ABSTRACT Advanced lithium-ion batteries having high energy capacity and density have reduced the issue of range anxiety among electric vehicle users to a great extent. However, fast charging of these batteries is still a challenge due to significant surface temperature rise, accelerated aging, and dendrite formation. Multistage-constant current (MS-CC) charging offers a solution to these fast-charging issues. In this paper, a novel Modified Grey Relational Analysis-based Taguchi Optimization Method (MGRA-TOM) for a health-aware MS-CC charging strategy has been proposed to balance rapid charging and the battery lifespan. Near-optimal charging current for each stage has been identified using a Taguchi-based iterative optimization. The proposed MS-CC has been experimentally tested on an IFR18650 lithium-ion battery of 2 Ah nominal capacity. The results indicate that the battery takes 19.35% less time with a minutely higher temperature of 1.10°C to get fully charged with the proposed charging as compared to the traditional constant current constant voltage (CC-CV) charging.

ABSTRACT This study introduces a multi-level Energy Management Strategy (EMS) designed for Hybrid Electric Vehicles (HEV) that considers multiple uncertainties. In the upper-level strategy, an extended Long Short-Term Memory (xLSTM) neural network algorithm is utilized for short-term vehicle speed prediction. Simultaneously, within the prediction horizon, the optimal control sequence for the engine is then determined using the Dynamic Programming (DP) algorithm, which also generates a State of Charge (SOC) trajectory. In the lower-level strategy, the SOC trajectory from the upper-level strategy serves as a reference, and a tube-based Model Predictive Control (tube-MPC) approach is utilized to address the reference trajectory tracking problem under multiple uncertainties. Simulation results demonstrate that the xLSTM-based speed prediction model improves accuracy and reduces compute time compared to the Long Short-Term Memory (LSTM) and transformer speed prediction model; the proposed strategy improves fuel economy by 11.65% over the rule-based strategy and improves fuel economy by 5.25% over the latest Model Predictive Control (MPC) strategy, with a fuel consumption of 4.641 L/100 km, achieving 92.67% fuel economy of the DP strategy. Furthermore, it maintains over 90% of the DP strategy’s fuel efficiency across various driving conditions, confirming its robustness and adaptability.

Electric vehicles have recently seen rapid innovation, decline in cost and a rise in popularity. Past a tipping point where uptake becomes self-propelling, electric vehicles could irreversibly replace internal combustion engine vehicles, as industry discontinues conventional production chains. Here we provide evidence that this tipping point has occurred or lies within the next few years in lead markets of the European Union and China, and potentially the United States, which could spill out into peripheral vehicle markets across the rest of the world. The historical evidence shows a sudden decline in conventional vehicle sales starting around 2019 concurrent to a rapid rise in sales of electric vehicles. Critically, we observe a loss of resilience of the incumbent technology consistent with the approach to a tipping point. We use simulations of technology evolution to identify timescales for cost-parity and policy frameworks that could accelerate the transition to largely eliminate combustion vehicles before 2050.

In inductive power transfer (IPT) charging systems for electric vehicles (EVs), shielding metals are commonly used to reduce electromagnetic field (EMF) radiation emitted by the coils. Nevertheless, these components also introduce additional common mode (CM) noise to the system and affect the electromagnetic compatibility (EMC) performance. To mitigate the impact of the CM noise, this paper investigates the asymmetric character of CM impedance of the IPT coils and proposes a distributed circuit model to reflect the stray capacitances of the IPT coils. A comprehensive analysis is conducted to determine the CM impedance and a complete CM noise model is subsequently derived for the IPT system. Based on the novel CM noise model, a balance technique is built on a symmetric compensation circuit topology, without the need for additional hardware. The balance technique is provided to ensure compliance with the CISPR 22 standard for CM noise. An 11kW IPT prototype with the LCC (Inductor-Capacitor-Capacitor) compensation network has been implemented and experiments have been conducted. At low frequency (150kHz to 5MHz), the conductive CM noise is reduced by 5dB; at high frequency (5MHz to 30MHz), is reduced by 13dB, which validates the effectiveness of the proposed balance technique.

This study presents a comparative assessment of four microgrid configurations for rural communities in Southern Italy, with Puglia as a representative case. Using a scenario-based techno-economic model combining MATLAB R2024a and Python 3.12.7 simulations, the analysis evaluates systems based on second-life electric vehicle (EV) batteries, new lithium-ion batteries, and diesel-dominated setups, focusing on economic performance, environmental impact, and renewable integration potential. The results show that storage technology selection critically shapes both cost-effectiveness and sustainability outcomes. Second-life EV batteries emerge as the most balanced option, combining affordability and environmental benefits. These systems enable renewable penetration above 90% while maintaining a levelized cost of storage (LCOS) of EUR 0.12/kWh. Over a 20-year horizon, they achieve a positive net present value (NPV), with annual diesel consumption reduced to just 3200 l, significantly cutting greenhouse gas emissions. This highlights the potential of circular economy strategies, such as battery repurposing, to support low-carbon rural energy transitions. New lithium-ion batteries offer slightly higher technical performance, but their competitiveness is limited without policy support. The LCOS rises to EUR 0.18/kWh, reducing financial attractiveness despite marginal improvements in loss of load probability and lower diesel reliance. Premium storage technologies may therefore be most suitable where reliability is paramount and subsidies are available. By contrast, the diesel-dominated scenario illustrates the economic and environmental costs of fossil dependency. It consumes nearly 28,000 L of fuel annually, produces ~90 tons of carbon dioxide (tCO2) emissions, and achieves only 48% renewable penetration, resulting in a strongly negative NPV. Overall, the findings confirm that second-life EV batteries provide a practical, sustainable, and cost-effective pathway for rural electrification in Southern Italy and comparable Mediterranean regions. Realizing their potential will require supportive policies for battery reuse, safety, and recycling infrastructure.

The growing consumption of lithium-ion batteries (LIBs) in electronic devices and electric vehicles has led to a significant increase in waste containing valuable metals such as lithium and cobalt. Recovering these metals is essential to reducing dependence on primary sources and minimizing environmental impact. In this study, the leaching of the cathode active material from discarded LIBs was evaluated using oxaline, a deep eutectic solvent (DES) composed of oxalic acid and choline chloride in a 1:1 molar ratio. The process began with the collection, discharge, washing, drying, and dismantling of the LIBs, followed by the separation of their components. Subsequently, the cathode active material was characterized, revealing a primary composition of cobalt (54.5%) and lithium (6.5%), with the presence of LiCoO2 confirmed by XRD analysis. Leaching experiments were conducted to evaluate the effects of temperature, time, and solid percentage, demonstrating that oxaline is effective for the selective leaching of lithium and cobalt. Under optimal conditions (90 °C, 1–2 wt.% cathode active material, 400 rpm), lithium underwent complete dissolution within the first hour, while cobalt achieved complete leaching by 4 h. Both metals were recovered as oxalates and separated based on differences in solubility. Oxaline proves to be an efficient and environmentally friendly alternative for the selective recovery of lithium and cobalt from LIB waste, supporting a circular economy in the management of critical metals.

The integrated optimization of charging station (CS) placement and distributed generation (DG) placement in distribution networks has been examined in this work. The aim is to improve system performance while incorporating the effects of load models and traffic congestion. Power distribution systems have become more complex due to the widespread adoption of electric vehicles (EVs) and the increasing penetration of DGs. To ensure power system stability, efficient resource utilization is essential. This paper proposes an integrated optimization model that combines DG allocation and CS placement using a Quokka Swarm Optimization (QSO) considering both traffic congestion and dynamic load profiles for realistic modelling. The proposed approach aims to minimize system losses, enhance voltage stability and mitigate the impact of traffic congestion on system performance. Simulations are performed with modified IEEE 69-bus radial distribution system. Proposed method for DG allocation and CS placement using QSO is compared with Grey Wolf Optimization (GWO) for analysis. The results show that optimal integration of DGs and CSs leads to a 48.23% reduction in active power losses (from 224.85 kW to 116.41 kW) and an improvement in the minimum bus voltage from 0.9101 p.u. to 0.9568 p.u. (over 5.15%,) under peak load and high congestion scenarios. These findings confirm that strategic coordination of DG and CS locations significantly enhances grid performance.

Abstract - In this paper presenting power enhancement of grid-connected solar photovoltaic and wind energy (PV-WE) system integrated with an energy storage system (ESS) and electric vehicles (EVs). The research works available in the literature emphasize only on PV, PV-ESS, WE, and WE-ESS. The enhancement techniques such as Unified Power Flow Controller (UPFC), Generalized UPFC (GUPFC), and Static Var Compensator (SVC) and Artificial Intelligence (AI)- based techniques including Fuzzy Logic Controller (FLC)- UPFC, and Unified Power Quality Conditioner (UPQC)-FLC have been perceived in the existing literature for power enhancement. Further, the EVs are emerging as an integral domain of the power grid but because of the uncertainties and limitations involved in renewable energy sources (RESs) and ESS, the EVs preference towards the RES is shifted away. The EVA designed is proposed for the PV-WE-ESS-EV system to obtain the benefits such as uninterruptible power supply, effective the load demand satisfaction, and efficient utilization of the electrical power. The reduced power quality at the load side is observed as a result of varying loads in the random fashion and this issue is sorted out by using UPQC in this proposed study. From the results, it can be observed that the maximum power is achieved in the case of PV and WE systems with the help of the FLC-based maximum power point tracking (MPPT) technique. Furthermore, the artificial neural network (ANN)-based technique is utilized for the development of the MPPT algorithm which in turn is employed for the validation of the proposed technique. The outputs of both the techniques are compared to selecting the best-performing technique. A key observation from the results and analysis indicates that the power output from FLC-based MPPT is better than that of ANN-based MPPT. Key Words: Renewable Energy Sources, Energy storage systems, Unified Power Quality Conditioner, Fuzzy Logic Controller, Artificial Neural Network and Maximum Power Point Tracking.

This article proposes a practical sustainability roadmap for workshops that service electric and hybrid vehicles. The approach is structured around three main fronts: (1) properly handling hazardous waste—especially batteries and high-voltage components—through safe and traceable procedures; (2) improving the energy efficiency of the workspace with low-cost measures (lighting, compressed air, climate control, and consumption management); and (3) tracking progress through simple indicators, such as kWh per work order, proper waste segregation rate, and staff training. The roadmap translates widely recognized standards and industry best practices into reproducible routines, helping workshops enhance environmental compliance, technician safety, and energy performance—without relying on proprietary equipment or high investments.

Accurate estimation of battery state of health is essential for ensuring safety, supporting fault diagnosis, and optimizing the lifetime of electric vehicles. This study proposes a compact dual-path architecture that combines Convolutional Neural Networks with Convolutional Long Short-Term Memory (ConvLSTM) units to jointly extract spatial and temporal degradation features from charge-cycle voltage and current measurements. Residual and inter-path connections enhance gradient flow and feature fusion, while a three-channel preprocessing strategy aligns cycle lengths and isolates padded regions, improving learning stability. Operating end-to-end, the model eliminates the need for handcrafted features and does not rely on discharge data or temperature measurements, enabling practical deployment in minimally instrumented environments. The model is evaluated on the NASA battery aging dataset under two scenarios: Same-Battery Evaluation and Leave-One-Battery-Out Cross-Battery Generalization. It achieves average RMSE values of 1.26% and 2.14%, converging within 816 and 395 epochs, respectively. An ablation study demonstrates that the dual-path design, ConvLSTM units, residual shortcuts, inter-path exchange, and preprocessing pipeline each contribute to accuracy, stability, and reduced training cost. With only 4913 parameters, the architecture remains robust to variations in initial capacity, cutoff voltage, and degradation behavior. Edge deployment on an NVIDIA Jetson AGX Orin confirms real-time feasibility, achieving 2.24 ms latency, 8.24 MB memory usage, and 12.9 W active power, supporting use in resource-constrained battery management systems.

To improve recognition accuracy, convergence speed, and numerical stability in estimating the road adhesion coefficient for distributed-drive electric vehicles, a nonlinear seven-degree-of-freedom vehicle dynamics model was developed based on a modified Dugoff tire model. Using the Unscented Kalman Filter (UKF) as a foundation, a Square-Root Unscented Kalman Filter (SR-UKF) algorithm was derived through covariance-square-root processing and Singular Value Decomposition (SVD). A co-simulation platform was built with CarSim and Simulink, and a vehicle speed-following model was developed for simulation analysis. The results show that the SR-UKF algorithm for road identification consistently maintains matrix positive definiteness, ensures numerical stability, speeds up convergence, and fully utilizes measurement information. Simulations under various road conditions (high-adhesion, low-adhesion, split-μ, and opposite-μ) and driving scenarios demonstrate that, compared to the traditional UKF, the SR-UKF converges faster and provides higher estimation accuracy, enabling real-time, accurate estimation of the road adhesion coefficient across multiple scenarios. Final results confirm that the SR-UKF exhibits excellent estimation accuracy and robustness on low-adhesion surfaces, confirming its superiority under high-risk conditions. This offers a dependable basis for improving vehicle active safety.

This paper develops and applies an ex-ante methodological framework to assess the societal optimisation of eBRT innovations within the Horizon Europe eBRT2030 project, using Multi-Criteria Decision Analysis (MCDA) and the PROMETHEE method. The study evaluates 11 eBRT innovations to be deployed in five demonstration sites in Europe and one in Colombia. Twenty social parameters, including 10 risks and 10 benefits, were weighted and scored through expert and stakeholder engagement, to calculate the Societal Optimisation Index (SOI). Positive SOI values indicate that societal benefits outweigh risks, and negative values indicate the opposite, while close-to-zero values indicate socially neutral or ambiguous options requiring case-specific judgement. The results indicate that innovations such as Adaptive Fleet Scheduling and Planning, Intelligent Driver Support Systems, and IoT Monitoring Platforms provide strong societal benefits with manageable risks, while charging-related innovations are associated with social concerns. The study emphasises the importance of social impact assessment prior to implementing innovations, to enable inclusive decision-making for policymakers and transport planners and enable the development of socially optimised eBRT systems. Embedding experts’ perspectives and social criteria ensures that technological innovations are aligned with societal needs, assisting the transition towards more equitable, low-carbon transport systems.

Accurate prediction of electric vehicle charging profiles and durations is critical for adoption and optimising infrastructure. Direct current fast charging presents complex behaviours shaped by many factors. This work introduces a deep learning framework trained on 909,135 real-world sessions, capable of predicting charging profiles and durations from minimal input with uncertainty estimates. The model initiates predictions from a single point on the power and state-of-charge profile and incrementally refines them as new observations arrive, enabling real-time updates. The model generalises across vehicle types and charging scenarios. It achieves 90% accuracy in predicting charging duration from a single point, and 95% accuracy with an absolute error under one minute using six points within five minutes. This work shows that using readily available input data at charge time enables accurate prediction of charging behaviour and offers a practical, scalable solution for deployment, energy planning, and infrastructure reliability.

To address the instability issues of distributed-drive electric vehicles (DDEV) operating on roads with abrupt changes in adhesion coefficients, a lateral stability control strategy and torque distribution method based on backpropagation (BP) neural network optimization were proposed. First, an Unscented Kalman Filter (UKF) estimation algorithm incorporating real-time variation detection of adhesion coefficients was developed. To ensure rapid response and accurate estimation of current adhesion coefficients during sudden road condition changes, threshold-based real-time detection of adhesion coefficient fluctuations was introduced. Second, a hierarchical stability control strategy specifically designed for varying adhesion coefficient conditions was established. The upper-layer controller employs a Bat Algorithm (BA) optimized BP neural network, which takes the sideslip angle and yaw rate as control targets to calculate the required yaw moment for vehicle stabilization, thereby enhancing real-time computational efficiency and solution accuracy. The lower-layer controller utilizes the estimated road adhesion coefficients to implement a quadratic programming algorithm, optimizing wheel torque distribution to minimizing tire load rate. Finally, a co-simulation platform was constructed using Carsim/Simulink for validation. The results demonstrate that the proposed estimation algorithm can precisely estimate road adhesion coefficients under extreme conditions of abrupt coefficient changes. The developed stability controller significantly enhances both handling stability and driving stability of DDEV.

Continuous advancements in power conversion techniques address the growing need for efficiency and adaptability in contemporary energy applications, including e-mobility, renewable energy, and energy storage systems. This work presents a review grounded in the fundamental topologies of power converters and subsequently analyzes their modern modifications and technological advances. Traditional structures such as Buck, Boost, Ćuk, and flyback converters remain effective solutions for voltage and current regulation; however, they exhibit limitations when extremely high voltage conversion ratios are required. These constraints have motivated the emergence of more sophisticated architectures capable of overcoming such challenges. In this context, the paper provides a novel characterization and comparative analysis of quadratic and bidirectional converter topologies, emphasizing their capability to efficiently achieve both high and low conversion ratios while minimizing component stress and avoiding extreme load cycles. Quadratic converters demonstrate high performance in nonlinear systems with significant energy demands, whereas bidirectional converters enhance energy management in applications requiring bidirectional power flow, such as electric vehicles and energy storage systems.

The growing adoption of electric vehicles (EVs) requires accurate and robust State of Charge (SoC) estimation to ensure optimal battery performance, reliable driving range, and operational safety. This paper introduces KANBiLSTMAtt, a novel hybrid deep learning model that integrates the Kolmogorov-Arnold Network (KAN), Bi-directional Long Short-Term Memory (BiLSTM), and attention mechanisms to capture nonlinear interactions and long-term temporal dependencies in lithium-ion battery data. The framework incorporates Optuna for efficient hyperparameter tuning and NSGA-II for multi-objective optimization, achieving high predictive accuracy with minimal computational overhead. Validation on two distinct battery chemistries under varying temperatures, using the LG dataset and driving cycles from the CALCE dataset, demonstrates strong generalization and robustness. KANBiLSTMAtt achieves an RMSE of 0.02%, a MAE of 0.01%, and an R² of 99% for both datasets, utilizing a lightweight architecture and converging within 90s, making it highly suitable for real-time and embedded battery management systems. By combining hybrid deep learning and evolutionary optimization, the proposed model addresses limitations of traditional SoC estimation methods, offering a scalable solution for next-generation EV energy management.

Real-life Pilot of the Battery Product Passport for Electric Vehicle Batteries

The transportation sector's reliance on fossil fuels necessitates a transition towards sustainable alternatives like electric vehicles (EVs). While lithium-ion (Li-ion) batteries currently dominate the EV market, their limitations in charging time, thermal management, and resource sustainability motivate the exploration of advanced battery technologies. This research investigates the potential of graphene-enhanced batteries as a viable alternative for Li-ion batteries in EVs, focusing on enhancing charging efficiency and thermal management. A comparative analysis is conducted using a MATLAB-based simulation framework, modelling a graphene-enhanced battery system against a conventional Li-ion system based on considered reference of Tata Nexon EV Prime specifications. The simulations evaluate performance across various discharge rates (0.2 to 3C), analysing charging time, temperature profiles, charging efficiency, and temperature coefficients. The results demonstrate that graphene-enhanced batteries exhibit significantly faster charging times (22% - 27%), maintain lower operating temperatures (0.1to 5°C lower), and also offer the potential for substantial weight reduction i.e. 53% in the modelled simulation). These advancements, stemming from graphene's exceptional electrical and thermal conductivity, indicate a promising route toward the development of more efficient, safer, and higher-performing electric vehicles. This study provides quantitative insights into the benefits of graphene integration in EV battery technology, highlighting its potential to address key limitations of Li-ion batteries and contribute to a more sustainable transportation future.

The transition to electric mobility requires salespeople to go beyond technical expertise and develop advanced social–cognitive skills that shape consumer decision-making. This study examines how empathy, perspective-taking, and trust influence interactions between salespeople and potential buyers of electric vehicles (EVs). Through a systematic literature review and bibliometric analysis, we identify the key cognitive and emotional competencies that enable sales professionals to interpret customer intentions, manage uncertainty, and guide rational yet emotionally influenced purchase decisions. Findings suggest that successful EV sales rely on understanding consumer beliefs about sustainability, risk, and technology, as well as on the salesperson’s ability to align messages with these cognitive frames. Based on this analysis, we propose a competency development framework that emphasizes empathy-driven communication, adaptive reasoning, and the integration of social cognition into training strategies. This perspective contributes to the broader understanding of how social–cognitive processes affect human judgment and decision-making in the emerging electric vehicle market.

The increasing demand for reliable DC fast-charging stations in electric vehicle (EV) infrastructure necessitates efficient fault detection mechanisms to ensure operational stability and user safety. This paper will present the development of a diagnostic method for identifying open-circuit faults and short-circuit faults in DC charging stations by leveraging Total Harmonic Distortion (THD) analysis combined with a Second-Order Generalized Integrator (SOGI). The proposed approach uses the THD method to detect anomalies in the current and voltage waveforms, while the Frequency Locked Loop (FLL) serves to track the frequency of the grid and keep the SOGI tuned to it, and SOGI-FLL provides the rectifier with the capability of tracking the frequency, amplitude, voltage, and phase of the grid and monitoring these parameters of the grid. The ability to measure the THD is the kernel of the detection of faults. Detailed simulation confirms the method’s high sensitivity and robustness in detecting open/short circuit faults with minimal false positives. This technique offers a cost-effective, non-invasive diagnostic solution suitable for modern DC charging systems, contributing to improved reliability and efficiency of EV charging infrastructure.