Vehicle State Research Articles

Toward just and equitable mobility: Socioeconomic and perceptual barriers for electric vehicles and charging infrastructure in the United States

Based on a large-scale public survey, we identify and quantify the significance of key factors associated with the deployment of electric vehicles and charging infrastructure. Our results indicate that individual characteristics, such as income, age, region, and single vs. multi-family housing type can significantly affect electric vehicle purchase preferences, especially those concerning overnight charging and perceptions of benefits and barriers. Moreover, our results challenge earlier findings in the literature by showing how certain elements, such as expected electric driving range, certain travel behaviors (e.g., driving distance, destination types), the most common perceived benefits (e.g., cleaner air) or barriers (e.g., reliability concerns), and preferred location for public charging seem to not vary much or at all with the socioeconomic, demographic, and geographical variables examined in this study. We conclude with the implications for policies to advance equitable vehicle electrification. Our findings underscore the importance of lower-cost models of electric vehicles, home and public charging access, charging infrastructure planning, more integrated analysis of interlinked housing and transportation needs and solutions, the availability of alternative transportation modes, and the potential role of gas stations for electric vehicles. We encourage others to build on these results and have shared our complete survey instrument as an added contribution.

Comprehensive Chassis Control Strategy Based on Coordination of Traction/Braking Distribution and Active Roll Control

A coordinated control of front/rear traction, four-wheel independent braking, and an active roll control system is proposed in this paper to improve overall vehicle cornering performance. The control algorithm is structured in a hierarchical form: the supervisory controller is used to calculate the desired yaw rate and roll angle based on the driver input and vehicle states; the upper-level controller is utilized to decide the target longitudinal tire forces, yaw moment, and anti-roll moment based on the sliding mode control method; and the control allocator is used to optimally map the virtual control input to the control commands of specific actuators. Co-simulations of MATLAB/Simulink and CarSim were conducted to verify the performance enhancement of the proposed controller. The results suggest that the proposed controller can effectively enhance the driving performance in limit cornering while maintaining lateral stability, and it reduces tire usage, as indicated by the index of tire dissipation energy when compared to control methods such as AWD, AWD-ESC, and ICC.

Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis.

Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: "Stationary Vehicle Warning-Broken-Down Vehicle" and "Dangerous Situation-Electronic Emergency Brake Lights". Volkswagen's warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.

Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes

When a ground vehicle runs at high speeds, even a slight excess in the wheel steering angle can immediately cause the vehicle to slide sideways and lose control. In this study, we propose an active safety control system designed to address emergency situations where the driver applies excessive steering input and the vehicle speed varies significantly during control. The system combines the direct yaw moment (DYM) method with a steering saturation scheme that prevents excessive driver steering input from adversely influencing the front-wheel steering. Consequently, the control system allows the DYM to focus more on other stabilization tasks and maintain tire/road friction within its workable linear range. The implementation relies on a reference steering angle and a reference vehicle state, derived from a linear vehicle model considering tire/road friction limitations. When the driver’s steering angle and the system state deviate from these reference values, the control system intervenes by applying both the steering saturation scheme and DYM method. This ensures the front-wheel steering angle and system state remain close to the reference values. The control strategy is developed using the polytopic Linear Parameter Varying (LPV) technique and Linear Matrix Inequality (LMI) to account for the changes in vehicle speed. It is further enhanced with an input saturation technique based on a high-gain approach, which improves control utilization and system response during emergency situations. The advantages of the proposed control strategy are demonstrated through simulation results.

Low-carbon optimal scheduling of park-integrated energy system based on bidirectional Stackelberg-Nash game theory

Multi-stakeholder participation is crucial in facilitating the development of park-integrated energy systems (PIES). Balancing the diverse interests of various stakeholders, each with its distinct requirements presents a notable challenge. Concurrently, the model's complexity increases due to the engagement of various stakeholders, posing challenges to its resolution through traditional methods. In this context, this paper aims to investigate an optimal scheduling model that incorporates shared energy storage (SES) system, microgrids operator (MGO), electric vehicles station (EVS), and user aggregator (UA) with multiple prosumers. To comprehensively address the interests of all stakeholders, this study introduces a tri-level optimization model. The proposed model integrates a bidirectional Stackelberg-Nash game framework, in which the SES acts as the leader, the MGO acts as the secondary leader, and the UA-EVS acts as the followers while allocating benefits based on the asymmetric Nash bargaining theory. The Stackelberg game model between MGO and UA-EVS is analyzed using the Karush-Kuhn-Tucker (KKT) condition, while the Stackelberg game model between SES and MGO is resolved using the bisection method. Meanwhile, the Nash bargaining method among users is solved using the alternating direction method of multipliers (ADMM) technique. The analysis indicates that the proposed strategy can reduce PIES's costs and carbon emissions, yielding a win-win situation for all stakeholders.

Car-following informed neural networks for real-time vehicle trajectory imputation and prediction

Vehicle trajectory information is a crucial part of improving the efficiency and the safety of the ITS. Data missing or irregular sampling in the real-world road traffic makes it hard to obtain accurate real-time vehicle trajectories. In this paper, we focus on trajectory imputation and prediction tasks with small data (magnitude set as 10 1 and 10 2 ). Limited by insufficient data, the simulation results of the existing data-driven algorithms are unsatisfactory. With car-following models integrated as prior physical information to constrain the training process, we design the car-following-informed neural network (CFINN). A multi-head self-attention layer is attached to the fully connected network layer to extract vehicle features. Different from the structure of most neural networks in regression analysis, an extra physics-based dataset is constructed in the CFINN. The loss function consists of two parts including the given trajectory's fitting error and the generated trajectory's residual error. We embed the gated recurrent unit-based encoder–decoder layer to the CFINN framework for trajectory predictions. The rationality and the superiority of our model are validated on the NGSIM dataset and the HighD dataset. Compared with baseline models in both single-vehicle and queue-typed trajectory imputation experiments, lower error can be achieved via the CFINN and coefficients of car-following models can be calibrated. According to driving regimes derived from CFINN-based trajectory prediction experiments, we discuss the impact of cut-in behaviours on the target vehicle and carry out the kinetic analysis. The novel neural network model driven by both data and physical knowledge provides technical support in vehicle status assessments and trajectory predictions.

Research on a Path Tracking Control Strategy for Autonomous Vehicles Based on State Parameter Identification

With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions.

High-fidelity simulations of boundary layer transition on an underwater axisymmetric body with stability theory

During the cruise status of an underwater high-speed vehicle, the flow around the vehicle's head typically transitions from a laminar to a turbulent state, triggering flow noise that can interfere with the normal operation of sonar. In order to accurately investigate this flow noise through numerical simulation, a high-fidelity turbulent flow field solution is essential. Common traditional turbulence numerical simulation methods, such as unsteady Reynolds-averaged simulation and large eddy simulation (LES), struggle to capture high-frequency turbulent fluctuations accurately due to their inability to directly resolve small-scale eddy structures, which results in compromising the simulation accuracy of high-frequency flow noise. To address this issue, this paper employs direct numerical simulation (DNS) to achieve high-fidelity resolution of the turbulent flow field, thereby enabling a more accurate assessment of flow noise distribution on the vehicle's surface. Meanwhile, considering significant computational resources required to solve the entire flow field in an underwater high Reynolds number environment, this study also incorporates the fixed transition modeling method and stability theory to confine the DNS computational domain to the vicinity of the transitional zone to improve simulation efficiency. Comparative analysis of flow noise monitoring results in the laminar, transitional, and turbulent zones revealed that the flow noise source in the laminar zone exhibits the lowest amplitude across all frequencies, while the flow noise source in the transitional zone features the highest amplitude, approximately 10 dB higher than that in the turbulent zone. Moreover, significant amplitudes in high-frequency components (above 30 kHz) are detected in both the transition and turbulent zones. Additionally, this study employs LES with the Smagorinsky model to simulate the flow field within the same computational domain as DNS, demonstrating the limitations of the Smagorinsky model-based LES in capturing high-frequency flow noise.

Feasibility analysis of charging stations for electric vehicles. Case study route E35, Loja-Cuenca in Ecuador

Feasibility analysis of charging stations for electric vehicles. Case study route E35, Loja-Cuenca in Ecuador

Sizing requirements of the photovoltaic charging station for small electrical vehicles

Electric vehicles (EVs) are being introduced in Rwanda and becoming attractive for different reasons. For instance, these types of vehicles can help decrease air pollution and noise emissions. In addition, it presents an alternative to combustion engines, given the increased price of fuel resources in Rwanda and around the world. This paper presents a tool tailored to optimize the design of an electrical charging station serving small-sized electric vehicles, utilizing the algorithm to assist in sizing stand-alone mopped charging stations. The developed tool is based on the toolbox EventSim from MathWorks, which permits the combination of the simulation of discrete events (such as the arrival of customers at the station) with continuous states (such as the simulation of the charging process). The required PV power was estimated by utilizing solar resources, for the location, from renewables. Ninja. The number of customers arriving at the existing oil station is normalized to estimate the energy requirements of the mopped fleet. A Poisson distribution was proposed to model the battery discharge upon arrival, and different related parameters were evaluated through a sensitivity analysis to identify their effects on the performance of photovoltaic charging station. For the testing values, the station parameters were changed by ±25% to determine the impact of key design parameters on station performance, as well as other satisfaction measures such as average waiting time and average queue length. With a 25% increase in photovoltaic panels, the blackout period decreases by 2.12%, while a 25% decrease in photovoltaic panels causes an increase of 2.18% in the blackout period. Utilizing the energy management system (EMS), the waiting time was reduced by 8%.

A review of research on tire burst and vehicle stability control.

Tire burst is an accidental occurrence that poses a serious threat to the driving stability and road safety of vehicles. Therefore, it is of great practical significance to investigate early warning systems for tire burst and develop stability and safety control measures after burst incidents. The development of an accurate model that can effectively represent the impact of tire burst on vehicle dynamics is crucial for the design of control systems and the development of stability control strategies. Most of the existing research on tire burst models is based on static tire tests, the effectiveness of these models still needs to be further verified. The main approach to studying the impact of burst tires on vehicle performance is to embed a burst tire model into a vehicle dynamics model. Understanding the impact of tire burst on vehicle performance is essential for identifying burst incidents and developing stability control strategies. The research on burst identification primarily focuses on early warning systems and estimating vehicle state parameters after burst incidents, while the current research on stability control strategies focuses on enabling vehicles to continue running safely after burst incidents through braking, active steering, and collaborative control. Currently, there is no comprehensive review of research on vehicle tire burst stability control. Therefore, this paper primarily reviews five aspects: (a) the causes and prevention of tire burst, (b) the impact of tire burst on vehicle performance, (c) burst identification, (d) stability control strategies for burst incidents, and (e) future prospects for tire burst research.

Assessment of Dynamic States of Railway Vehicles: Structural Mathematical Modeling

Introduction. The speed rise of railway transport and an increase in the loads on the axles of wheelsets necessitate the modernization of the existing fleet. Scientific studies in the field of rolling stock dynamics are aimed at taking into account the oscillatory processes that occur during the movement of railway vehicles in a traditional design. The attachment of supplementary elements was considered at the coupling level of two cars and the attachment of a third trolley in the center of gravity of the railway platform. The scientific literature has not paid enough attention to the construction of mathematical models that make it possible to assess the dynamic states of such constructive solutions. The objective of this study is to create a method for evaluating the dynamic conditions of a car. The situation is considered when an additional set of mass-inertial and elastic elements is introduced into its structure, and the general dynamic condition of the vehicle depends on the adjustment of their parameters.Materials and Methods. The basic research tool is the structural mathematical modeling, which is based on an approach where the source design scheme is a mechanical oscillatory system in the form of a solid body on elastic supports with supplementary typical elements introduced into its structure. The dynamic analogue of the calculation scheme used is the block diagram of the automatic control system, the use of which provides detailing the connections between typical elastic and mass-inertia elements.Results. A method for estimating the dynamic states of railway vehicles is proposed. It is based on the construction of mathematical models, taking into account the introduction of an additional structure of mass-inertia and elastic elements. The impact of additional parameters on the dynamic condition of the vehicle is investigated. Analytical relations have been obtained that provide reducing the dynamic loads on the major structural elastic elements when changing the corresponding parameters of a technical object. The transfer function of interpartial relations is given, which provides controlling the interaction between the coordinates of the vehicle movement under the action of two kinematic disturbances of the in-phase type.Discussion and Conclusion. The generated mathematical model provides for assessment, monitoring and control of the dynamic state of the vehicle under conditions of kinematic disturbances. The research results can be used to modernize existing vehicles and create new ones with improved dynamics.

Enhanced supervisory control scheme for hybrid microgrid operation with virtual power plants

Owing to the urgency of energy demand, an enhanced supervisory control scheme (ESCS) is proposed for hybrid microgrids (HMGs) integrating AC and DC grids. This system optimizes energy management within a virtual power plant (VPP) setup, facilitating smart charging stations for electric vehicles (EVs) and enabling vehicle-to-grid (V2G) and grid-to-vehicle (G2V) interactions. The proposed ESCS combines three sub-controllers: a sliding mode approach-based maximum power algorithm (SMA-MPA), active current detection technique (ACDT), and state of charge (SOC) regulation scheme. In this proposed approach, the SMA-MPA method is employed to extract maximum power with necessary stability confirmation. Moreover, ACDT is utilized to mitigate harmonics from nonlinear loads through the DC-AC inverter, thereby improving power quality (PQ). To enhance SOC regulation of the VPP, a detailed flow chart of appropriate converting mode selection associated with SOC controller design is proposed for smoother operation and improved dynamics. The coordination between sub-controllers is achieved by analyzing power demand and supply, DC-link voltage conditions, and SOC states of the VPP. The proposed ESCS approach enhances PQ even during PV shutdown conditions. Through software simulations and real-time Hardware-in-the-Loop (HIL-402) validation, the ESCS's superior power management, PQ, and regulatory compliance are demonstrated against conventional PQ methods. The findings exhibit excellent power management, improved PQ, and better voltage/frequency regulation in accordance with prescribed international IEEE 519 standards.

Mixed traffic capacity estimation of autonomous vehicles impact based on empirical data

This study refined the composite headway distribution model to estimate the mixed traffic capacity by utilizing extensive empirical mixed traffic data. The enhanced model integrated an automatic iterative algorithm into the stochastic process to capture the headway distributions of autonomous and human-driven vehicles in five types of car-following pair combinations. The proposed iterative algorithm automatically fitted the free-flow headway distribution and dynamically determined the threshold for distinguishing the car-following and free-flow states of autonomous and human-driven vehicles based on extensive trajectory data. Quantitative analysis was carried out to assess the impact of time period and vehicle type on the headway distributions of following vehicles in the five car-following pairs. The mixed traffic capacity was estimated using the overall composite headway distribution model, considering the effects of penetration rate, platoon intensity, and maximum platoon size of the autonomous vehicle. The findings indicated that autonomous vehicles showed more cautious and sensitive car-following behaviors compared to human-driven vehicles. Compared to the daytime, autonomous vehicles showed more cautious car-following behaviors at night. When following heavy vehicles, autonomous vehicles typically adapt conservative and stable car-following behaviors. In addition, the results also showed that the penetration rate, platoon intensity, and maximum platoon size significantly affected the capacity of mixed traffic flow. These results of this study can assist traffic management authorities in formulating rules or policies that take into consideration the growing prevalence of autonomous vehicles.

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Vehicles' perception capability of environmental situations has been enhanced in the connected environment. However, the utilization of abundant and diverse traffic information poses a challenge in the formulation of energy-efficient strategies for current connected vehicles. To address this challenge, a hierarchical intelligent energy-saving control strategy for fuel cell hybrid buses based on traffic flow prediction is proposed. Diverging from traditional approaches that rely on surrounding vehicles status information, a more macroscopic perspective is introduced by incorporating traffic flow prediction information into the formulation of energy-saving control strategies for the first time, which enhances the adaptability of strategies. At the upper layer, a multi-objective intelligent eco-driving control strategy is designed, encompassing driving safety, energy consumption costs, traffic efficiency, and ride comfort. At the lower layer, an intelligent energy management strategy is developed to reduce hydrogen consumption and maintain stable battery state of charge. Simultaneously, action variables serve as the information bridge between the upper and lower layer strategies, and comprehensive analyses and validations of strategic performance are conducted from various perspectives. The research findings indicate that the introduction of traffic flow information enhances the cognitive capabilities of the intelligent system towards the traffic environment with little impact on the convergence process. The model excels in energy efficiency, driving smoothness, and passenger comfort compared to the baseline model, while also having the opportunity to surpass the traffic efficiency of the IDM model. The developed energy management strategy demonstrates an energy-saving benefit of 92.07 % of the offline optimal benchmark, and closely approaches the theoretical optimal performance of MPC with a prediction horizon of 5s. Additionally, the proposed strategy demonstrates high computational efficiency and effectively mitigates the degradation of the vehicle lifespan, making it conducive to real-time online applications.

Segmentation Of Vehicle Delivery Test Services Using K-Means Clustering Case Study Of West Pasaman District Transportation Services

This research aims to segment services using the K-Means Clustering method in the case of Vehicle KIR Test services. Service segmentation is important to understand the time, place and type of vehicle to be tested, so that the West Pasaman Regency Transportation Service can direct service efforts more effectively. The K-Means Clustering method is used to group customers based on certain attributes, such as test location, test time, vehicle type and test status. KIR test service data is collected, including variables such as test location, test time, test status and type of vehicle to be tested. Research data was analyzed using K-Means Clustering analysis. The research results are categorized into 3 clusters, namely: 1. First cluster: KIR services with work as a trader, income around Rp. 3,473,882, located in the Ranah Batahan District area, the average visit time is 13.18 WIB which is also influenced by the distance traveled by KIR officers from Simpang Empat to Ranah Batahan ±82.3Km, 2. Second Cluster: KIR services with traders earning Rp. . 2,606,324, with the aim of KIR services to Koto Balingka District with a distance of ±56.3 Km and an average visiting hour of 10.37 WIB, the type of vehicle that is often tested is goods cars.3. Third Cluster: KIR services with self-employed work earning Rp. 4,544,071, with the aim of the KIR service to Pasaman District and the average visiting time is 11.05 WIB, the type of vehicle that is often tested is the trailer/outboard carriage.

Dynamic traffic noise simulation at signal-controlled intersections based on cellular automata model

Predicting and assessing road traffic noise is a crucial task in urban environmental management. To accurately predict traffic noise at signal-controlled intersections, this study introduces a dynamic traffic noise simulation model based on cellular automata. Firstly, a cellular automata traffic flow model for a multi-lane signal-controlled intersection is established. The vehicle noise emission and noise propagation models are incorporated into the dynamic simulation of traffic flow to accurately calculate traffic noise. Subsequently, the simulated data is compared with the measured data. The results demonstrate that the proposed method can effectively predict the statistical distribution of traffic noise at the intersection, with prediction errors of less than 2 dB(A) for Leq, L50, L90, and less than 3 dB(A) for L10. Finally, the proposed method is employed to simulate the operational status and noise emissions of vehicles at intersections under various traffic conditions. Based on the simulation results, the intrinsic connection between traffic operation and noise emission is explored, and the characteristics of traffic noise emission and distribution at intersections are analyzed. The method and its findings are anticipated to serve as a valuable reference for the management of road traffic noise.

Off-Grid Electric Vehicle Charging Station with Integrated Local Server OCPP Protocol as a Management System

Abstract Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle

Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation.

Evaluation of electric vehicle response capability in power grid

In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states—such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users’ requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %.•The response capability of EV is non-zero during state transformations•Users’ travel demand assessment•Seamless integration of vehicle-to-grid technology into the power grid.