Electric Vehicle Battery Research Articles

Consumer preference and willingness-to-pay for formal recycling of electric vehicle batteries: a discrete choice experiment study

The burgeoning electric vehicle (EV) market poses a substantial challenge to battery recycling systems, yet understanding EV battery recycling behavior from the demand side remains limited. Previous studies have analyzed perceptual or attitudinal factors, neglecting the observable attributes of EV battery recycling. To this end, we proposed a discrete choice model to investigate the differences between formal and informal recycling behaviors, identifying consumer preferences and willingness to pay. By analyzing 1190 sample data collected from Chongqing, China, we find that: (1) The formal recycling market exhibits greater sensitivity to prices compared to the informal recycling market. (2) The formal recycling market favors recycling by EV battery producers, whereas the informal recycling market shows the least preference for recycling by automobile producers. (3) Door-to-door recycling services are the most effective in facilitating the transition from informal to formal recycling markets for EV batteries. (4) Capacity subsidy policies outperform one-time fixed subsidy policies in incentivizing formal recycling. (5) The formal recycling market for EV batteries necessitates "traceability to the recycling outlet", as opposed to being untraceable. (6) The high-awareness group exhibits greater sensitivity to government policies compared to those with lower environmental concerns and less knowledge of EV battery recycling.

Thermal management optimization strategy for lithium-ion battery based on phase change material and fractal fin

In order to solve the problem of temperature control of lithium-ion battery (LB) in electric vehicles, a new battery thermal management system (BTMS) based on phase change material (PCM) coupled fractal fin (FF) was proposed. The effects of latent heat, thickness, and thermal conductivity of PCM (paraffin) on the thermal properties of BTMS were investigated. Subsequently, the fin structure was added to the BTMS to improve the heat transfer capacity, and the number of fins, aspect ratio, material and structure morphology were explored. The results show that when the latent heat of PCM is 200 kJ/kg, the thickness is 2 cm, and the thermal conductivity is 0.8 W/(m·K), the temperature of LB is 34.9 K lower than that of LB without the BTMS at 4C. In addition, the BTMS has the best performance when FF-II with a fin count of 5, aspect ratio of 7:1, and material of carbon steel are used. The temperatures of LB are 301.8 K, 303.9 K, 305.4 K, and 307.3 K at 1 to 4C, respectively, and the temperature difference in BTMS can be controlled within 2.5 K. This study provides some guidance for the optimization of BTMS.

In-depth evaluation of laser welding of thick busbar to 21700 Li-ion cell terminal for electric supercar vehicle battery pack

In-depth evaluation of laser welding of thick busbar to 21700 Li-ion cell terminal for electric supercar vehicle battery pack

Lithium‑sulfur batteries for next-generation automotive power batteries carbon emission assessment and sustainability study in China

The rise of electric vehicles has ushered in a revolution in the automotive industry, propelling the global automotive sector towards sustainable development. However, considerable controversy persists regarding the comprehensive performance of electric vehicle batteries and their environmental impact. The development of next-generation power batteries, aimed at enhancing energy storage performance while mitigating environmental consequences, has become a focal point of research in this field. Lithium‑sulfur batteries (LSBs), with their innovative structural design and environmentally friendly materials, not only enhance energy storage performance but also harbor significant environmental potential. When coupled with an all-solid-state battery structure, the all-solid-state lithium‑sulfur battery (A-LSB) demonstrates even more superior performance. This combination represents an ideal next-generation power battery for automotive applications, offering heightened efficiency and environmental sustainability. This study conducts a prospective life cycle assessment (LCA) to examine the environmental performance, particularly carbon emissions, during the production processes of LSBs and A-LSBs in the Chinese region. The primary objective is to uncover the potential for sustainable development in the future of lithium‑sulfur battery technologies. During the research process, we conducted comparative validation by comparing with traditional ternary lithium-ion batteries (NCM) and lithium iron phosphate batteries (LFP). Experimental setups were designed to simulate both laboratory and industrial production scenarios, with a primary focus on analyzing the sustainable performance of LSB and A-LSB. The results indicate that, compared to traditional lithium-ion batteries, both LSB and A-LSB exhibit lower carbon emissions and superior environmental performance, with A-LSB showing particular promise. However, A-LSB demonstrates higher sensitivity to environmental impacts, displaying significant variations before and after industrial production. Additionally, it exhibits a greater dependency on regional resources and energy structures. In contrast, LSB displays better regional adaptability.

A comprehensive analysis of India’s electric vehicle battery supply chain: barriers and solutions

This study investigates challenges and solutions for India’s battery supply chain in the growing electric vehicle (EV) market. Key obstacles include raw material dependency, supply chain complexity, production costs, environmental impacts, rapid technological changes, and skilled workforce shortages. Methods involve reviewing current supply chains, evaluating government policies like the FAME and PLI schemes, and analyzing strategic partnerships with countries rich in lithium, cobalt, and nickel. Results show heavy reliance on imports exposes India to geopolitical risks and price volatility, while a fragmented supply chain hinders coordination and quality control. Establishing gigafactories is crucial but requires substantial investment. The study recommends enhancing domestic production, securing raw materials through exploration and international partnerships, investing in R&D for advanced technologies and alternative chemistries, and developing a skilled workforce through specialized training. Implementing these strategies will ensure a robust and sustainable battery supply chain, supporting EV market growth and positioning India as a leader in electric mobility. Further research should focus on long-term environmental impacts and the economic implications of a localized supply chain.

North America’s Potential for an Environmentally Sustainable Nickel, Manganese, and Cobalt Battery Value Chain

The Detroit Big Three General Motors (GMs), Ford, and Stellantis predict that electric vehicle (EV) sales will comprise 40–50% of the annual vehicle sales by 2030. Among the key components of LIBs, the LiNixMnyCo1−x−yO2 cathode, which comprises nickel, manganese, and cobalt (NMC) in various stoichiometric ratios, is widely used in EV batteries. This review reveals NMC cathodes from laboratory research. Furthermore, this study examines the environmental effect of NMC cathode production for EV batteries (including coating technologies), encompassing aspects such as energy consumption, water usage, and air emissions. Although gaps persist in NMC cathode environmental assessments (NMC111, NMC532, NMC622, and NMC811), limited life cycle assessments “(LCA)” have been conducted. Most available data originate from Asia (primarily China), accounting for 85% of the production of EV LIB cathode materials. The concept of battery passports for data collection on LIB components has been proposed to facilitate material traceability as a system for ensuring a sustainable supply chain for critical minerals. The automotive industry’s shift to electrification necessitates a sustainable supply chain from mine to vehicle end-of-life. As the critical mineral supply moves from Asia to North America, environmentally friendly industrial methods must be studied to provide this supply chain direction.

Design and Analysis of a Novel Wireless Electric Vehicle Battery Charging System Using Dual T‐Type Converter With High Efficiency and Bidirectional Capability

Design and Analysis of a Novel Wireless Electric Vehicle Battery Charging System Using Dual T‐Type Converter With High Efficiency and Bidirectional Capability

Critical Success Factors for Building Resilience in Circular Supply Chains of Electric Vehicle Batteries: Evidence from an Emerging Country

Given the global expansion of electric vehicles (EVs), decision makers in developing and emerging countries must address important challenges across EV battery supply chains (EVBSCs) toward circularity. For example, batteries usually make up about 40% of an EVs’ value, and the race to achieve net zero emissions will further underscore the critical need for vital minerals and metals, such as lithium, cobalt, and graphite, necessary to make batteries. Stakeholders routinely question the resilience of circular (C) EVBSCs worldwide, from mining valuable materials to manufacturing the batteries necessary to support the widespread deployment of EVs. Identifying and investigating critical success factors (CSFs) of any system is a necessary step in achieving its targets. Little research, however, has been performed to investigate the CSFs for building resilience in EVBSCs, particularly those focused on building a circular supply chain. The goal of this research is, therefore, to systematically scrutinize the CSFs of resilient C-EVBSCs in Türkiye. To this end, a decision framework applying inter-valued neutrosophic ISM-MICMAC is proposed. Based on expert opinions, an application of the decision framework finds that effective government policies, directives, and incentives and well-established dynamic capabilities, are key driving CSFs to building resilience in a C-EVBSC.

Review of Lithium as a Strategic Resource for Electric Vehicle Battery Production: Availability, Extraction, and Future Prospects

This article presents a comprehensive review of lithium as a strategic resource, specifically in the production of batteries for electric vehicles. This study examines global lithium reserves, extraction sources, purification processes, and emerging technologies such as direct lithium extraction methods. This paper also explores the environmental and social impacts of lithium extraction, emphasizing the need for sustainable and ethical practices within the supply chain. As electric vehicles are projected to account for over 60% of new car sales by 2030, the demand for high-performance batteries will persist, with lithium playing a key role in this transition, even with the development of alternatives to lithium-ion batteries, such as sodium and ammonium-based technologies. However, there is an urgent need for technological advancements to reduce the environmental impact of lithium production and lithium-ion battery manufacturing. Additionally, ensuring the safety of LiBs during both use and recycling stages is critical to sustainable EV adoption. This study concludes that advancements in battery recycling and the development of new technologies are essential to improving safety, reducing costs, and minimizing environmental impacts, thereby securing a sustainable lithium supply and supporting the future of electric mobility.

Predicting the most economical option of managing electric vehicle battery at the end of its serviceable life

It is predicted that millions of electric vehicle batteries (EVBs) will retire in the next decade. With the global Electric Vehicles (EVs) market share increasing to over 40 million by 2030, the replacement of unwanted EVBs will become more abundant and may end up in landfills. These discarded EVBs can become a threat to the environment and to human health if they are not treated, disposed of, and recycled appropriately. Numerous end-of-life (EOL) cost estimation methods that have been developed are mainly for evaluating fossil fuel vehicle components. Research within the scope of EOL of EVBs however, is slow in progress, therefore, this work focuses on developing a cost estimation method for predicting EOL costs of EVBs by adopting a circular economy approach. This proposed method is validated through a case study using two Nissan's EV battery packs. The significance of this study may provide valuable information which could, indirectly lead to the redesign of EVBs for better business circular economy and less waste pollution for future EVs. The main results show that the potential economic benefit that generated by reusing/repurposing EOL EVBs is 92% more profitable than recycling and 52% more profitable than remanufacturing. This study concludes that reuse/repurpose becomes the most profitable process when EVBs reach the end of their serviceable life. Remanufacturing method can also be economically viable if it considers multi-cell replacement of an EVB.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Intelligent Robust Controllers Applied to an Auxiliary Energy System for Electric Vehicles

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices.

Regional Analysis and Evaluation Method for Assessing Potential for Installation of Renewable Energy and Electric Vehicles

Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In addition, this is required in the time-series analysis to provide a detailed resolution. In this study, we conducted a time-series analysis in Japan to evaluate suitable areas for the combined use of RE and EVs. The results showed the surplus RE areas and shortage RE urban areas. The time-series analysis has quantitatively shown that it is not enough to charge EV batteries using surplus RE. Moreover, a ranking methodology was developed for the evaluation based on electric demand and vehicle numbers. This enables the government’s prioritization of prefectures and the prefectures’ prioritization of municipalities according to their policies.

The Complex and Spatially Heterogeneous Nature of Degradation in Heavily Cycled Li-ion Cells

Abstract As service lifetimes of electric vehicle (EV) and grid storage batteries continually improve, it has become increasingly important to understand how cells perform after extensive cycling. The multifaceted nature of degradation in Li-ion cells can lead to complex behavior that may be difficult for battery management systems or operators to model. Accurate characterization of heavily cycled cells is critical for developing accurate models, especially for cycle-intensive applications like second-life grid storage or vehicle-to-grid charging. In this study, we use operando synchrotron X-ray diffraction (SR-XRD) to characterize a commercially manufactured polycrystalline NMC622 pouch cell that was cycled for more than 2.5 years. Using spatially resolved synchrotron XRD, the complex kinetics and spatially heterogeneous behavior of such cells can be mapped and characterized under both near-equilibrium and non-equilibrium conditions. The resulting data is complex and multifaceted, requiring a different approach to analysis and modelling than what has been used in the literature. To show how material selection can impact the extent of degradation, we compare the results from polycrystalline NMC622 cells to an extensively cycled single-crystal NMC532 cell with over 20,000 cycles – equivalent to a total EV traveled distance of approximately 8 million km (5.5 million miles) over six years.

Supply Chain Management for Electric Vehicles: Challenges, Opportunities, and the Path to Scalability

Abstract—As electric vehicles (EVs) gain momentum as a solution to reduce greenhouse gas emissions and fossil fuel dependence, their supply chains become increasingly complex, particularly for lithium-ion batteries (LIBs), which are crucial for EV performance, safety, and longevity. This paper reviews existing literature on several key aspects of the EV supply chain, focusing on thermal management systems (TMS), lifecycle environmental assessments, closed- loop supply chains (CLSCs), and the impacts of global disruptions like the COVID-19 pandemic. Research by Murugan et al. (2022) underscores the importance of improving TMS for LIBs, noting that experimental validation remains sparse despite advances in numerical modeling. In addition, Rüdisüli et al. (2022) highlight the environmental benefits and limitations of different mobility technologies, emphasizing the need for advancements in seasonal energy storage systems. Gu et al. (2018) discuss the challenges of establishing profitable CLSCs for EV batteries, where reuse improves sustainability, but recycling faces economic hurdles. Finally, the analysis of Wen et al. (2021) on the COVID-19 pandemic’s impact shows significant disruptions to the Chinese EV market and raises concerns about the global EV supply chain’s resilience. This review identifies gaps in the literature, such as the need for real-world validation of models, exploration of alternative battery chemistries, and improved supply chain governance, all of which are essential for advancing the EV industry toward sustainability. Keywords—Electric Vehicles, Supply Chain Management, Lithium-ion Batteries, Thermal Management Systems, Closed-Loop Supply Chains, Sustainability, Greenhouse Gas Emissions, Lifecycle Assessment, Resource Dependencies, Circular Economy.

Novel Droop-Based Techniques for Dynamic Performance Improvement in a Linear Active Disturbance Rejection Controlled-Dual Active Bridge for Fast Battery Charging of Electric Vehicles

Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs.

Prediction of the Resource of the Power Plant Hybrid Vehicle

This article substantiates the possibility of solving a difficult task to offer a reliable estimate of the residual resource of the power plant of a hybrid car, which is exposed during operation to the influence of a complex set of external factors. This task is solved by monitoring the time change of diagnostic parameters and controlling the load–speed mode of the hybrid power plant, as well as using physical and chemical processes that cause the degradation of the traction battery. Methodological principles for assessing the resource of a hybrid power plant of a car have been developed, which are based on the disclosure of cause-and-effect relationships between realized and nominal quality indicators, operating conditions, and indicators of technical condition. The choice of rational solutions for the operation of a hybrid car is given, and the external conditions are determined, under which the residual life of the hybrid power plant will change. The scientific result of the study is a theoretical generalization of the scientific provisions of forecasting the resource of the hybrid power plant of a car. This ensures the efficient use of hybrid vehicles and reduces maintenance costs.

A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions

Accurate estimation of the state of charge (SOC) is pivotal for ensuring the reliable operation of lithium-ion batteries in electric vehicles, particularly under complex real-world conditions. This paper introduces a novel SOC estimation method named Multidimensional Elemental Space Mapping Architecture (MESMA), which integrates the battery equivalent circuit model with a data-driven approach to tackle adaptability under intricate conditions and high-dimensional input data correlation. Initially, a least squares method incorporating a forgetting factor is employed to identify the battery circuit model parameters and analyze their correlation with SOC. Subsequently, a feature fusion algorithm is introduced to discern spatial variables highly correlated with SOC by leveraging both original voltage and current data and circuit model parameters as inputs to a Convolutional Neural Network (CNN) model, and an eight-input and one-output spatially mapped feature CNN model is established. Finally, the XGBoost model is harnessed for battery SOC estimation. The effectiveness and adaptability of MESMA are validated using experimental data from both public single condition and mixed condition datasets, including FUDS, Mixed-1, etc. The results unequivocally demonstrate that MESMA accurately estimates the SOC of lithium-ion batteries under varying operating conditions. Furthermore, it exhibits remarkable adaptability and stability across different temperatures, effectively capturing SOC trends.

Conceptual model for extending electric vehicle battery lifetime

To maximise the resource efficiency of electric vehicle lithium-ion batteries (LIBs), their lifetimes can be extended through cascading second- and third-life applications. Using expert input, this study establishes a conceptual model for understanding these applications’ state of health (SOH) thresholds and user requirements. Using a qualitative methodology, including focus group discussions with multistakeholder experts and policy analysis of the European battery regulatory landscape, we propose extending LIB use. Our model outlines potential second- and third-life applications aiming to maximise battery value retention. The findings highlight gaps in current European Union regulations that inadequately support battery-repurposing strategies. The conceptual model with an SOH threshold and key performance indicators serves as a foundation for researchers and industries to explore cascading battery applications, foster long-term resource efficiency and contribute to the circular economy by extending LIB lifespans through repurposing initiatives.