs and ensure a secure materials supply chain.

ISCIENCE (2021)

Article Energy & Fuels

Optimising the geospatial configuration of a future lithium ion battery recycling industry in the transition to electric vehicles and a circular economy

Viet Nguyen-Tien, Qiang Dai, Gavin D. J. Harper, Paul A. Anderson, Robert J. R. Elliott

Summary: This paper provides an analysis of the future LiB recycling industry in the UK, considering economic, environmental, and geographical factors. It develops a supply chain model and presents ideal geographical locations for recycling facilities based on different technologies and battery material evolution.

APPLIED ENERGY (2022)

Article Business, Finance

The electric vehicle revolution: Critical material supply chains, trade and development

Benjamin Jones, Viet Nguyen-Tien, Robert J. R. Elliott

Summary: The emergence of a mass market for electric vehicles provides development opportunities for countries with abundant resources, but strong institutions are crucial to mitigate investment risks. This paper examines the outlook and factors influencing electric vehicle demand and raw material usage, emphasizing the need to assess and track market transformations.

WORLD ECONOMY (2023)

Article Energy & Fuels

Theoretical and experimental investigation on the advantages of auxetic nonlinear vortex-induced vibration energy harvesting

Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou

Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.

APPLIED ENERGY (2024)

Article Energy & Fuels

Evaluation method for the availability of solar energy resources in road areas before route corridor planning

Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan

Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.

APPLIED ENERGY (2024)

Article Energy & Fuels

Impacts of PTL coating gaps on cell performance for PEM water electrolyzer

Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender

Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.

APPLIED ENERGY (2024)

Article Energy & Fuels

Coordinated pricing mechanism for parking clusters considering interval-guided uncertainty-aware strategies

Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado

Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.

APPLIED ENERGY (2024)

Article Energy & Fuels

The establishment of evaluation systems and an index for energy superpower

Duan Kang

Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.

APPLIED ENERGY (2024)

Article Energy & Fuels

A model-based study of the evolution of gravel layer permeability under the synergistic blockage effect of sand particle transport and secondary hydrate formation

Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang

Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.

APPLIED ENERGY (2024)

Article Energy & Fuels

Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi

Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.

APPLIED ENERGY (2024)

Article Energy & Fuels

Asymmetry stagger array structure ultra-wideband vibration harvester integrating magnetically coupled nonlinear effects

Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong

Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.

APPLIED ENERGY (2024)

Article Energy & Fuels

Enhancement of hydrogen production via optimizing micro-structures of electrolyzer on a microfluidic platform

Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song

Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.

APPLIED ENERGY (2024)

Article Energy & Fuels

A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system

Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz

Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.

APPLIED ENERGY (2024)

Article Energy & Fuels

Archery-inspired catapult mechanism with controllable energy release for efficient ultralow-frequency energy harvesting

Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He

Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.

APPLIED ENERGY (2024)

Article Energy & Fuels

A novel combined probabilistic load forecasting system integrating hybrid quantile regression and knee improved multi-objective optimization strategy

Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang

Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.

APPLIED ENERGY (2024)

Article Energy & Fuels

Capacity fade prediction for vanadium redox flow batteries during long-term operations

Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung

Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.

APPLIED ENERGY (2024)

Article Energy & Fuels

State-of-charge balancing strategy of battery energy storage units with a voltage balance function for a Bipolar DC mircrogrid

Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu

Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.

APPLIED ENERGY (2024)

Article Energy & Fuels

Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings

Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen

Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.

APPLIED ENERGY (2024)