Electric Vehicles Research Articles

Multilayer Composite Electrodes for Simultaneously Improved Mechanical and Electrochemical Performance.

Structural batteries offer a transformative approach to integrate energy storage directly into the frameworks of electric vehicles and aircrafts, enabling multifunctional construction. This study presents a nacre-inspired multilayer composite electrode fabricated via the cold sintering process (CSP), achieving a balance of enhanced electrochemical performance and mechanical robustness. The composite electrode combines active electrode materials with a ductile conducting polymer-carbon-mixture phase in a layered architecture. The resulting electrodes exhibit a material toughness of 0.635 MJ m-3, approximately 9 times greater than conventional single-layer electrodes, alongside maintained ultimate strength. Full-field strain mapping using digital image correlation (DIC) reveals the underlying strengthening mechanisms. The influence of soft-layer thickness variations on both mechanical and electrochemical properties was also systematically analyzed to achieve optimal performance. Notably, the multilayer composite electrode delivered a reversible specific capacity of 177 mAh g-1 and an areal capacity of 22 mAh cm-2 at a high mass loading of 136 mg cm-2, significantly outperforming commercial cathodes (e.g., 3 to 4.5 mAh/cm2).

Integration of electric vehicles in electricity-carbon market toward eco-transport futures

Integration of electric vehicles in electricity-carbon market toward eco-transport futures

Systemic Multi-Objective Optimization of Induction Motor-Driven Electromechanical Systems

The optimization of electromechanical systems, such as those involving induction motors and gearboxes, is crucial for improving energy efficiency, system performance, and reliability in industrial applications. This paper presents an advanced methodology for optimizing the energy efficiency of electromechanical systems, integrating both mechanical and electrical subsystems to minimize the system's overall weight, energy losses, and transient response time. The optimization problem is approached holistically, considering the interdependence of various system parameters and applying multi-objective optimization techniques to address conflicting objectives. The analysis focuses on optimizing the gearbox speed ratio to minimize the relative weight of the motor-gearbox system while maintaining operational efficiency. A systemic approach, utilizing convex surrogate modeling and multi-stage gearboxes, is proposed to improve the scalability of the solution. The results demonstrate the existence of optimal gearbox speed ratios for various motor sizes and configurations, offering insights into the best design choices for minimizing system weight and optimizing performance. These findings apply to a range of real-world systems, including electric vehicles and industrial machinery, where minimizing weight and optimizing energy efficiency are critical for improving overall system performance and reducing operational costs.

How can we improve entrepreneurial dynamics in electric vehicle manufacturing for a sustainable future: insights using a deep learning-based hybrid PLS-SEM-ANN approach

How can we improve entrepreneurial dynamics in electric vehicle manufacturing for a sustainable future: insights using a deep learning-based hybrid PLS-SEM-ANN approach

Regression algorithm based residual range prediction and validation on EV travel data

ABSTRACT Range anxiety is one of the daunting facts that holds back customers from purchasing electric vehicles (EVs). The range of an EV depends on many internal and external parameters that cause large deviations in the energy left in the battery. taxi and bus fleets also decide on a fixed distance of travel after which they recharge the battery so as to avoid unwanted last-minute surprises. In a country like India, range prediction methods have to be extensively worked out due to inadequate charging infrastructure and vivid range of climatic and terrain conditions. This paper presents residual range prediction methods implemented in two different models of EVs based on real travel data on Indian roads. The data collected are from two compact Sports Utility Vehicles (SUV) in the Indian automobile sector, namely MGZSEV and TATA Nexon EV. The travel data of MGZSEV was collected from the mobile application whereas the data collected from Tata Nexon EV was from the dashboard display of the vehicle. Two different approaches were implemented, first being the conventional vehicle dynamics based mathematical method and second using Machine Learning (ML) based algorithms. The analysis and plots are discussed in detail and interpreted.

Charging Scheduling of Electric Vehicles Considering Uncertain Arrival Times and Time-of-Use Price

To advance sustainable transportation solutions, this work investigates an electric vehicle charging scheduling problem under the uncertainty of vehicle arrival times. Given a set of appointed electric vehicles, the objective of the considered problem is to explore charging strategies that minimize the total charging cost for the charging station. To address this problem, this work first establishes a mixed-integer programming model. Then, an enhanced sample average approximation approach alongside two versions of distribution-free approaches are applied to solve the studied problem. Additionally, this study introduces a BP neural network-enhanced distribution-free approach to efficiently resolve the problem. Finally, numerical experiments are conducted to demonstrate the effectiveness of the proposed approaches.

Multi-modal framework for battery state of health evaluation using open-source electric vehicle data

Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation. Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems. Furthermore, we make the field data of these electric vehicles publicly available aiming to promote further research on the development of effective and reliable battery management systems for real-world vehicles.

Optimization of power loss and thermal performance in electric vehicle propulsion: a comparative analysis of Si, SiC, and GaN MOSFET power converters for EESM-based EVs

ABSTRACT This research delves into power loss and thermal dynamics in Si, SiC, and GaN inverters and two-quadrant DC-DC converters for Electric Vehicle (EV) applications with Electrically Excited Synchronous Motors (EESM). Utilizing the Cauer thermal model for dissipating junction temperature through heatsinks, the research unveils semiconductor material performance intricacies. The Si-based inverter exhibits the highest total power loss at 355.49 W, followed by SiC at 164.28 W, and GaN at 139.21 W. The efficiencies of Si, SiC, and GaN-based inverters are calculated as 94.72%, 97.49%, and 97.86%, respectively. Among them, the GaN-based inverter shows the highest efficiency, which is 3.14% higher than that of the Si-based inverter. GaN emerges as the energy-efficient choice, showcasing superior conduction and switching losses for EESM-based EV propulsion. Integration of heatsinks reduces Si average junction temperatures from 199.69 °C to 55.63 °C, SiC from 93.9 °C to 45.97 °C, and GaN from 84.13 °C to 38.78 °C. Enhanced forced cooling and advanced thermal interface materials highlights the critical role of robust thermal management in EESM-based EVs. GaN emerges as the prime candidate for EESM-based EV inverters and DC-DC converters. This research underscores meticulous semiconductor material selection and efficient cooling strategies for optimal performance and reliability.

Review on Lithium-Ion Battery Heat Dissipation Based on Microchannel–PCM Coupling Technology

Lithium-ion battery heat dissipation difficulties seriously affect the efficient and stable operation of electronic devices and electric vehicles. Faced with the increasing heat dissipation demand, traditional liquid cooling systems cannot ensure the quality of heat dissipation and are seriously limited in the ability to provide uniform temperature adjustments. Combining microchannels with PCMs is beneficial. For the hybrid system here described, the maximum and average values of the temperature field were 10.35 K and 1 K, respectively, surpassing those of a liquid cooling system. By introducing suitable PCMs, the maximum value could be reduced by 5.6 K (under a 2C discharge rate) and by 16.2 K (under a 3C discharge rate). This article briefly introduces the development status and main problems of the technology combining microchannels and PCMs in BTMSs, then reviews the research progress for lithium-ion battery heat dissipation achieved by using the technology coupling microchannels and PCMs, analyzes its performance advantages, and finally prospects the future development direction of the microchannel–PCM coupling technology.

A Review of Pnictogenides for Next-Generation Anode Materials for Sodium-Ion Batteries

With the growing market of secondary batteries for electric vehicles (EVs) and grid-scale energy storage systems (ESS), driven by environmental challenges, the commercialization of sodium-ion batteries (SIBs) has emerged to address the high price of lithium resources used in lithium-ion batteries (LIBs). However, achieving competitive energy densities of SIBs to LIBs remains challenging due to the absence of high-capacity anodes in SIBs such as the group-14 elements, Si or Ge, which are highly abundant in LIBs. This review presents potential candidates in metal pnictogenides as promising anode materials for SIBs to overcome the energy density bottleneck. The sodium-ion storage mechanisms and electrochemical performance across various compositions and intrinsic physical and chemical properties of pnictogenide have been summarized. By correlating these properties, strategic frameworks for designing advanced anode materials for next-generation SIBs were suggested. The trade-off relation in pnictogenides between the high specific capacities and the failure mechanism due to large volume expansion has been considered in this paper to address the current issues. This review covers several emerging strategies focused on improving both high reversible capacity and cycle stability.

Dual-Stage Energy Recovery from Internal Combustion Engines

Waste heat recovery is one of the most investigated solutions for increasing the efficiency of powertrains in the transportation sector. A major portion of thermal energy is wasted via exhaust gases. Almost one third of fuel energy is lost, and its recovery as propulsion energy is a promising goal. Moreover, this enables the increased electrification or hybridization of powertrains, assuming the energy recovered is converted into electrical form and used to fulfill different vehicles’ needs. The present study focuses on a dual-stage energy recovery system designed to enhance the efficiency of internal combustion engines (ICEs) in heavy-duty vehicles (HDVs). The system combines a turbocompound unit for direct heat recovery (DHR) and an organic Rankine cycle (ORC) for indirect heat recovery (IHR). These technologies aim to exploit waste heat from exhaust gases, converting it into electrical energy. In this regard, electrical energy can be stored in a battery for it to be available for the energy needs of powertrains that use hybrid propulsion and for driving pumps and compressors on board, following recent technologies of auxiliaries on demand. The proposed setup was modeled and analyzed under off-design conditions to evaluate energy recovery potential and engine performance impacts. From this point of view, in fact, any device that operates on exhaust gas introduces a pressure loss, increasing engine backpressure, whose effect is an increase in specific fuel consumption. An estimate of this negative effect is presented in this paper based on experimental data measured in a F1C IVECO™ engine. An average net recovery of 5–6% of engine power has been demonstrated, with an important prevalence of the turbocompound with respect to the ORC section. The results demonstrate the viability of integrating DHR and IHR stages, with implications for advancing sustainable transportation technologies.

Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm

In contemporary transportation systems, the imperatives of route planning and optimization have become increasingly critical due to vehicles’ burgeoning number and complexity. This includes various vehicle types, such as electric and autonomous vehicles, each with specific needs. Additionally, varying speeds and operational requirements further complicate the process, demanding more sophisticated planning solutions. These systems frequently confront myriad challenges, including traffic congestion, intricate routes, and substantial energy consumption, which collectively undermine transportation efficiency, escalate energy usage, and contribute to environmental pollution. Hence, strategically planning and optimizing routes within complex traffic milieus are paramount to enhancing transportation efficacy and achieving low-carbon and environmentally sustainable objectives. This article proposes a vehicle path low-carbon planning model, Adaptive Cooperative Graph Neural Network (ACGNN), predicated on an adaptive multi-strategy ant colony optimization algorithm, addressing the vehicle path low-carbon planning conundrum. The proposed framework initially employs graph data from road networks and historical trajectories as model inputs, generating high-quality graph data through subgraph screening. Subsequently, a graph neural network (GNN) is utilized to optimize nodes and edges computationally. At the same time, the global search capability of the model is augmented via an ant colony optimization algorithm to ascertain the final optimized path. Experimental results demonstrate that ACGNN yields significant path planning outcomes on both public and custom-built datasets, surpassing the traditional Dijkstra’s shortest path algorithm, random graph network (RGN), and conventional GNN methodologies. Moreover, comparative analyses of various optimization methods on the custom-built dataset reveal that the ant colony optimization algorithm markedly outperforms the simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO). The method offers an innovative technical approach to vehicle path planning and is instrumental in advancing low-carbon and environmentally sustainable goals while enhancing transportation efficiency.

Mechanical degradation on defective lithium-ion batteries

Abstract Unavoidable minor electric vehicle collisions can cause defects and deformations in lithium-ion batteries (LIBs). The safety of such defective batteries is crucial for ensuring the overall safety and stability of battery systems. However, the usability of a defective battery and the mechanisms underlying the damage remain unclear. In this study, we focus on the mechanical performance of defective batteries, safety thresholds, and the associated failure mechanisms. Initially, two typical defective cells are prepared by quasi-static loading using different indenters. The mechanical responses of these cells are then assessed via quasi-static flat compression. Lastly, a mechanical failure criterion for the battery is proposed based on the thickness of the separator. The effects of defect area and defect angle on the performance of defective cells are also discussed. These results provide valuable insights for the safety assessment and management of defective lithium-ion batteries.

Method for calculating the range of an electric motorboat using solar energy

Relevance. The need to shift the focus to renewable energy sources and increase the usage of the electric vehicles. This gives us the access to the areas where conventional combustion engines are forbidden. The goal is not only reducing CO2 emissions, but also it is necessary to increase the autonomy of vehicles and their independence from infrastructure. Aim. To calculate the power generated by photovoltaic panels based on the insolation of the area of Tomsk, Russia, to define the efficiency of electrically driven research boat powered by the solar energy. Subject. An electrically driven motorboat, built by a student team according to the "Project Activity" training program at Moscow Polytechnical University. Methodology. Calculation using empirically and experimental data and the data from the open source. Results. We have defined the method for calculating the running time of the electrically driven solar powered boat. We obtained as well an approximate amount of the electrical power generated by the solar panels for the area of Tomsk in summer. As a result, we calculated the boat running time for a summer day at a speed of 7 km/h. This method can be used for the calculation of the energy balance of infrastructure-independent vessels, as well as floating autonomous platforms for research, mining, and transportation at restricted areas.

Simulation and Experimental Study on the Oil Circulation Rate (OCR) of R290 Electrical Vehicle Compressors

This paper establishes a simulation model for the performance of an R290 variable frequency compressor in automotive air conditioning and sets up a compressor performance testing system. It investigates the effects of different evaporation temperatures, condensation temperatures, compressor speeds, and pressure ratios on the oil circulation rate (OCR), as well as the impact of various oil circulation rates on the performance of the R290 compressor. As the comparison between simulation and experimental data shows, compressor performance predictions from the simulation model align with experimental results when the OCR is not taken into consideration. Experimental results indicate that the OCR increases with a rising evaporation temperature, decreases with a lowering condensation temperature, and increases with higher compressor speeds. The simulation model shows a minor deviation when predicting volumetric efficiency, while errors are larger when predicting isentropic efficiency and the discharge temperature. Isentropic efficiency and the discharge temperature show a notable impact from the OCR. Additionally, for system cooling capacity, power, and COP predictions, when the OCR is within the range of 2~10%, the accuracy of the simulation model proves satisfactory, with deviations within 5%.

Powertrain configuration design for two mode power split hybrid electric vehicle

Hybrid transmissions have attracted great attention in the automotive industry due to their energy-saving, low-emission properties, and have become a focus of research and development. This paper presents a new method to design the configuration of two mode power split hybrid transmission using the combination of the simple planetary gear trains (PGT). For this purpose, the hybrid transmission structure is divided into two substructures, which achieve different operation modes respectively. Then the output of one substructure is reconnected with the components of another substructure to obtain the complete hybrid transmission configuration. In addition, by adding additional shifting elements, the hybrid configurations can also realize the fixed gear mode as a traditional automatic transmission. As a result, 38 hybrid transmissions are obtained, and the kinematic analysis of a new configuration is performed for the subsequent optimization. Furthermore, using this decomposition and recombination method, the hybrid transmissions with multiple PGTs and different operation modes can be synthesized by changing the predefined modes.

Efficient Recycling Processes for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are an indispensable power source for electric vehicles, portable electronics, and renewable energy storage systems due to their high energy density and long cycle life. However, the exponential growth in production and usage has necessitated highly effective recycling of end-of-life LIBs to recover valuable resources and minimize the environmental impact. Pyrometallurgical and hydrometallurgical processes are the most common recycling methods but pose considerable difficulties. The energy-intensive pyrometallurgical recycling process results in the loss of critical materials such as lithium and suffers from substantial emissions and high costs. Solvent extraction, a hydrometallurgical method, offers energy-efficient recovery for lithium, cobalt, and nickel but requires hazardous chemicals and careful waste management. Direct recycling is an alternative to traditional methods as it preserves the cathode active material (CAM) structure for quicker and cheaper regeneration. It also offers environmental advantages of lower energy intensity and chemical use. Hybrid pathways, combining hydrometallurgical and direct recycling methods, provide a cost-effective, scalable solution for LIB recycling, maximizing material recovery with minimal waste and environmental risk. The success of recycling methods depends on factors such as battery chemistry, the scalability of recovery processes, and the cost-effectiveness of waste material recovery. Though pyrometallurgical and hydrometallurgical processes have secured their position in LIB recycling, research is proceeding toward newer approaches, such as direct and hybrid methods. These alternatives are more efficient both environmentally and in terms of cost with a broader perspective into the future. In this review, we describe the current state of direct recycling as an alternative to traditional pyrometallurgical and hydrometallurgical methods for recuperating these critical materials, particularly lithium. We also highlight some significant advancements that make these objectives possible. As research progresses, direct recycling and its variations hold great potential to reshape the way LIBs are recycled, providing a sustainable pathway for battery material recovery and reuse.

Empowering the shift: Understanding influential factors in electric vehicle switching behavior

With rising concerns over environmental pollution and energy scarcity, electric vehicles (EVs) have attracted increasing attention as an eco-friendly alternative. Yet, despite societal and governmental support, EVs still face lower market acceptance compared to traditional fuel vehicles. This research examines the primary determinants influencing consumers’ propensity to switch to electric vehicles, drawing on the Theory of Planned Behavior (TPB) and customer experience concepts. Based on a survey of 425 participants, we explore how factors such as perceived behavioral control, attitude, and driving experience shape consumers’ intention to switch to EVs. Findings indicate that policy support indirectly shapes consumer attitudes, while EV-related services and product innovations influence the switching decision through enhanced driving experiences. Notably, subjective norms show no significant effect on consumer willingness to switch. These results offer essential direction for policymakers and marketers to foster electric vehicle uptake and contribute to sustainable transportation research.

Design and Optimization of Battery Thermal Management Systems in Electric Vehicles Using Advanced Simulation Techniques

Design and Optimization of Battery Thermal Management Systems in Electric Vehicles Using Advanced Simulation Techniques

Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management

Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.