Range anxiety remains a major barrier to electric vehicle (EV) adoption, especially in charging-desert regions with limited infrastructure. Mobile charging stations (MCSs) offer a flexible on-demand solution, yet EV rescue planning in such regions has not been systematically investigated, particularly under velocity- and battery-state-of-charge-dependent energy constraints. This paper proposes an energy-constrained rescueplanning framework for an EV and a MCS on a road network. The framework integrates velocity-dependent energy modeling, reachable-subgraph construction, and a multi-objective rendezvous optimization that jointly accounts for energy consumption, travel time, and distance to the charging-desert boundary. A case study on the Kentucky–Tennessee boundary demonstrates that the proposed method can identify feasible meeting nodes and the globally optimal rescue strategy. Sensitivity analyses demonstrate the effects of state of charge reserve threshold, auxiliary load, and objective-function weights on feasibility and optimal speed selection.

As electrification technology for medium- to heavy-duty trucks becomes more mature, an increasing number of logistics companies are integrating electric trucks into their fleets. However, limitations such as limited driving range and increased labor costs associated with long charging durations still need to be addressed. To tackle these challenges, towable mobile charging hubs (MCHs) can be adopted as an alternative charging solution for electric delivery fleets. This article integrates towable MCHs into the traditional electric vehicle routing problem with time window constraints (EVRPTW) and nonlinear partial recharging. A hybrid ant colony system (HACS)-based algorithm is introduced to co-optimize decisions between intraroute charging using fixed charging stations (FCSs) and interroute charging using MCHs, routing for delivery vehicles (DVs) and MCHs, and charging schedules for DVs. Case study results demonstrate that the proposed algorithm can balance the benefits between intraroute charging using FCSs and interroute charging using MCHs, achieving up to a 12.16% reduction in operational costs on Solomon instances and 11.27% on a real-world dataset.

Electric vehicles equipped with in-wheel motors provide packaging, controllability, and drivetrain-simplification advantages; however, the increase in wheel-side unsprung mass can intensify vibration transmission to the chassis, seat, and occupant. This paper presents a coordinated active seat and active suspension control strategy for an integrated 8-DOF quarter-car model that includes an in-wheel motor, an active seat suspension, and a 4-DOF seated driver body model. The proposed controller combines a Harmony Search (HS)-optimized proportional–integral–derivative (PID) feedback baseline with a repeatable-disturbance feedforward compensation term. The HS-PID loop provides baseline transient attenuation, while the feedforward term compensates the repeatable component of the bump-induced disturbance transmitted through the coupled seat–vehicle system. The controller is evaluated against passive suspension, active-seat-only control, active-vehicle-suspension-only control, and an HS-PID baseline under repeated bump/shock excitation. The results show that coordinated actuation reduces occupant displacement and acceleration responses relative to the benchmark cases. The discussion explains the active-seat-only peak-acceleration amplification, the different magnitudes of displacement and acceleration improvements, and the practical implications of suspension stroke and actuator-force limits. The reported conclusions are therefore confined to the repeated bump/shock condition considered in this numerical study; broader ride-comfort generalization requires standardized whole-body vibration metrics, random-road validation, speed variation, parametric uncertainty analysis, and drivetrain energy evaluation.

The lifespan of Electric Vehicle (EV) batteries is impacted by the battery Depth of Discharge (DoD), defined as the percentage of the battery’s total capacity that has been utilized. Higher DoD levels accelerate degradation and increase stress on battery components. In rural areas, where access to reliable charging infrastructure may be limited, optimizing battery longevity through effective charging strategies becomes particularly important. This paper presents an aging-aware charging strategy that adapts charging thresholds to enhance battery health and extend driving range within recommended DoD limits. The proposed methodology establishes an optimal, adaptive DoD range that accounts for aging effects to maximize driving range and improve battery lifespan. Validation with real-world driving data shows that this strategy can increase the average driving range by approximately 49 % for selected rural EV users experiencing range anxiety, compared to their observed suboptimal charging behaviors. Additionally, the approach proves effective in promoting proper charging behaviors, further extending battery longevity. These findings highlight the challenges of addressing range anxiety and mitigating battery degradation across diverse user profiles, underscoring the potential of adaptive DoD management to enhance EV battery performance in areas with limited charging access.

The transition toward electric vehicles (EVs) in the automotive industry underscores the critical need for efficient thermal management systems (TMS) to ensure the safety, performance, and longevity of key components such as lithium-ion batteries, electric motors, and power electronics. These components generate significant heat during operation, with lithium-ion batteries being particularly sensitive to temperature fluctuations. Excessive temperatures accelerate battery degradation and pose risks of thermal runaway, while suboptimal thermal conditions reduce energy efficiency and driving range. This study explores the application of numerical simulation techniques to design and optimize a comprehensive TMS for electric cars, addressing the complex interplay of heat generation, dissipation, and energy consumption. Numerical simulations, employing computational fluid dynamics (CFD) software ANSYS/Fluent, enable modeling of thermal and fluid flow phenomena within the TMS. The momentum and energy equations are solved with various combinations of boundary conditions. The study investigates thermal behavior under diverse operating conditions. Additionally, a comprehensive parametric study is explored to enhance thermal performance of a thermal management system. Simulation results are compared to the experimental results measured in the battery lab, the air flow rates are specified while the battery temperatures are validated against the measured data. The agreement between the numerical results and the lab data demonstrates the success of the numerical model. The results highlight the trade-offs between cooling effectiveness and system energy consumption. The findings underscore the value of numerical simulation as a cost-effective and versatile tool for TMS development. By enabling virtual prototyping and rapid iteration, simulations reduce reliance on physical testing while providing insights into complex multi-component interactions. This approach facilitates the design of lightweight, compact, and scalable TMS architectures tailored to evolving EV requirements.

Abstract Cooperative eco-driving (Co-ED) is a promising technology for improving vehicle efficiency through appropriate coordination. Additionally, platooning can significantly improve the vehicle's energy efficiency by reducing aerodynamic resistance. The optimal trajectory of the Co-ED vehicles in a platoon will be challenging to derive due to the high nonlinearity of the aerodynamic drag coefficient. Furthermore, although the electrification of vehicles has made rapid progress, the traffic on the road will still tend to be a mix of conventional vehicles (CVs) and electric vehicles (EVs) for a long time. It is critical to take the energy consumption characteristics of different vehicle types into account for a mixed platoon during Co-ED. This paper considers the platooning effects and heterogeneity of leading vehicles in two-vehicle platoons and utilizes Pontryagin's Minimum Principle (PMP) to derive the optimal speed trajectories for both homogeneous (all-electric) platoon and heterogeneous platoons (with different fuel types of vehicle). Simulation results from the proposed PMP-based Co-ED strategy show that the platooning effect has a noticeable impact on the Co-ED driving behaviors (particularly the intervehicle space and the transient performance). Simulation results also demonstrate that the same following EV will result in less energy consumption by 4.8% in an EV-led platoon under urban/suburban scenario and approximately the same energy consumption under an interstate scenario compared in a CV-led platoon.

Accurate gross mass estimation is critical in operations of electric vehicles that experience payload variations due to its predominant impact on energy consumption and dynamics. This paper introduces a novel Immersion and Invariance (I&I) adaptive observer for estimating gross vehicle mass using only vehicle velocity and driving torque signals. Compared to conventional adaptive observer designs, our approach offers two distinct advantages. First, the I&I scheme is "no regret", ensuring that the norm of parameter estimation errors remains non-expansive. Second, the design enables asymptotic recovery of deterministic observer error dynamics, as the I&I adaptation drives parameter-error-induced perturbations toward an attractive and zeroing manifold. Additionally, an observer incorporating drivetrain inertia is proposed to account for rotational dynamics and enhance model fidelity. The effectiveness of the observers is demonstrated through high-fidelity, hardware-in-the-loop, experiments.

Range anxiety caused by moderated battery performance and accelerated capacity loss at low temperatures is regarded as one of the most significant barriers to the adoption of battery electric vehicles (BEVs). Preheating the battery using grid power has been proven to be a convenient and effective method for BEV users to maintain driving range and extend battery life. Traditional preheating strategies typically heat the batteries right before the vehicle departs, which helps improve operating conditions during battery discharge. However, the impact of battery temperature during charging is often overlooked. Charging batteries at extreme sub-zero temperatures can lead to consequences similar to those of discharging under such conditions, but simply preheating before charging may shift energy usage to peak hours under time-of-use electricity pricing and increase the charging cost. To address this challenge, this paper proposes an optimal coordinated charging and preheating strategy for BEVs to minimize operational costs and extend battery life during level 2 overnight charging. An electro-thermal-aging coupled model is used to describe the battery dynamics under different conditions. Results of a case study under -20 degrees C show that over 80% saving on the operational cost is achieved by the proposed strategy comparing to the conventional strategy and the optimized strategy neglecting accelerated battery aging under low temperature.

One-pedal driving (OPD) can help battery electric vehicles (BEVs) improve energy efficiency and driving comfort with enhanced regenerative braking. Furthermore, vehicle platooning can also improve energy efficiency and thus extend the BEV driving range, through the reduction of aerodynamic drag coefficients of platoon members. The spacing control to achieve short inter-vehicular distance in platooning is rather challenging due to the change of aerodynamic drag coefficient and other environmental factors (e.g., rolling resistance and road grade). Thus, this article presents a robust sliding mode control (SMC)-based inter-vehicle distance control framework for an OPD-enabled BEV to accurately achieve the desired inter-vehicle distance in vehicle platooning applications, in the presence of uncertainties such as aerodynamic drag coefficient, road grade, and rolling resistance coefficient. The control framework contains an SMC spacing controller and a high-gain input observer for estimating the lead vehicle's acceleration. The framework is validated by both simulations on a high-fidelity BEV model with the OPD feature and field tests. In addition, a novel method is proposed to analyze the robustness of the controller by adjusting the parameters in the control law, without altering the testing environments. The efficacy of the proposed control framework and the analysis method are proved.

Accurate estimation of the state of health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

A networked battery system (NBS) consists of multiple batteries linked to form a battery energy storage system (BESS). Intelligent NBS are formed by smart battery agents that communicate and coordinate together to optimize transients, energy usage, storage, and power allocation. Popular applications of these include DC microgrids. Traditionally, a decentralized voltage-current (V-I) droop control strategy is deployed in these systems to regulate energy distribution. However, V-I droop control operates as a power-averaging distributed algorithm, which may not be suitable for battery systems with heterogeneous batteries of varying capacities and state-of-charge (SoC) dynamics. During battery discharge, a battery with a lower SoC level or smaller installed capacity will deplete first and will no longer be able to contribute to the battery system. Therefore, maintaining SoC balancing is essential to prevent these issues. To overcome these limitations, we propose a multi-agent reinforcement learning control algorithm to optimize SoC balancing. This approach ensures that each battery maintains the same SoC level. Reinforcement learning (RL) enables data-driven learning of the optimal charging/discharging control policies even in the presence of uncertainties in the battery capacities and SoC dynamics. This multi-agent RL framework ensures optimal balancing of SoC levels with the ultimate goal of improving battery life and reducing battery maintenance costs.

When electric vehicle (EV) batteries degrade below a certain capacity, they may no longer be suitable for automotive use but can be repurposed as second-life batteries (SLBs) for other applications, such as EV charging stations. When integrated with photovoltaic (PV) systems, SLB can store surplus solar energy, reducing reliance on the grid and lowering operational costs. This paper presents a novel techno-economic assessment framework for deploying SLBs in combination with PV in grid-connected EV charging stations. The proposed framework integrates the value proposition, charging station operation, optimal dispatch strategies, battery degradation modeling, input data requirements, and detailed procedures for generating key economic performance metrics. Insightful analyses are performed to assess the performance of SLBs in comparison to new batteries across various cost scenarios. The results indicate that SLBs become financially attractive when their cost is 40% or lower than new batteries.

Electrochemical sensors have been broadly applied in various areas for diagnostics and controls. However, many electrochemical sensors are often cross-sensitive to one or more interfering compounds, which can make it rather challenging in attaining accurate concentration of the target compound. The sensor cross-sensitivity can cause instability issues in feedback control systems and inaccurate diagnostics. In many cases, a state estimation problem with cross-sensitive output is equivalent to a state estimation problem with limited number of state candidates that can lead to the same output. In this study, an innovative estimation error-based observer is proposed for state estimation for a general class of 1st-order nonlinear dynamic systems with cross-sensitive output. By constructing observers for each potential candidate and analyzing the respective estimation errors, the true state (or mode) of the system can be determined based on the cross-sensitive output measurement. The proposed algorithm was applied to estimate the ammonia coverage ratio of a Diesel selective catalytic reduction system with ammonia cross-sensitive NOx sensor output. Simulation results show that the true ammonia coverage ratio can be accurately estimated with this method at both low and high ammonia coverage ratios. This method can be applied to a broad class of nonlinear dynamic systems with cross-sensitive outputs for state estimation.

We solved the challenge of integrating hours of regulation in long haul deliveries into the electric vehicle routing problem and investigated its impact from the perspectives of the drivers and fleet operators. The routing and driver's charging and rest schedule are optimized using a hierarchical hybrid ant colony system algorithm. The algorithm is validated on 12 modified electric vehicle routing problem (EVRP) instances containing 21-199 customers. It was found that the delivery time considering breaks would not significantly increase if the resting facility provided a charging service. Additionally, the proposed schedule optimizer is able to save more than 10% delivery time.

This review provides a comprehensive examination of Vehicle-Grid Integration (VGI) technologies and their impacts on transportation systems, with a particular emphasis on the transportation-energy nexus. It systematically explores how VGI affects key transportation applications such as charging infrastructure planning, electric vehicle (EV) routing, smart charging coordination, shared mobility, and dynamic pricing. By synthesizing recent literature from both transportation and energy systems perspectives, this study highlights how advanced methodologies, such as reinforcement learning, game theory, and optimization techniques, are used to model the complex interactions between EVs, mobility patterns, and distributed energy systems. The review also identifies critical challenges, including behavioral factors, data limitations, and system scalability. Drawing on these insights, the paper outlines emerging research opportunities to support the design of integrated, resilient, and user-centric VGI solutions that advance sustainable mobility and energy system efficiency.

Second-life battery energy storage systems (SL-BESS) are an economical means of long-duration grid energy storage. They utilize retired battery packs from electric vehicles to store and provide electrical energy at the utility scale. However, they pose critical challenges in achieving optimal utilization and extending their remaining useful life. These complications primarily result from the constituent battery packs' inherent heterogeneities in terms of their size, chemistry, and degradation. This paper proposes an economic optimal power management approach to ensure the cost-minimized operation of SL-BESS while adhering to safety regulations and maintaining a balance between the power supply and demand. The proposed approach takes into account the costs associated with the degradation, energy loss, and decommissioning of the battery packs. In particular, we capture the degradation costs of the retired battery packs through a weighted average Ah-throughput aging model. The presented model allows us to quantify the capacity fading for second-life battery packs for different operating temperatures and C-rates. To evaluate the performance of the proposed approach, we conduct extensive simulations on a SL-BESS consisting of various heterogeneous retired battery packs in the context of grid operation. The results offer novel insights into SL-BESS operation and highlight the importance of prudent power management to ensure economically optimal utilization.

The lack of charging infrastructure has become one of the most significant barriers for widespread adoption of battery electric vehicles (BEVs). Mobile charging stations (MCSs) with integrated batteries is a promising charging alternate due to their flexible deployment and enhanced grid stability. In addition, using retired BEV batteries as an energy storage system (ESS) for MCSs further reduces equipment cost, which makes MCSs economically viable. MCSs discharge energy from the ESS therein to BEVs, and the discharging strategy needs to be well designed to address the safety risks posed by the degradation of second-life battery capacity and performance during secondary use. Although the optimal charging strategies from the chargers to BEVs have been well studied, the optimal discharge strategy from the ESS-integrated MCS side has received little attention. To fill in this gap, this paper develops an optimal aging-aware discharging strategy specifically for second-life battery (SLB)-integrated MCS applications using Pontryagin’s Minimum Principle (PMP). The discharging time and current profile of the SLBs are optimized to extend the battery life and reduce the risk of thermal runaway. The simulation results show that the proposed strategy effectively reduces capacity loss without significantly extending discharging time, while maintaining temperature within the optimal range compared to conventional constant current discharging methods. Additionally, the performance of the method is proven on different SLB-integrated MCSs with different State of Health (SoH).

This paper presents an advanced optimization framework for the optimal dispatch of electric vehicle (EV) charging stations integrated with second-life batteries (SLBs) and photovoltaic (PV) generation. The proposed formulation explicitly models EV demand dynamics using arrival and departure times, energy targets, and plug constraints, alongside laxity-based charging policies. The framework incorporates detailed operational constraints for battery energy storage systems, including charging/discharging limits, round-trip efficiency, and energy balance. A mixed-integer linear programming model is developed to maximize station profitability by optimizing grid energy purchases, solar power utilization, EV charging revenues, and battery operations. Through case studies with different levels of SLB degradation and varying numbers of EV charging ports, we demonstrate the effectiveness of the proposed optimization model in improving economic performance, enhancing energy efficiency, and meeting EV demand under realistic operational constraints.

As the first-generation Battery Electric Vehicles (BEV5) reach the end of use life, the disposal of retired batteries raised significant economic and environmental concerns. To alleviate these problems, reusing retired BEV batteries on other applications such as off-grid photovoltaic (PV) systems with integrated energy storage system is a promising direction to give them a "second-life". Although reuse Second-life Batteries (SLB) reduces cost of PV systems, significant challenges such as battery performance degradation caused by aging still need to be tackled. This paper utilizes Genetic Algorithm (GA) to minimize total cost of an off-grid PV system by optimizing the solar array size, SLB size, and starting state of health of SLBs simultaneously. The battery dynamics, aging, and performance degradation are modeled and integrated into the problem. Additionally, a real-world BEV charging dataset from 39 state parks in Tennessee is used to validate the performance of the algorithm in off-grid PV charger applications. The simulation results exhibit the algorithm can minimize total cost while ensuring battery performance. An economic analysis is performed comparing with the use of new batteries, and it is found that utilizing SLB for off-grid PV charger gives an average of 49.19% cost saving. Copyright (c) 2024 The Authors.

Mobile charging stations (MCSs) play a pivotal role in mitigating charging deserts prevalent in rural areas by offering the flexibility to be transported to desired locations for electric vehicle (EV) charging. MCSs address concerns related to power infrastructure limitations and locational constraints, thereby alleviating range anxiety. Despite this significance, current research exhibits a notable dearth of investigations focusing on off-grid energy storage systems that integrate renewable energy sources and repurpose second-life batteries (SLBs) retired from EVs for EV charging stations (EVCS). While conventional power grid sources are conventionally relied upon for EV charging, further inquiry is imperative to explore the potential of off-grid systems leveraging renewable energy and SLBs. Addressing this research gap holds substantial promise in advancing sustainable EV charging infrastructure. This study endeavors to fill this void by presenting the sizing design and cost analysis of a standalone photovoltaic (PV) system integrated with an SLB bank for EVCS in public parks. The methodology commences by utilizing real-world power demand data collected from Tennessee state park as input and subsequently determining capacity loss based on the selected aging model to decide appropriate battery sizes. Finally, the Life Cycle Cost (LCC) estimation of proposed charging stations inputs for the cost analysis. The results indicate that the proposed SLB -based EVCS can reduce LCC by 32.16%, when compared to the baseline. Copyright (c) 2024 The Authors.