Electric Vehicles Research Articles

Biomass-Driven Polygeneration Coupled to Power-to-X: An Energy and Economic Comparison Between On-Site Electric Vehicle Charging and Hydrogen Production

The power-to-X strategy for passenger car applications offers a viable solution for using the surplus electrical power from renewable energy sources instead of exporting it to the grid. The innovative system proposed in this study allocates surplus electrical power from a building-integrated biomass-based Combined Cooling Heating and Power (CCHP) system to on-site applications and evaluates the energetic and economic benefits. The system comprises two key components: a 50 kW electric vehicle (EV) charging station for EVs and a 50 kW alkaline electrolyzer system for on-site hydrogen production, which is later dispensed to fuel cell electric vehicles (FCEVs). The primary goal is to decrease the surplus of electricity exports while simultaneously encouraging sustainable transportation. The system’s economic viability is assessed through two scenarios of fuel (e.g., biomass) supply costs (e.g., with and without fuel market costs) and compared to the conventional approach of exporting the excess power. The key findings of this work include a substantial reduction in surplus electricity exports, with only 3.7% allocated for EV charging and 31.5% for hydrogen production. The simple payback period (SPB) is notably reduced, enhancing economic viability. Sensitivity analysis identifies the optimal hydrogen system, featuring a 120 kW electrolyzer and a 37 kg daily hydrogen demand. The results underscore the importance of prioritizing self-consumed energy over exports to the national grid, thereby supporting integrated renewable energy solutions that enhance local energy utilization and promote sustainable transportation initiatives.

Lithium from clay: Assessing the environmental impacts of extraction

The burgeoning electric vehicle (EV) sector in the United States (US) is expected to drive up the demand for lithium, a critical element for EV batteries. Lithium-rich clays in the Nevada desert emerge as a prospective US-based domestic source. This study employs Life Cycle Assessment (LCA) to examine the environmental aspects of extracting lithium from this source. Among the two evaluated routes, acid leaching was more energy-efficient (35 MJ/kg LCE (Lithium Carbonate Equivalent) than roasting (200 MJ/kg LCE), based on pilot plant data. When compared to conventional methods like spodumene-based extraction, acid leaching shows reductions across almost every category, with notable decreases in high-magnitude impacts like Global Warming (48 %), Freshwater Ecotoxicity (15 %), and Smog (69 %). Water consumption is the only category that increases, rising by 79 %. Insights from this study on upstream impacts of lithium from clay could help inform sourcing decisions downstream, in the battery and EV sector.

Social risk assessment of the transition from internal combustion engine vehicles to electric vehicles: Material supply chains in China, Japan, and South Korea

In order to reduce carbon emissions, the production of internal combustion engine vehicles (ICEVs) is being transitioned to the production of electric vehicles (EVs). However, the social unsustainability of this process needs further testing. This study explored the social risk characteristics of the transition from ICEVs to EVs based on the Social Hotspots Database (SHDB), combining with the Social Life Cycle Assessment (SLCA) and the Marginal Analysis Method (MAM). Here, we revealed the differences in social risks in the material supply chains between China, Japan, and South Korea, which may create incentives for 100 % supply concentration in a hypothetical game. In terms of social risk, common substitution trends in the iron‑aluminum sector and synergy patterns in the copper-rubber sector were observed in the three countries. As a key factor influencing social risks, cost factor may change, and cost changes due to transition were mainly influenced by production adjustments and sourcing differences, reflecting differences in production capacity trends. This study makes it possible to consider ICEVs and EVs as two endpoints for transition activity in a SLCA exercise and provides valuable insights focusing on transition, aiding in material selection for future product design and underscoring the importance of proactive consideration of social sustainability indices.

A multi-scale modeling approach for predicting and mitigating thermal runaway in electric vehicle batteries

This study presents a unique multi-scale modelling technique for electric vehicle (EV) battery thermal runaway prediction models using electrochemical, thermal, and machine learning approaches. This modelling framework has illuminated the thermal runaway’s physical process and the race between heat production and dissipation at the cell module, and pack levels.Experimental evaluation has demonstrated solid predictive performance for each component. The modified pseudo-two-dimensional (P2D) electrochemical model has achieved high voltage prediction accuracy (mean absolute error: 8.5 mV), and the 3D thermal model has captured battery module temperatures with an average error of ± 1.5 °C.The machine learning model has exhibited 96.8% accuracy, excellent classification precision, and an 18.3-minute early warning time. It was also demonstrated that the integrated multi-scale model outperformed standalone single-scale models with 15% higher prediction accuracy and 32% higher average early warning time.Based on these findings, adaptive cooling techniques, unique charge/discharge procedures, and early isolation methods were created. Simulations showed that these mitigation techniques decreased thermal runaway occurrence by 78% and severity by 93%. Despite its high computing cost, this technique might improve EV battery safety, guide battery pack design, and accelerate EV adoption, which would cut carbon emissions.

Strategic deployment of hydrogen fuel cell buses and fueling stations: Insights from fleet transition models

Establishing new hydrogen value chains is challenging, requiring economies of scale and balanced supply-demand dynamics. Municipalities can mitigate this risk through government support and deployment strategies. This study analyzes Edmonton's transition to zero-emission buses (ZEBs), focusing on hydrogen fuel cell electric vehicles (HFCEVs) and hydrogen fueling stations (HFSs). Using scenario-based modeling and S-curve models for technology diffusion, we project the adoption of battery electric vehicles (BEVs) and HFCEVs. Deploying over 1000 ZEBs by 2040 is necessary to meet Net-Zero targets, with 310–760 HFCEVs required for the municipal bus inventory. This results in an estimated hydrogen demand of 6.2–14.5 t-H2/day and a reduction of 0.4–1.0 Mt-CO2 in tailpipe emissions by 2050. We use these scenario projections to develop a phased deployment strategy, optimizing fleet operations to reduce HFS costs by 50–60% from 8 to 9 C$/kg-H2 to 3–4 C$/kg-H2. The study underscores the importance of strategic planning and infrastructure investment in realizing net-zero goals, providing a model applicable globally.

Experimental Investigation of Thermal Runaway Behaviour and Inhibition Strategies in Large-Capacity Lithium Iron Phosphate (LiFePO4) Batteries for Electric Vehicles

Large-scale lithium-ion batteries play an important role in the electric vehicle market and the thermal runaway (TR) behaviour of batteries is one of the significant threats to the passengers. In this study, we conducted a series of thermal abuse tests concerning single battery and battery box to investigate the TR behaviour of a large-capacity (310 Ah) lithium iron phosphate (LiFePO4) battery and the TR inhibition effects of different extinguishing agents. The study shows that before the decomposition of the solid electrolyte interphase (SEI) film, temperature consistency within the battery were maintained and it was advisable to use temperature detectors. With the destruction of the internal structure of the battery and the uneven temperature distribution at the subsequent battery reaction stage, a large amount of combustible gas was released when the safety valve was opened. The combustible gas fire detector is used to improve alarm reliability and provided a stable early warning response time. Based on this TR behaviour, a LiFePO4 battery box with a standard size of 1060 × 660 × 250mm was constructed. The TR inhibition effect of two different extinguishing agents (heptafluoropropane and perfluorohexanone) were investigated using a metal tube spraying method with a spraying dose of 1.8kg (spraying rate of 0.06kg/s) and a spraying pressure of 2.5MPa. The results showed that using either of these, satisfy the requirements for TR inhibition in large-capacity LiFePO4 batteries and the flames could be completely extinguished without any reignition, and the chain reaction of the surrounding batteries was effectively prevented. Moreover, heptafluoropropane was proven to have a shorter fire-extinguishing time, whereas perfluorohexanone had the advantage of inhibiting temperature rise. This study provides valuable experimental results for early warning and inhibition of TR in large-capacity LiFePO4 batteries for electric vehicles.

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.

Enhancing Chemomechanical Stability and High-Rate Performance of Nickel-Rich Cathodes for Lithium-Ion Batteries through Three-in-One Modification

Ni-rich cathode, recognized for high specific capacities and cost-effectiveness, are deemed promising candidates for high-energy Li-ion batteries. However, these cathodes display notable structural instability and experience severe strain propagation during rapid charging and extended cycling under high voltage, hindering their widespread commercialization. To tackle this chemo-mechanical instability without compromising energy and power density, we propose an efficient modification strategy involving hexavalent metal cation-induced three-in-one modification to reconstruct the nanoscale surface phase. This strategy includes uniform W-doping, integration of cation-mixed phases, and Li2WO4 nanolayers on the surface of Ni-rich cathode microspheres. W-doping strengthen the bond to oxygen, thereby enhancing structural stability and suppressing oxygen loss linked to a layered-to-rock salt phase transition during deep delithiation process. Additionally, establishing a cation-mixing domain with an optimal thickness on the cathode surface enhances Li⁺ diffusivity and alleviates particle structural degradation. Moreover, Li2WO4 nanolayers reduce electrolyte side reactions and act as a damping medium against cycling stresses. Importantly, detailed investigations into structural changes before and after modification at varying current rates were conducted to better comprehend the rate-dependent degradation mechanism. These findings yield valuable mechanistic insights into the high-rate utilization of a viable Ni-rich cathode, ensuring prolonged service life in electric vehicles.

A novel construction and evaluation framework for driving cycle of electric vehicles based on energy consumption and emission analysis

The driving cycle (DC) is essential for establishing vehicle emission standards, transportation policies, and urban planning. However, existing driving cycles demonstrate poor representativeness and excessive randomness due to the insufficient capture of driving characteristics. Therefore, a novel framework for constructing and evaluating driving cycles of electric vehicles (EVs) based on energy consumption and emissions analysis is proposed to enhance the representativeness of the constructed driving cycles. First, based on road information, an improved dual-chain Markov chain method combined with the self-organizing mapping (SOM) neural network is introduced for clustering and constructing driving cycles. Subsequently, a double-layer evaluation model oriented towards energy consumption and emissions is established through a combination of model-driven and data-driven approaches to select a representative driving cycle (RDC). Finally, comparative experiments are conducted to evaluate the feasibility and scientific validity of the proposed method in multiple dimensions. The results indicate that the driving cycle constructed in this study demonstrates excellent representativeness, with an emission error of 2.04% and a comprehensive characterization parameter (CCP) value of 0.097. This study emphasizes the necessity of incorporating reasonable constraints in the driving cycle construction. This improved representativeness provides a reliable foundation for electric vehicle design and transportation policy development.

Assessing Fire Dynamics and Suppression Techniques in Electric Vehicles at Different States of Charge: Implications for Maritime Safety

Assessing Fire Dynamics and Suppression Techniques in Electric Vehicles at Different States of Charge: Implications for Maritime Safety

Article: Green Regionalism: US Strategies to Ensure Secure and Reliable Supplies of Critical Minerals

Critical Minerals are essential constituents for numerous modern technologies from computers, and modern cars to household appliances. Most critical minerals are also key inputs in technologies that are crucial for clean energy transitions such as batteries, electric vehicles (EV), wind turbines, solar panels etc. Global trends indicate that the race for ensuring a secure and reliable supply of critical minerals is being furthered through industrial policy, domestic regulations as well as international economic law instruments. More recently these three aspects have been characterized by increasing securitization as seen through attempts to friendshore and reshore supply chains and capacities. This paper peruses the recent Critical Material Transparency and Reporting of Advanced Clean Energy (Critical Material TRACE) Bill which focuses on implementing a digital identifier system for advanced energy systems including batteries and battery components. The paper looks at the bill in connection with other initiatives aimed at creating United State (US) led critical mineral value chains. These include industrial policy measures including the Inflation Reduction Act (IRA) as well as international initiatives such as the Mineral Security Partnership (MSP) and the Indo-Pacific Economic Framework (IPEF) inter alia. In our examination, the focus is on analysing the progress made under the US-led MSP and the potential coverage and implication of the Critical Material TRACE Bill. We also examine if such steps by the US further promote green regionalism or are necessary to break the dominance of a few countries (mainly China) in leading the critical minerals supply chain.

Expansion force signal based rapid detection of early thermal runaway for pouch batteries

With the extensive application of lithium-ion batteries in electric vehicles and energy storage stations, thermal safety issues have increasingly become the focus of public attention. To alleviate the safety concerns, a qualified battery management system (BMS) must be able to detect and warn of thermal abuse before it develops into a thermal runaway (TR). For the first time, the expansion force characteristics of the pouch battery module during the early heating stage of TR under typical working conditions are analyzed, which proves that the expansion force signal can reflect the internal abnormal heating earlier than the terminal voltage and the surface temperature, and has better noise resistance. Furthermore, the electromechanical coupling model (EmCM) is improved for more accurate open-loop expansion force estimation. On this basis, an expansion force based early TR rapid detection framework is proposed with less 3 min expend in tests, in which the Kalman filter ensures the stability and convergence/divergence of the observer estimation error, and the reliability of fault judgment is ensured by the adaptive threshold designed by the model structure.

Fast and Smart State Characterization of Large-Format Lithium-Ion Batteries via Phased-Array Ultrasonic Sensing Technology.

Lithium-ion batteries (LIBs) are widely used in electric vehicles and energy storage systems, making accurate state transition monitoring a key research topic. This paper presents a characterization method for large-format LIBs based on phased-array ultrasonic technology (PAUT). A finite element model of a large-format aluminum shell lithium-ion battery is developed on the basis of ultrasonic wave propagation in multilayer porous media. Simulations and comparative analyses of phased array ultrasonic imaging are conducted for various operating conditions and abnormal gas generation. A 40 Ah ternary lithium battery (NCMB) is tested at a 0.5C charge-discharge rate, with the state of charge (SOC) and ultrasonic data extracted. The relationship between ultrasonic signals and phased array images is established through simulation and experimental comparisons. To estimate the SOC, a fully connected neural network (FCNN) model is designed and trained, achieving an error of less than 4%. Additionally, phased array imaging, which is conducted every 5 s during overcharging and overdischarging, reveals that gas bubbles form at 0.9 V and increase significantly at 0.2 V. This research provides a new method for battery state characterization.

ChatEV: Predicting electric vehicle charging demand as natural language processing

The increasing popularity of electric vehicles (EVs) in recent times has introduced considerable load conditions for urban power grids and transportation systems, which highlights the importance of accurately predicting charging demand to enhance charging efficiency. However, current forecasting methods still face challenges in effectively aligning diverse data and generating accurate predictions that can be applied to unseen scenarios. To overcome the challenges, this work introduces a novel perspective: employing large language models (LLMs) as EV charging demand predictors. First, we reformulate the prediction task into a text-to-text format, enabling seamless and effective alignment of various features within a unified language semantic space. Subsequently, we fine-tune a LLM using a meta-learning framework to adapt it specifically for EV charging prediction. Through comprehensive evaluations, it has been demonstrated that the proposed model, ChatEV, achieves outstanding performance in EV charging demand forecasting, particularly in scenarios with limited data.

Efficient Iron Loss Estimation of Interior PMSMs in Electric Vehicles: Analytical Modelling and Experimental Validation

Efficient Iron Loss Estimation of Interior PMSMs in Electric Vehicles: Analytical Modelling and Experimental Validation

General Analytical Model for Electromagnetic-Thermal Bi- Directional Coupling of IPM Motors in Electric Vehicles Considering Different Rotor Structures

General Analytical Model for Electromagnetic-Thermal Bi- Directional Coupling of IPM Motors in Electric Vehicles Considering Different Rotor Structures

Graphene functionalized nanoencapsulated composite phase change material based nanofluid for battery cooling: An experimental investigation

The graphene-nano encapsulated composite phase change material (GnePCM) based nanofluid holds great potential as a coolant for batteries in electric vehicles. The current work focuses on the synthesis and study of the cooling performance of GnePCM-based nanofluid. Few layered graphene (FLG) was synthesized via the liquid phase exfoliation method. Mini-emulsion polymerization was adopted to encapsulate composite PCM (octadecane: paraffin wax) within polystyrene shell and distribute these nanoballs across the graphene flakes of FLG to obtain GnePCM. Differential Scanning Calorimetry (DSC) was used to optimize the composition of composite PCM based on the melting range and latent heat. GnePCM was characterized by Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), DSC, Fourier Transform Infrared (FTIR) spectroscopy, and Raman Spectroscopy. Nanofluid was made by mixing GnePCM slurry with the base fluid (ethylene glycol − water mixture) and its thermo-physical properties were estimated. The analysis of thermal conductivity and specific heat capacity showed a 14.7 % and 56 % increase for the nanofluid compared to the base fluid. The optimal concentration of nanofluid for maximum stability was 10 % v/v based on zeta potential measurements. The heat transfer studies and pressure drop studies were conducted on a set of 10 cylindrical heaters, mimicking a 18,650 battery cell pack. The nanofluid could potentially achieve a maximum reduction of 5 °C in average surface temperatures of the cells as compared to the base fluid. The enhanced cooling efficiency of nanofluid could be related to the increased thermal conductivity and heat capacity of GnePCM, as well as the absorption of latent heat during its melting process. Enhancement in heat transfer parameters was found to be more prominent at lower flow rates for the nanofluids. The results reveal that the flow rate and power input play a significant role in the cooling performance of the nanofluid. Due to the increased viscosity of the nanofluid, a slight increase in the pressure drop and pumping power was observed at higher flow rates, as compared to base fluid.

Improving powertrain efficiency through torque modulation techniques in single and dual motor electric vehicles

Battery Electric Vehicles (BEVs) typically experience reduced powertrain energy efficiency under low-torque operating conditions. This issue can be mitigated by torque modulation, i.e., alternating driver torque demand between zero and an appropriate value. This paper aims to maximize the efficiency improvement of torque modulation and investigate its potential in two powertrain topologies: single and dual motor powertrains. To this end, optimal modulation strategies are proposed for both powertrains, considering overall powertrain energy losses and specific powertrain characteristics. Additionally, the adverse impact on driver comfort and powertrain durability due to the pulsating torque is examined. The results suggest that the proposed optimal modulation strategies can enhance powertrain efficiency while maintaining acceptable levels of driver comfort and powertrain durability. In addition, complementary modulation and torque distribution can be applied in the dual motor powertrain to provide further energy-saving potential and reduced impact on driver comfort.

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.

Innovative Strategies for Grid Resilience: Electric Vehicles, Load Response, and Renewable Energy Synergy in the Smart Grid Era

Innovative Strategies for Grid Resilience: Electric Vehicles, Load Response, and Renewable Energy Synergy in the Smart Grid Era