Browse Topic: Energy management

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Hybrid-Powered Tracked Vehicle Integrated Energy Management Strategy2026-99-212311/13/2026
To address the energy management strategy of hybrid tracked vehicles, and in order to achieve a more rational power distribution among the engine-generator set and the power battery, thereby improving overall fuel economy and system operating efficiency, this paper proposes an integrated energy management control strategy based on a rule-based strategy and deep Q-networks (DQN). Considering the structural characteristics and operating features of the series-hybrid tracked vehicle power system, a relatively comprehensive vehicle dynamics and energy management simulation model is established on the MATLAB/Simulink platform. We introduce a deep reinforcement learning method. The agent’s observation inputs are the vehicle’s operating state and power demand, whereas fuel consumption, battery limits, and various system operating restrictions are folded into the optimization objective. By continuously interacting with the model environment, the agent can continuously learn energy allocation strategies for the powertrain. After training is completed, the trained agent is tested under specific operating conditions, and the proposed integrated control strategy is compared with traditional constant-voltage control and a multi-point speed control strategy. Simulation results show that, under the same test conditions, the integrated control method can optimally adjust engine speed, thereby reducing the number of operating points in the low-efficiency range. While ensuring vehicle dynamic performance and system stability, this strategy can reduce fuel consumption by at least 3.66%, thereby demonstrating its feasibility and effectiveness in energy management for series-hybrid tracked vehicles.
Yan, Zhipeng, Shi, Feina, Zhao, Zhiying, Niu, Liye, Geng, Jiangbo
Research on Multi-Train Cooperative Energy-Saving Optimization Based on Regenerative Braking Energy Recovery2026-99-213411/13/2026
With the acceleration of urbanization, passenger traffic on urban rail transit has proliferated rapidly, emerging as a significant source of urban energy consumption and impacting the efficient operation and sustainable development of urban rail transit systems. In the field of multi-train cooperative energy-saving operation for urban rail transit, previous research has faced two main challenges: First, studies often indirectly represent regenerative braking energy recovery rate through the overlap time of “traction-braking” phases, neglecting to account for intuitively demonstrating energy-saving effects. Second, research has predominantly centered on mathematical formulation and algorithmic development, neglecting practical engineering constraints and failing to adequately assess the viability of implementing operational plans in real-world scenarios. To bridge these identified gaps, this study fully integrates the characteristics of power supply sections with the spatiotemporal coupling of train operations. It analyzes the interaction scenarios of regenerative braking energy among all up- and down-bound trains within the same power supply zone that influence each other. Employing the spatiotemporal network modeling method, a three-dimensional space-time-speed grid network framework is constructed. The global train operation time and state are discretized, and the objective function is formulated to minimize the net energy consumption of train operations. A mixed-integer programming (MIP) model is developed subject to various constraints such as departure intervals, stop durations, safety tracking, and additional operational constraints. The resulting solution is an energy-optimized urban rail train timetable and operational diagram. Numerical experiments are conducted in engineering scenarios to verify the efficacy of this approach, thereby establishing a generalized framework for energy-saving optimization in actual urban rail transit lines. Two solution methods based on the constructed model are proposed for different engineering applications, ensuring safety, operational efficiency, and energy sustainability of urban rail transit operations.
Chen, Yuefeng, Lao, Ziyao, Jiang, Jiahui, Jiang, Yangsheng, Huang, Jingyang
Optimizing Container Drayage with Electric Tractors under a Separation Mode2026-99-200111/13/2026
The global strategic imperatives of carbon dioxide emission reduction and carbon neutrality, together with the promotion of high-quality sustainable development, have led to a progressive decline in the reliance on fossil fuels across both commercial and passenger vehicle fleets. In response, a growing number of freight carriers are transitioning toward electric trucks as their primary delivery fleet. This study addresses the local container drayage problem with electric vehicles and conventional diesel vehicles, aiming to find out the gap between these two different types of vehicles within and outside the tractor-container separation mode. In this context, an electric tractor can be separated from the container during the process of (un)loading at customer nodes for the other tasks or recharging. This study also focuses on the different energy consumption rates of electric trucks under empty load, laden load, or separation mode to simulate a more realistic transportation scenario. Accordingly, a route planning and charging decision model for electric tractors is formulated with the objective of minimizing the total operation cost, including the fixed cost, energy cost, and labor cost. To efficiently solve this mathematical model, an adapted Clarke and Wright heuristic algorithm suitable for this problem is designed. We introduce the feasibility check functions for battery state and (un)loading time constraints into the adapted heuristic algorithm. Finally, a series of numerical experiments is conducted in real-world highway networks and transportation scenarios to assess the performance of the algorithm compared with Gurobi and to analyze the practicability of electric tractors for the separation mode under the influence of various objective terms.
Zhang, Yang, Xue, Zhaojie, You, Jintao, Dai, Xintong
Optimization Research on Energy Consumption Prediction Model for New Energy Vehicles Based on Speed System2026-99-210311/13/2026
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Gao, Ming, Gao, Zhang, Liu, Shuang
Development of a Driving Behavior Scoring Model for Electric Vehicles Oriented to Energy Consumption2026-99-212211/13/2026
Considering the huge influence of personal driving habits on the actual driving mileage of electric vehicles (EVs), together with the weak interpretability of most existing evaluation models, this research combines logistic regression and scorecard methods to build a new assessment model that focuses on energy consumption and driving behavior, and a complete closed-loop technical framework has been set up in the research process, covering key steps such as raw data preprocessing, feature mining and screening, model design and construction, as well as final score output. Taking real collected vehicle operation data as the basic research material, we extracted 17 feature indicators from three major dimensions, including driver pedal operation habits, real-time vehicle operating state, and actual energy consumption performance, and adopted the mutual information (MI) calculation method to select eight core features with strong differentiation for 100-kilometer power consumption. Since obvious nonlinear correlations exist between these core indicators and vehicle energy consumption, we adopted decision tree-based grouping processing, weight of evidence (WOE) encoding, and information value (IV) quantitative analysis to realize feature discretization and optimization, and then constructed a scoring model relying on a logistic regression algorithm. The verification results demonstrate that the proposed model has an Area Under the Curve (AUC) value of 0.83 when identifying driving behaviors with high or low energy consumption, proving its excellent differentiation ability. This model turns complicated driving behaviors into intuitive quantitative scores, effectively making up for the defects of traditional black-box models that cannot offer practical guidance for energy-saving optimization, and it can also provide solid theoretical reference and practical application support for promoting energy-saving driving behavior among EV users.
Deng, Shuxiang, Guan, Di, Wu, Yunzhu, Liang, Yongkai, Ma, Kunqi, Xu, Hang
Real Road Energy Consumption Analysis of Electric Vehicles in Typical ASEAN Countries2026-99-213911/13/2026
The export of new energy vehicles (NEVs) has become a crucial strategic direction for the high-quality development of China’s automotive industry. As a major market for China’s NEV exports, the Southeast Asian region highlights the significance of developing region-adapted automotive products to advance the high-quality growth of China’s automotive sector. However, most currently exported models are developed based on existing standards without full adaptation to the regional traffic characteristics of Southeast Asia, resulting in insufficient accuracy of adaptive calibration. To address this issue, this study focuses on Thailand, Indonesia, and Vietnam, collecting 5,577 km of real-vehicle road data covering multiple typical scenarios. Multi-dimensional analysis is conducted to reveal regional driving characteristics and energy consumption patterns. The results indicate that the driving characteristics of the three countries show the highest consistency with the China Light-duty Vehicle Test Cycle (CLTC), while energy consumption exhibits significant regional differences. Vehicle speed and acceleration are identified as the core influencing factors. This research provides key data support for the calibration optimization and energy consumption control of models targeting the Southeast Asian market, contributing to enhancing the export competitiveness of China’s NEVs.
He, Junnan, Zhao, Zhiqiang, Wang, Xueying, Lin, Meixia, Meng, Qingyu, Li, Yongping
Method for Identifying Typical Vehicle Operation Scenes Applied to Energy-Saving Driving of New Energy Vehicles2026-99-214111/13/2026
Aiming at the problems that existing energy-saving technologies for new energy vehicles highly rely on high-cost, high-precision sensors such as radars and cameras, and suffer from high implementation costs, this paper studies vehicle operation scene recognition methods for energy-saving driving of new energy vehicles, aiming to develop a low-cost scene recognition model and provide corresponding technical support for energy-saving driving of such vehicles. Firstly, actual road driving data of new energy vehicles with comprehensive scene coverage are collected via real-vehicle tests, with the total test mileage exceeding 126,000 km. Secondly, a data-driven recognition method for typical vehicle operation scenes combining Principal Component Analysis (PCA) and the K-means clustering algorithm is proposed, which classifies three typical operation scenes: congested, urban free-flow, and suburban scenes. Finally, a set of scene-specific driving assistance control strategies is designed, and the potential energy-saving effects of the strategies based on vehicle operation scene recognition are investigated based on a mature powertrain energy consumption simulation model for new energy vehicles. The research shows that under the China Light-Duty Vehicle Test Cycle (CLTC), the energy consumption of new energy vehicles can be reduced by approximately 4.5% through anticipatory perception of vehicle operation scenes and adaptive switching of scene-specific energy-saving control strategies. Moreover, the proposed technology can realize accurate scene recognition without relying on high-cost, high-precision sensors such as radars and cameras, and it can meet the application requirements of low-cost new energy vehicles, including logistics vehicles.
Liu, Yu, Guo, Yijing, Zhang, Hao, Yu, Hanzhengnan, Yuan, Xiaohui
Comparative Study on Fitting Methods of Low-Temperature Road Driving Resistance on Chassis Dynamometer2026-99-210911/13/2026
The driving range of electric vehicles (EVs) under low-temperature conditions remains a critical challenge in the automotive industry—especially in high-latitude regions where prolonged low temperatures severely degrade user experience. In addition to the well-documented reductions in battery energy density and charging efficiency, the effect of low temperature on vehicle road driving resistance is equally significant, contributing an additional 15%–25% to overall energy consumption [1, 2]. Guided by mainstream international and domestic standards for simulating low-temperature road driving resistance, actual road tests and chassis dynamometer resistance fitting were conducted on four representative vehicle models—including compact sedans and mid-size SUVs—at 20 °C (normal ambient temperature) and -7 °C (typical low-temperature operating condition). The discrepancies between simulated resistance values from different methods and actual road test data were systematically analyzed, along with the underlying mechanisms. The results indicate that the low-temperature chassis dynamometer set resistances obtained using the Chinese current national standard (GB/T 18386-2021) and the United Nations Global Technical Regulation No. 15 (UN GTR No. 15) are largely consistent, with deviations of 5%–12% from actual road driving resistance. In contrast, the SAE J2264 method exhibits a substantial deviation of 25%–28% from actual low-temperature driving resistance. Notably, even the −7 °C driving resistances simulated by current mainstream methods still deviate to a certain extent from real-world low-temperature road resistance. This underscores the need for further investigation into the mechanisms by which transmission system resistance and tire rolling resistance influence overall low-temperature driving resistance, as well as the development of more precise correction models to improve the reliability of low-temperature range prediction.
Bai, Bateer, Deng, Shuxiang, Nie, Guole, Niu, Yazhuo, Zhang, Xianfeng, Li, Ping, Guo, Wencui
An Enhanced Mamba Integrating Dynamic Compression Ratio Channel Attention and Progressive Projection for Multivariate Time Series Forecasting2026-99-064011/6/2026
Time series forecasting is a practical cornerstone for decision-making in diverse fields including finance, energy management and meteorology, yet conventional Transformer-based models are plagued by high computational complexity inherent in their attention mechanism, as well as poor efficiency in capturing long-range dependencies when processing long-sequence time series data. Meanwhile, the recently proposed Mamba model built on a linear-complexity state space model has achieved strong performance in long-sequence modeling tasks, but the vanilla Mamba model still has obvious limitations in time series forecasting scenarios, including insufficient discriminability of channel features, unsatisfactory prediction accuracy, and information loss during feature transmission in the encoder layer. To address these issues, this paper proposes An Enhanced Mamba Integrating Dynamic Compression Ratio Channel Attention and Progressive Projection for Multivariate Time Series Forecasting, which incorporates three core improvement strategies: a lightweight dynamic compression ratio channel attention, a residual-optimized encoder, and a progressive prediction head. These strategies realize adaptive weighting of channel features, lossless information transmission in the encoder and gradual refinement of forecasting results respectively. Extensive experiments on multiple public time series datasets show that compared with the vanilla Mamba model and mainstream Transformer-based forecasting methods, the proposed model achieves significant improvements in prediction accuracy, along with stronger robustness and higher practical application value.
Zhu, Yongdong, Zou, Lei, Li, Na, Wang, Chenxi
A Multi-UAV Cooperative Path Planning Method Based on an Improved Dung Beetle Optimizer2026-99-221111/6/2026
Complex terrain imposes highly nonlinear constraints on multi-UAV cooperative path planning. It also leads to path redundancy and inefficient information sharing among UAVs. To address these issues, this paper proposes a cooperative path-planning method based on an improved Dung Beetle Optimizer (DBO) under dynamic constraints. First, a multi-objective path optimization model is constructed. The model considers detection efficiency, path smoothness, obstacle-avoidance cost, and energy consumption. State-space equations are introduced to constrain the path generation process. This ensures that the planned paths satisfy UAV dynamic constraints and remain feasible for control implementation. Second, to reduce the risk of premature convergence in the standard DBO, the Latin hypercube sampling-Orchestrated Dung Beetle Optimizer (LO-DBO) is proposed. Latin hypercube sampling is used to generate a uniformly distributed initial population in the solution space, thereby enhancing the diversity of the initial population. A dynamic convergence factor is then introduced to balance global exploration in the early stage and local exploitation in the later stage. In addition, a cooperative search mechanism is designed to help individuals escape local optima and improve the algorithm’s global search capability. Experiments on CEC2017 benchmark functions and multi-UAV cooperative path-planning scenarios show that LO-DBO achieves higher convergence accuracy, better solution quality, and greater planning efficiency than several representative optimization algorithms. Compared with other improved DBO, LO-DBO can coordinate multi-UAV resources more effectively. It also enables safe obstacle avoidance and efficient full-coverage inspection operations in complex terrain scenarios.
Wu, Binglin, Song, Yifei, Zhu, Chunhua, Li, Ning
A Hierarchical Optimization Strategy for On-Orbit Energy Management and Attitude Control of Spacecraft2026-99-060511/6/2026
This paper addresses an energy-optimal attitude control strategy for space solar power satellites (SSPSs) during on-orbit energy transmission missions. The study focuses on a space solar power generation and energy transmission experimental satellite as the research object. First, the attitude dynamics of the satellite are modeled using the Lagrange method, and the satellite’s attitude control actuators, namely the single-gimbal control moment gyroscopes (SGCMGs), are also modeled to capture their dynamic behavior and constraints. Second, a PD controller based on quaternion and angular velocity error feedback is designed to achieve the desired attitude necessary for the energy transmission task. Subsequently, a hierarchical optimization approach is developed to obtain the energy-optimal attitude control strategy over one orbital period, considering multiple factors such as the satellite’s on-orbit energy consumption, the energy collection efficiency of the solar array in non-shadow regions, and the energy transmission efficiency between the satellite and the ground station. The optimization process is divided into two levels. In the first level, within the ground station’s visibility window, an energy balance analysis at different potential transmission moments is performed to determine the optimal transmission timing that maximizes the energy received by the ground station. In the second level, during the energy transmission phase, a cost function is formulated that incorporates pointing accuracy, pointing stability, and actuator energy consumption, and the controller parameters are globally optimized using the Adaptive Particle Swarm Optimization (APSO) algorithm. The simulation results demonstrate that the proposed hierarchical optimization method can significantly enhance the operational efficiency of on-orbit energy transmission, providing a practical engineering approach for the efficient on-orbit operation of future space solar power stations.
Wang, Ke, Zhang, Ning, Ma, Wenlai, Zhao, Yang
Energy Management for Series Hybrid Aircraft Based on a Dual Adaptive Equivalent Fuel Minimization Strategy2026-99-060311/6/2026
Series hybrid tiltrotor aircraft operate under highly dynamic and multi-phase flight missions. This paper addresses the resulting energy management challenge. Moving beyond the direct application of existing optimization algorithms, this study proposes a novel codesign framework that synergizes offline global optimization with online fuzzy adaptive control. The key theoretical innovation lies in transforming the online, real-time optimization problem of determining the optimal equivalent factor into an offline global optimization problem for the parameters of a fuzzy-PI controller. This is achieved through a hybrid particle swarm optimization and genetic algorithm (PSOGA), which optimizes the controller's scaling factors and gains. The optimized controller embeds mission-level economic information into the online generation of the equivalent factor, so that the power-split decision is determined not only by the instantaneous operating conditions, but also by the overall economic objective of the mission. Simulation results under typical and stochastic flight conditions validate the framework's effectiveness. Compared to benchmark strategies, the proposed PSOGA-Fuzzy-A-ECMS achieves a 6.7% improvement in fuel economy while robustly maintaining the battery state of charge within a safe and stable range. This study provides a systematic and theoretically-grounded design paradigm for real-time energy management in complex, time-varying systems.
Wang, Zhihao, Wang, Xiangyang
An Interactive Click-Based UAV Path Planning Method for Anti-Corrosion Spraying Using BIM-SLAM Fused 3D Models2026-99-063711/6/2026
To solve the critical challenges of uncoordinated operational node selection and premature mission termination caused by dynamic energy or coating depletion during anti-corrosion spraying on largescale steel structures, this paper proposes an advanced path planning methodology for UAVs. The proposed approach is built upon a BIMSLAM fusion model. Specifically, a high-fidelity three-dimensional (3D) environmental model is reconstructed by integrating offline BIM structural priors with real-time SLAM data. Furthermore, the framework implements the ICP algorithm and Kalman filtering simultaneously to eliminate point cloud noise and compensate for perceptual occlusions, thereby achieving a 5 cm localization accuracy in cluttered industrial environments. A dual-stage pipeline is established for pre-screening and dynamic evaluation. In the offline phase, geometrically inaccessible task points are identified and pruned via spatial accessibility analysis. During real-time operations, the onboard system continuously calculates the battery State of Charge (SoC), operational power, and remaining paint volume to eliminate inefficient flights. For the path generation stage, a hybrid search strategy is developed, which implements a Genetic Algorithm for task sequencing alongside a multi-objective cost function parameterized by four metrics: energy consumption, paint requirements, obstacle proximity, and task density. Additionally, Softmax is used to dynamically regulate the weight coefficients, enabling the system to balance operational safety and efficiency based on the instantaneous state vector. Finally, a fifth-order B-spline parameterization is used to maintain flight stability under complex wind disturbances and heavy time-varying payloads. Consequently, the results show that the proposed method significantly outperforms conventional baselines. The recorded 96.7% mission completion rate demonstrates its operational reliability for the maintenance of bridges and towers.
Qian, Lei, Huang, Chen, Tan, Libin, Huang, Tao
Modeling and Energy Consumption Characteristics Simulation Analysis of a Two-Speed Drive System for a Permanent Magnet Synchronous Motor2026-99-192711/6/2026
This study systematically analyzes the energy efficiency optimization requirements of the permanent magnet synchronous motor (PMSM) drive system for electric vehicles from three dimensions: motor topology, vector control algorithm, and two speed variable transmission mechanism. Firstly, analyze the electromagnetic structure of PMSM fixed rotor and compare the differences in magnetic circuit characteristics between surface mounted and built-in rotors, laying a theoretical basis for motor selection. Secondly, based on the id=0 control principle, a PMSM speed current dual closed-loop speed regulation architecture is built, and the PI regulator parameters are tuned according to the bandwidth characteristics of the current loop to ensure that the system has excellent dynamic response and speed following capabilities. Furthermore, key sub modules such as vehicle driving cycle, motor body, and two-speed transmission are integrated to construct a joint simulation environment. Evaluate the vehicle speed tracking accuracy and energy consumption level of the system under four typical operating conditions: NEDC, WLTC, CLTC-C, and CLTC-P, with a focus on revealing the mechanism of transmission ratio matching on the coverage range of the motor's high-efficiency zone. The core parameters of the simulation model in this article are calibrated based on the standard engineering parameters of mass-produced pure electric passenger vehicles and validated academic literature data. The model verification is completed by benchmarking the output results of existing mature simulation models to ensure the reliability of the simulation. Simulation data shows that the two speed transmission scheme significantly outperforms the single speed system in terms of energy consumption performance, and the optimal transmission ratio combination (8,5) can effectively broaden the high-efficiency working range of the motor, demonstrating optimal energy efficiency under WLTC and CLTC-P operating conditions. This study is mainly based on high fidelity simulation models for theoretical verification. Although bench tests have not been conducted, the model parameter calibration and verification process conforms to industry standards. The conclusion can provide theoretical reference and data support for parameter matching and energy-saving design of electric vehicle multi speed drive systems.
Liu, Chunhui
Design of a Gantry-Type Intelligent Grasping Device for Logistics Warehousing Based on MaixCam2026-99-192511/6/2026
Traditional warehousing suffers from low efficiency, high manual error rates, substantial labor costs, and complex deployment, while existing sorting devices remain poorly adaptable for small- and medium-sized operations. To address these issues, this paper presents an intelligent gantry-type grasping device for logistics warehousing based on the MaixCam. The device integrates mechanical design, computer vision, and electronic control. An Arduino Mega2560 serves as the main control board, with 57-series and 42-series stepper motors and servo motors driving the gantry and gripper. The mechanical structure adopts a miniaturized gantry architecture constructed from alloy steel, enabling operation in confined workspaces. A YOLOv5s model with transfer learning was trained for cargo box recognition using a dataset of 654 images collected under various lighting conditions and backgrounds. The model achieves a recognition accuracy of 93%–96% with inference times of 10–14 ms at 25–28 FPS. The proposed device provides a viable solution for automated sorting in small-to medium-sized warehouses, balancing recognition performance, power consumption, and cost.
Xiang, Chang’an, Zhou, Dan, Fan, Zhiwei
Smart Home–Truck Cabin Temperature Synchronization2026-28-021111/5/2026
Temperature management in both residential environments and commercial vehicle cabins are rapidly evolving toward intelligent, interconnected climate-control ecosystems. However, current systems operate in isolation, with no mechanism for synchronizing thermal comfort preferences between a user’s smart home and the cabin environment of a truck used for either personal mobility, last-mile delivery, or commercial logistics. This disjointed control results in inconsistent comfort experience, unnecessary HVAC energy consumption, and the inability to anticipate user needs based on transition patterns (e.g. leaving a cool home for a hot truck cabin) between home and vehicle. To address this technical challenge, this research paper provides bidirectional communication temperature-synchronization framework in which a smart-home HVAC app communicates seamlessly with the truck’s cabin climate-control system, integrating user-specific comfort profiles(such as temperature preferences, real-time cabin and home temperature, humidity levels, HVAC operating states, outside weather forecasts, trip start times, thermal-soak estimates etc.) and environmental prediction models to maintain consistent thermal comfort across both environments. Based on user’s comfort profile, our framework derives the Comfort Deviation Index (CDI), Transient Temperature Gradient (TTG) and applies predictive algorithms to precondition the cabin or home environment accordingly. The AI technique Long Short-Term Memory (LSTM) network is used for forecasting temperature transitions across home-to-vehicle. Our technique becomes even more pronounced in small-haul trucks that operate under frequent stop-and-go conditions, where rapid cabin temperature fluctuations are common. There is currently no existing research or framework that enables seamless temperature synchronization between a user’s home environment and the truck cabin. A smart-home–to-cabin synchronization approach would fill this gap by intelligently accounting for outside weather, trip duration, thermal soak, and energy-efficiency constraints. Additional challenges include ensuring secure communication over cloud and edge networks, managing latency constraints for real-time actuation, and preventing conflicts between home HVAC automation and automotive HVAC control logic
Prajapati, Saurav, Sagar, Kshitij Singh, B. S., Sanchitha
An Optimization Framework for Dynamic Vehicle and Driver Fleet Allocation2026-28-014211/5/2026
EV fleets in the EU/US region are owned by a fleet manager who manages the entire fleet operation. Currently, the telematics ECU transmits a significant amount of information that can be used to our advantage for vehicle–route assignment. The beneficiaries of this solution would be the fleet managers, who will receive an optimized vehicle–route plan, and the drivers, who will be able to see their entire week’s schedule. This can be achieved using models and applications in operations research, which take in EV vehicle parameters and provide an optimal solution. Existing fleet and vehicle control strategies typically rely on fixed actuation limits or rule-based logic that are configured without direct awareness of fleet-level objectives or task-specific requirements. As a result, vehicles may operate inefficiently, experience unnecessary energy consumption, or incur accelerated component aging. There is therefore a need for a system in which fleet and task information directly influence vehicle actuation in a dynamic and coordinated manner. This approach enables fleet operators to reduce energy cost, battery degradation, and unplanned downtime by dynamically aligning vehicle actuation with actual task and fleet needs rather than static calibrations. It improves fleet utilization and scalability by allowing vehicles to automatically adapt their energy and power behavior as operational conditions change. As electrified and mixed fleets grow and complexity increases, such optimization-driven actuation becomes critical for achieving cost-efficient, reliable, and sustainable fleet operations. The scheduling is modelled in three distinct layers that is vehicle-task scheduler, charging scheduler, human driver scheduler, thereby ensuring a clean modular design.
Anavekar, Abhishek Anil, Bhowmick, Saikat
Distributed Regenerative Active Suspension System with Cross-Coupled Hydraulic Actuation and Road-Preview AI2026-28-016511/5/2026
This paper presents a Distributed Regenerative Active Suspension System (D-RASS) that integrates active suspension control with suspension-level energy regeneration in a predictive architecture. Each wheel employs an electro-hydraulic actuator interconnected through hydraulic cross-coupling and a shared DC bus, enabling coordinated force generation and bidirectional energy flow. Suspension forces are generated via controlled pressure differentials, while rebound energy is converted into electrical energy through motor–pump units operating in generator mode. Road-preview intelligence from LiDAR, radar, and geospatial data enables anticipatory suspension control. The centralized controller optimizes ride comfort, handling stability, and energy recovery by adapting force and energy flow strategies in real time. The proposed system establishes a unified framework for predictive suspension control and energy-aware vehicle dynamics.
Hazra, Sandip, Ambatkar, Kedar
A Surrogate Modeling Approach for the Weight Optimization of a Two Wheeler Chassis with PSD Based Durability Validation2026-28-020711/5/2026
As the global automotive industry pivots toward sustainable mobility, reducing vehicle curb weight remains a paramount objective for enhancing energy efficiency and range. This paper presents a robust and comprehensive methodology for the light weighting of a two-wheeler chassis through the integration of Design of Experiments based optimization and Power Spectral Density durability analysis. The study begins by establishing a baseline durability profile for the vehicle using frequency-domain durability simulations under random vibrational loading, ensuring a benchmark for structural integrity. To explore the design space effectively, frame tube thicknesses are parameterized, and a Design of Experiments design matrix is constructed using Modified Extensible Lattice Sequence technique. This design matrix is solved, and a response matrix is built. These two matrices are combined, and appropriate curve-fitting techniques are applied to construct a Reduced Order Model. This mathematical surrogate model enables the rapid evaluation of the frame’s response under varying frame thickness combinations, thereby avoiding computationally expensive traditional Finite Element Analysis iterations. The optimum combination of frame tube thicknesses is identified using the Global Response Surface Method. This configuration minimizes the overall mass of the vehicle while strictly maintaining predefined durability and safety targets. The final optimized frame is evaluated by a secondary Power Spectral Density durability analysis to validate the accuracy of the surrogate model’s predictions. The integration of this metamodel-based design approach results in a significant reduction in structural weight. Ultimately, this framework accelerates the design cycle by developing a lightweight, cost-effective, and durable structure, thereby minimizing the overall time-to-market for sustainable automotive products.
Chettiar, Alfred Selvamani, V., Vinothkumar, S., Deepak, Praveen, Kannan
Trajectory Optimization for Non-Cooperative Target Spacecraft Based on Sequential Convex Programming2026-99-233010/30/2026
Autonomous operations near space targets that do not cooperate present great challenges because position and attitude planning must be linked. A target of this type lacks navigation aids or communication systems, which makes relative motion planning much more difficult. Although methods based on convex optimization work well for problems with three degrees of freedom, like planetary landing, their use in integrated operations with six degrees of freedom is limited by constraints that are not convex. To solve this problem, this paper proposes a new framework for servicing spacecraft using sequential convex programming. The primary contribution is the extension of convex methods to high dimensional trajectory planning while adding a robust trust region constraint based on trajectory deviation. This mechanism reduces the chance of finding no solution during convex approximations. It improves reliability and convergence when solving complex problems. The approach first creates a continuous optimal control problem with an index that balances energy use and the angle of the line of sight. Nonlinear dynamics are simplified using first order Taylor expansions. Constraints for the field of view and collision avoidance are modeled using spherical and ellipsoidal regions. The non convex problem is changed into a sequence of discrete convex subproblems and solved through iterations. Numerical simulations show that the servicing spacecraft can safely approach the target with stable attitude while meeting all physical limits. The method using sequential convex programming with a trust region demonstrates great potential in optimizing computational efficiency alongside strong numerical stability, laying a solid foundation for future real-time autonomous trajectory generation in complex orbital environments.
Wang, Yuzhi, Yao, Zihao, Wang, Qian, Han, Ziwen, Zhang, Chao, Zhang, Yumei, Li, Shunli
Numerical Simulation and Geometric Parameter Optimization of Solid Propellant Single-Screw Extrusion2026-99-236010/30/2026
For the single-screw extrusion process of solid propellant, the geometric optimization of the homogenization section faces a core problem: the high coupling between pressure establishment, material transport and energy consumption, and it is difficult to achieve a balance between different performance indicators by traditional empirical methods. Therefore, a numerical optimization framework combining CFD simulation and genetic algorithm is constructed to systematically explore the optimal screw geometric configuration. Propellant slurries are regarded as non-Newtonian fluids and are described by the Arrhenius temperature-dependent Bird-Carreau model, on which the flow field, pressure field and temperature field are solved. The key geometric parameters such as outlet core diameter, pitch, front and rear wedge angle, and thread width are selected as the optimization variables, and the ratio of pump power to shaft power is used as the objective function of energy efficiency to measure the degree of mechanical energy conversion into effective pressure work, and thermal safety constraints are introduced to control the upper limit of propellant temperature. The optimization results show that compared with the initial configuration, the volume flow rate is increased by 123% and the energy efficiency is only slightly reduced under the premise of maintaining a stable pressure drop. The flow field analysis shows that the optimized structure enhances the resistance-led conveying capacity and effectively inhibits the backflow phenomenon. Further analysis shows that the design with a larger pitch and narrower thread can reduce energy consumption while increasing the output, but its pressure accumulation ability is significantly weakened, revealing the inherent trade-off between screw geometry and pressure generation, conveying efficiency and energy consumption. The resulting optimized configuration achieves a good balance between multiple properties, which provides a theoretical reference for the continuous extrusion process of high-performance solid propellants.
Tang, Zhuang, Jiang, Ye, Mei, Mei, Wu, Wei-Tao
Optimization Method for UAV Scheduling Based on Beidou2026-99-235210/30/2026
Currently, UAVs are increasingly used in remote sensing monitoring, logistics and distribution, emergency rescue and recreational aerial photography, etc., which puts higher requirements on wireless networks, i.e., how to allocate limited wireless resources in real-time, efficiently and fairly to ensure that a large number of UAVs can work together in complex environments and maintain high-quality communication services. This paper investigates an optimisation method for UAV scheduling based on the BeiDou satellite navigation system, which improves the accuracy and efficiency of UAV mission execution by utilising the high-precision positioning and time synchronisation services provided by the BeiDou system. The results of the experiment showed that as the number of missions increased from 10 to 50, the average mission completion time increased from 8.3 to 12.5 minutes, the average speed increased from 30 to 34 km/h, the average energy consumption increased from 0.8 to 1.2 kWh, the average distance flown increased from 12 to 22 km, the communication success rate decreased slightly from 99.2 to 98.2%, the resource utilization rate increased from 85.1% to 87.5%. These results show that the method performs well in terms of task completion time, energy consumption, communication success rate and resource utilisation, which provides strong support for the application of UAVs in the fields of logistics and distribution, emergency rescue, and so on.
Ji, Hongbin, Jiang, Kexin, Zhang, Dongxue, Bai, Yanhui, Liu, He, Zhang, Shuangna
A Study on Energy Management Strategies for Range-Extended UAVs Based on A-ECMS2026-99-233310/30/2026
An adaptive equivalent consumption minimization strategy (A-ECMS) is proposed for a hydrogen fuel-cell range-extended UAV hybrid power system composed of a PEM fuel cell, a lithium-ion battery and a supercapacitor. The equivalent factor is updated online by a PID-based SOC feedback mechanism to improve the adaptability of conventional ECMS under variable flight loads. Under the same mission profile and the same initial and terminal SOC constraints, A-ECMS consumes 4.6277 g of hydrogen, reducing hydrogen consumption by 46.90%, 67.51% and 58.42% compared with PID control, conventional ECMS and state-machine control, respectively. Fuel-cell power fluctuation and battery SOC deviation are also reduced.
Li, Mengda, Liu, Zitong
Fine-tuned Large Language Model Enhanced Nonlinear Model Predictive Control for Spacecraft Attitude Stabilization2026-99-296610/19/2026
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Guo, Zili, Gao, Sihong, Liu, Yuhan, Wang, Pengyu, Guo, Yanning
The Emerging Strategies of Energy Management System for Fuel Cell Hybrid Aircraft in the Low-Altitude Economy2026-99-291910/19/2026
The low-altitude economy has been established as a strategic emerging industry, driving the development of various new aircraft types, including electric vertical take-off and landing (eVTOL) vehicles and industrial unmanned aerial vehicles (UAVs). In this development, proton exchange membrane fuel cell (PEMFC)/lithium-battery hybrid propulsion systems face challenges in balancing fuel economy, stack durability, and implementability under stochastic aerodynamic loads. Six energy management strategies (EMS) are systematically evaluated in this study, namely Thermostat, Fuzzy Logic, State Machine (SM-EMS), Power Following (PF-EMS), Dynamic Programming (DP-EMS), and a proposed load-disturbance-based method (LD-EMS). These strategies are tested on three distinct platforms, including a 2 kW fully simulated system, a 2 kW hardware-based setup incorporating a real PEMFC stack and Li-FePO4 battery, and a 30 kW hybrid testbed. Experiments were conducted under synthetic stochastic load profiles representative of gust-induced power transients. The results indicate that LD-EMS achieves a total equivalent hydrogen consumption of 2.71 g (close to the DP-EMS optimum of 2.64 g and 19 % lower than SM-EMS) while reducing fuel cell state-of-health degradation to only 0.0005 % over the test cycle, which is an order of magnitude better than PF-EMS and SM-EMS. On the 30 kW platform under two distinct challenging profiles, LD-EMS maintains fuel cell efficiency above 90 %, keeps the stack operating in high-efficiency zone, and limits power switching events to 97 and 212 respectively over test cycles, demonstrating robust scalability and transient suppression capability without requiring prior mission knowledge. A five-dimensional evaluation framework is introduced to compare the six EMS. In accordance with this framework, platform-specific selection guidelines are established. Thermostat EMS is recommended for small industrial UAVs where hardware simplicity dominates; LD-EMS is the optimal choice for passenger eVTOL under unpredictable gust conditions; and SM-EMS is preferred for commuter/regional aircraft operating on fixed, repetitive routes.
Gao, Zibo, Chigan, Dongdong, Xiao, Yizhuo, Li, Yuansheng, Ji, Guoming, Han, Qing, Li, Zhi-peng
Emergence and Regulation of Scale-Free Correlation in UAV Swarm under Communication Energy Constraints2026-99-298310/19/2026
This paper studies how scale-free correlations, a key feature for efficient group response, emerge in collective motion under communication energy constraints. A two-dimensional Boids model integrated with a point-to-point wireless communication energy consumption model is used. The model dynamically determines, based on a limited communication power budget, an individual’s interactive neighbors, where neighbors are selected based on two different mechanisms: distance-based and probability-based, until the total communication cost reaches the budget. Simulations of flock sizes ranging from 30 to 230 individuals confirm that scale-free correlation persists, even under strict power constraints, which is shown by a correlation length ξ proportional to the flock’s linear size L. Notably, the ratio ξ/L, which reflects the robustness of collective correlation, displays a non-monotonic dependence on the available communication power. Initially, increasing power enhances ξ/L by expanding effective interaction ranges and improving information transmission. However, beyond a threshold, further power increase causes a slow drop in ξ/L, likely due to signal interference from excessively many or distant connections. This finding demonstrates that communication power is a critical, tunable engineering parameter regulating collective coordination, analogous to interaction range or neighbor number in biological models. The results imply that for engineered swarms, maximizing communication resources is not always beneficial; instead, an optimal power allocation exists to maximize coordination performance, balancing connectivity with efficient, localized interactions.
Zhao, Shiping, Gan, Qingzhong, Liu, Jinglei, Liu, Zhihao, Zhang, Liang
Performance and Scheme Study of the Hybrid Engine Integrated PEMFC with Jet Engine for Commuter Aircraft2026-99-293410/19/2026
To support aviation decarbonization and improve overall propulsion efficiency, this paper proposes a hybrid propulsion architecture integrating a proton exchange membrane fuel cell (PEMFC) with a turbofan engine. The proposed parallel hybrid configuration achieves efficient energy conversion through the thermodynamic coupling of key components, including the heat exchanger, ducted fan, compressor, and combustor. A thermodynamic model is developed for parameter matching and system analysis. Results indicate that, at the design point, the overall system efficiency reaches 0.36, with a thermal efficiency of 0.42 and a propulsive efficiency of 0.82. Compared with the baseline CFM56-3C1 engine, the proposed system shows improved fuel economy and environmental performance, achieving a specific fuel consumption of 5.8 g/(s·kN), NOx emissions of 0.0063 g/kg-fuel, and a noise level of 92 dB. Exergy analysis further indicates that the primary exergy destruction occurs within the fuel cell (20%) and during combustor heat transfer (18%). These findings suggest that multi-energy pathway coupling can reduce fuel consumption and emissions, providing a potential approach for low-carbon aviation propulsion systems.
Huang, Mengxuan, Ji, Zhixing, Huang, Jiao, Xu, Zibo, Yao, Yunan, Cao, Zhipeng, Wang, Zhanxue
Design of Closed-Loop Self-Excited Drive Circuit for Resonant Accelerometers2026-99-297010/19/2026
Resonant MEMS accelerometers have important application value in the field of high-precision inertial detection due to their excellent characteristics such as quasi-digital output, high resolution and low power consumption. The drive loop and interface circuit are the core factors determining their detection accuracy and operational stability. This paper deeply investigates the force-frequency sensitive mechanism of resonant accelerometers, establishes a mass-spring-damping dynamic model and an RLC equivalent electrical model, and completes the full-process design of the closed-loop self-excited drive loop and interface ASIC chip based on 0.18 μm CMOS technology. Key modules including a high-gain operational amplifier, an integrated static error detection circuit with full-wave rectification and PI control, a fully differential nonlinear multiplier, a low-noise charge amplifier, and a low temperature coefficient bandgap reference are designed and optimized. A closed-loop control system is constructed to realize rapid start-up and constant-amplitude oscillation of the resonant beam. Simulation results show that the temperature drift of the bandgap reference circuit is 4.6482 ppm/°C over the full temperature range; the charge amplifier achieves a gain of 102.128 dB with a low-frequency thermal noise of 10.33 nV; the drive loop can be stably established within 100 ms, and the output fluctuation amplitude of the PI controller is 284.6 μV; The total current of the circuit is 2.44 mA, and the power consumption is 12.2 mW. The interface circuit designed in this paper has the advantages of low noise, high integration and high precision, which meets the high-performance application requirements of resonant MEMS accelerometers and provides reliable technical support for the engineering implementation of the device.
Tong, Jianghao, Liu, Guowen, Zhang, Qiukun, Li, Zhaohan, Gao, Naikun, Zhang, Wenbo, Yin, Liang, Fu, Qiang
Latency-Aware Optimal Scheduling Scheme for Computational Offloading12-10-01-000210/9/2026
Proactive safety and vehicle automation typically requires high energy use from sensors and energy-intensive computing from sensor data processing. For high-quality, reliable perception and localization within a driving environment at the vehicle level, incoming data from multiple sensors need to be fused using advanced computational algorithms, which demand a high compute load. Alternatively, computational offloading of automated driving tasks shifts energy consumption from the vehicle to cloud infrastructure, where renewable energy sources, such as hydropower or solar power, can be utilized more efficiently. Herein, an optimal scheduling strategy for autonomous driving tasks via the cloud layer is formulated as a mixed-integer linear programming (MILP) problem and verified using measurement-informed task graphs. It is shown that cloud-based computational offloading enables energy-efficient operation while maintaining task deadlines to ensure the timely availability of perception and localization information, which is consistent with prior studies in the literature. Simulation results demonstrate that on average, 35.78% of the computational load was offloaded to the cloud, which can achieve significant energy savings for the onboard system. The compute operations achieved an average energy savings of 35.65%, while total system savings ranged from 26.16% to 30.67% under different cloud energy efficiency scenarios, highlighting the advantages of offloading compute-intensive tasks. The framework was further evaluated using multiple directed acyclic graph (DAG) configurations to assess its scalability and adaptability. Critical tasks, such as sensor fusion, were executed exclusively on the vehicle to ensure real-time responsiveness.
Sharma, Sachin, Meyer, Richard, Feinberg, Ben, Asher, Zachary
Aerospace & Defense Technology: Oct 202626AERP1010/8/2026
Terabit Optical Communications in Space: Engineering High-Speed Networks for Data Centers in Orbit Mild Hybrids: A Current-Gen Bridge to Electrifying Military Ground Vehicles The Integrated and Flexible Approach to Large-Scale Metrology: How Integrating Laser Trackers and Modular Arms Creates a 'Unified Nervous System' The Defense Industry Continues to Meet CMMC Phase II Requirements Despite 60-Day Pause In Space, No One Can Hear You Stream AMD to Bring UCIe Connectivity to Select Versal Adaptive SoCs How Special Interface Gages Provide Microscopic Precision for Designing Ballistic Missiles The pristine precision needed to certify the nation's underwater nuclear deterrent is anchored on the talents of civilian scientists at a Navy laboratory in Southern California. Rapidly Testing Deployable Systems Sandia National Laboratories is evaluating portable security architectures that combine radar, drones, ground sensors, cameras and wireless communications networks into a single rapidly deployable system for remote and austere environments. Testing focuses on sensor integration, resilient communications and edge-based data processing to improve situational awareness while operating within real-world bandwidth constraints. Tailored Surface Vibrations to Improve Aerodynamic Performance in Passenger Jets University of Colorado Boulder researchers have demonstrated two new phononic flow-control concepts, super-resonance and scatterless interference, that use engineered subsurface vibrations to interact with boundary-layer airflow across a broader range of turbulence frequencies than previously possible. By delaying flow instabilities without altering a wing's external shape, the technology could reduce aerodynamic drag and improve fuel efficiency in passenger aircraft. New Type of Attack Can Slip Past the Defenses in Your Computer's Processor MIT researchers found a way to exploit a split-second gap in chip security - and used it to acquire a Linux system's password file. Army Tests Safer Electrical Connectors for Ground Vehicle Readiness U.S. Army DEVCOM Ground Vehicle Systems Center engineers are evaluating cadmium- and hexavalent chromium-free electrical connectors designed to reduce hazardous material exposure while maintaining corrosion protection, electrical performance, and long-term vehicle sustainment.
Importance of Duty Cycle Variability in Estimating Total Cost of Ownership for Vocational Battery Electric and Fuel Cell Vehicles13-07-02-000710/7/2026
Total Cost of Ownership (TCO) is a key metric in commercial vehicle purchase decisions and evaluation of advanced technologies such as battery electric and fuel cell vehicles (BEVs and FCEVs). Inputs derived from aggregate operating data, such as annual miles of travel and average fuel efficiency, as used in most TCO tools, may be inadequate for assessing technologies in specific applications. To provide more accurate estimates, the Transportation Technology Total Cost of Ownership (T3CO) model integrates vehicle simulation using the Future Automotive Systems Technology Simulator (FASTSim™). T3CO incorporates opportunity costs that may arise when technologies do not provide equivalent performance to conventional powertrains, such as reduced payload capacity and added downtime for more frequent and longer duration refueling. While past analyses used single duty cycles representative of average daily distance and energy per mile, fueling dwell time is nonlinear with respect to daily energy requirements and may not be captured with representative cycles. In this study, T3CO is applied to real-world operational data collected over 32 months from conventional Class 5 work trucks in three fleets, covering a variety of use cases and variability in daily demands. Diesel, BEV, and FCEV powertrains were simulated over the full data set, for a total of 1,986 vehicle days and 145,919 miles each. Analysis of duty cycle variability results in up to a 61% difference in fleet-level weighted average TCO compared to analysis over a single representative day. While diesel and FCEV TCO results are sensitive to fuel cost, BEV TCO is sensitive to the downtime from occasional daytime charging present in the total life cycle and not reflected in a subset “representative day.” For the studied fleets, these findings alter conclusions about the relative cost-effectiveness of new technology options at both the vehicle and fleet levels.
Birky, Alicia, Panneer Selvam, Harish, Sigler, Cory, Carow, Kyle, Miller, Eric, Ortmann, Walt, Tascillo, Mark
Thermoelectric Generators for Battery Charging: Waste Heat Utilization with Minimized Back Pressure13-07-02-000910/1/2026
Waste heat recovery has become a critical research area in the quest for improving automotive energy efficiency. Internal combustion engines lose a large amount of their energy as heat through exhaust gases. Thermoelectric generation technology presents a promising approach to capturing and converting this waste heat into useful electrical energy. In this work, a theoretical study on the application of thermoelectric generators (TEGs) for battery charging by converting waste heat from internal combustion engine exhaust into usable electrical energy is proposed. A prototype system incorporating TEG modules was designed focusing on the minimization of back pressure; flow of exhaust gas is ensured through a circular internal cross-section. Thermal simulations were performed using Ansys Workbench, and computational fluid dynamics (CFD) analysis was conducted to quantify the back pressure. Two heat exchanger materials, aluminum and copper alloys, were evaluated for their heat transfer performance. The results indicate that copper achieves superior heat transfer, with hot-side temperatures approximately 8.6% to 23.9% higher than aluminum. However, aluminum remains a viable alternative due to its lightweight and cost-effectiveness. With the cold side being maintained at 91°C to simulate realistic engine coolant conditions, the experiment shows that a series-parallel configuration of six TEG modules (three in series × two in parallel) can effectively generate the necessary voltage (13–15 V) and current (3.5–7 A) to charge a 12-V automotive battery. CFD analysis confirmed that the circular internal geometry produces low back pressure, with pressure drops of 28 Pa, 63 Pa, and 168 Pa for inlet velocities of 25 m/s, 40 m/s, and 70 m/s, respectively. This research underscores the potential of TEG-based battery charging systems in enhancing energy efficiency, though further development is required for real-world automotive integration. Future work could focus on on-vehicle testing, optimizing thermoelectric materials, and integrating advanced cooling mechanisms and maximum power point tracking controllers to improve overall system viability.
Satheesh, Amal, Satheesh, Akul, Pillai, Ashwin S., Mahisankar, J.S., Sreejith, B.J.
Time-Dependent Power Flow Modeling for Non-Passive Loads in Battery Electric Propulsion Architectures2026-01-75639/22/2026
Traditional Linear Circuit Analysis (LCA) relies on steady-state voltage assumptions that are fundamentally incompatible with battery-exclusive propulsion architecture. Whilst LCA remains valid for hybrid systems, where an auxiliary source actively regulates the State-of-Charge (SOC), it fails when analyzing non-passive, constant-power loads. In pure electric architecture, the time-dependent decay of the discharge voltage forces a continuous, non-linear increase in current to satisfy mechanical power demands. A time-dependent power flow methodology is introduced to resolve this theoretical divergence. Using the conservation of energy operating explicitly within the power domain, the time-dependent coupling of current and voltage can be modeled. This approach supersedes steady-state approximations for higher fidelity predictions for component efficiency, system heat generation, and battery capacity requirements for pure electric propulsion systems. Citation: N. Ingarra, K. J. Kobus, J. G. Kobus “TIME-DEPENDENT POWER FLOW MODELING FOR NONPASSIVE LOADS IN BATTERY ELECTRIC PROPULSION ARCHITECTURES” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Ingarra, Nicholas A., Kobus, Krzysztof (Chris) J., Kobus, Jadon G.
Experimental Comparison Between Real and Simulated Battery Pack Efficiency in Light Quadricycles Using a Custom Inertia Dynamometer2026-24-00089/21/2026
This study compares the energy efficiency of a real battery pack and a simulated battery pack using a hardware-in-the-loop battery emulator, through experimental testing on a dedicated inertia dynamometer for light quadricycles. The investigated LiFePo4 battery pack has a nominal voltage of 48 V and a nominal capacity of 100 Ah. Initial characterization identified a reduced State of Health based on capacity (SOHC), with a measured usable capacity of 48 Ah (from the nominal 100 Ah) and a corresponding reduction in charge acceptance capability. The emulator was configured to replicate the degraded battery characteristics, including the open-circuit voltage (OCV)-SOC relationship, internal resistance, and current limitations, enabling a direct comparison between simulated and experimental dynamic behavior. The experimental setup was designed to overcome the limitations of conventional chassis dynamometers for low-mass, independent four-wheel-drive quadricycles operating without mechanical friction braking systems. Multiple driving cycles were reproduced using a PLC-based closed-loop control system, with data acquisition performed via CAN communication. The comparative analysis highlights significant differences during regenerative braking events. While the emulator accurately reproduces baseline electrical behavior under mild operating conditions, the aged battery exhibits strong limitations during both high-power acceleration and severe deceleration phases. In particular, increased internal resistance and transient electrochemical polarization lead to premature saturation of charge acceptance, resulting in rejection of high-frequency current transients. Consequently, the experimentally observed energy recovery is significantly lower than the theoretical values predicted by the emulator. In addition, due to the absence of mechanical braking, the reduced regenerative capability directly leads to speed tracking deviations, as the required braking torque cannot be fully achieved. These results identify battery degradation as a key physical constraint affecting both energy efficiency and dynamic braking performance, highlighting the importance of improved electro-thermal and aging-aware model calibration for realistic system-level simulations.
Sementa, Paolo, Vaglieco, Bianca Maria, Altieri, Nunzio
Data-Driven System Dynamics and Chauvenet-Based Outlier Detection for Assessing Global CO₂ Emissions under Fossil Energy and Electric Vehicle Transition Scenarios2026-24-00209/21/2026
Understanding the structural drivers of global CO₂ emissions requires integrated analysis of fossil fuel production, total energy consumption, and electric vehicle (EV) deployment trends. This study presents a data-driven modeling framework combining system-dynamics formulation with statistical outlier detection implemented in Python to evaluate emission trajectories over a ten-year historical period. The methodology incorporates historical datasets of global CO₂ emissions, primary energy consumption, fossil fuel production, and EV manufacturing volumes. A computational routine developed in Python applies the criterion proposed by William Chauvenet to identify statistically inconsistent observations within the dataset, ensuring robustness prior to regression and correlation analyses. Carbon intensity (CO₂ per unit of energy) is calculated to assess decoupling behavior, while correlation matrices and elasticity indicators quantify the relative influence of fossil production and EV penetration on emissions. The dynamic structure expresses CO₂ emissions as a function of fossil energy share, total energy demand growth, and electrification rate. Sensitivity analysis evaluates the responsiveness of emissions to variations in these parameters. Results indicate that emission reductions are strongly dependent on carbon intensity evolution rather than EV growth alone. Outlier detection enhances model reliability by preventing anomalous years from biasing trend interpretation. The proposed framework provides a transparent and computationally efficient tool for emission diagnostics, transition scenario evaluation, and policy-oriented forecasting within the context of sustainable mobility and global energy transformation.
Gutierrez, Marcos, Taco, Diana
Optimal Eco-Driving for Electric and Conventional Refuse Trucks: A Comparative Analysis of Actual Speed, Energy Efficiency, and Travel Time2026-24-00319/21/2026
Achieving significant reductions in energy consumption and CO₂ emissions in the transportation sector is a key challenge for sustainable mobility, particularly for vocational trucks operating under demanding driving and duty cycles. Beyond technological advancements in powertrain design, energy efficiency can be improved through optimized driving strategies. In this context, eco-driving has emerged as an effective approach to reduce energy consumption by optimizing the speed profile under given operational constraints. Eco-driving optimization strategies are particularly well suited for predefined and repetitive driving cycles, such as those typically encountered in waste collection. This work presents a comparative analysis between electric and internal combustion engine powertrain configurations applied to refuse trucks and vehicles, highlighting the impact of intrinsic differences on optimal speed profiles, energy consumption, and travel time. Both configurations are required to follow identical routes characterized by speed limits, mandatory stops, and comfort-related constraints. To address the optimization problem, the conventional longitudinal dynamics equations are reformulated from a time speed domain to a spatial speed domain through spatial discretization. The spatial domain is subsequently discretized, enabling the formulation of an optimization problem solved using dynamic programming to determine the optimal speed profile along real-world routes. The optimization framework is based on the minimization of a cost function composed of two main terms, normalized energy consumption and travel time variation, and a shape factor introduced to balance the tradeoff between energy efficiency and travel time. The objective of the study is to compare the two powertrain configurations under identical routes and constraints, analyzing energy consumption and travel time, and to identify the optimal compromise between time and energy consumption for each case. The results provide insights into the effectiveness of eco-driving strategies and underline the influence of powertrain architecture on optimal driving behavior.
Giacobbo, Andrea, Beltrami, Daniele, Villani, Manfredi, Tribioli, Laura, Iora, Paolo, Uberti, Stefano
Use-Phase Emissions Comparison of Battery Electric and Internal Combustion Engine Vehicles in Canada2026-01-50749/17/2026
The objective of this study was to evaluate the in-use emissions and energy consumption of similar model internal combustion engine (ICE) and battery electric vehicles (BEVs) in Canada. For the ICE vehicles (ICEVs), carbon dioxide (CO2) emissions were measured at the tailpipe. For the BEVs, the carbon intensity of different energy sources was used along with vehicle energy consumption to estimate the in-use CO2 equivalent (CO2e) emissions. Three ICEVs, the Ford Transit, Ford F-150, and Nissan Versa, and three BEVs, the Ford E-Transit, Ford F-150 Lightning, and Nissan LEAF, were tested over standard test cycles on a chassis dynamometer. The Nissan Versa, Nissan LEAF, Ford F-150, and Ford F-150 Lightning were tested at two temperatures, 25°C and −7°C, to investigate the effect of colder temperatures on emissions and energy consumption. The Ford Transit 150 and E-Transit were tested at two test weights, 2722 kg (6000 lb) and 3629 kg (8000 lb), to study the effects of cargo loading on emissions and energy consumption. In most conditions, the BEV use-phase CO2e emissions were found to be lower than those of the ICEVs. Results showed a significant increase in both emissions in ICEVs (up to 20%) and energy consumption in BEVs (up to 78.5%) at −7°C when compared to 25°C. Results also showed the significant effect of the carbon intensity of electricity on the CO2e emissions of BEVs, where more carbon-intensive electricity grids resulted in higher BEV CO2e emissions, even surpassing ICEV CO2 emissions in certain cold-temperature conditions.
Araji, Fadi, Humphries, Kieran, Hornung, Jeremy, Shantz, Emory
Design Considerations for Optimizing the Wheel Bearing Outer Ring Bolt-Mounting Pattern for Reduced Drag2026-01-08309/14/2026
Electric vehicles (EVs) impose more demanding operating conditions on wheel bearing systems due to increased vehicle mass, higher drive torque, and the need to maximize energy efficiency and driving range. These factors elevate the loads transmitted through the bearing to knuckle joint and often require higher clamp loads to ensure joint integrity. However, higher clamp loads amplify distortion of the wheel bearing outer ring, increasing rotational drag and reducing bearing durability. Controlling outer ring distortion is therefore critical for EV wheel bearing design, as well as for high performance vehicles that experience severe lateral loads at the hub to knuckle interface. This paper investigates key design considerations for optimizing the wheel bearing outer ring and its mounting interface to minimize distortion under elevated clamp loads. A comprehensive CAE-based Design of Experiments (DOE) is used to evaluate the influence of multiple bolt-mounting patterns including rectangular, square, and trapezoidal configurations and the relative alignment of the bolt pattern between the outer ring and knuckle. The study also compares the performance of M12 and M14 fastener variants across loading conditions representative of EV and high-performance applications. The results identify geometric and interface design parameters that significantly reduce outer ring out of roundness, thereby lowering drag torque and improving long-term bearing life.
Mandhadi, Chaitanya Reddy, Lee, Seungpyo, Bovee, Benjamin, Callaghan, Kevin
Comparing the Economic Performance of Series-Parallel and Range-Extended Hybrid Vehicles2026-99-15699/11/2026
This study conducted a comprehensive economic evaluation of two major HEV architectures: the series-parallel configuration and the range-extended configuration. An analysis of these two configurations was performed using integrated vehicle and control models, allowing for a direct comparison of energy efficiency and operational economy. Findings show that the range-extended configuration has clear advantages in structural complexity, simplicity of control strategy, and development cost, while its energy consumption performance is similar to that of the series-parallel configuration. The results challenge the long-standing notion that range-extended configuration is less efficient, offering a new perspective on the design and configuration choices for hybrid electric vehicles.
Li, Ping, Guo, Wencui, Nie, Guole, Niu, Yazhuo, Bai, Bateer
Track De-icing and Road Maintenance Method Based on Induction Heating Technology2026-99-15369/11/2026
In order to solve the problems of long preheating time and high energy consumption caused by the traditional resistance heating track deicing technology and large power supply load, this paper presents a track deicing and road maintenance method based on electromagnetic induction heating. This paper studies the operational bottlenecks of the resistance heating system in a certain marshalling yard. It explains the eddy current heating from the principle of electromagnetic induction heating and designs an appropriate coil for turnouts and derives its power calculation formula. A mathematical model for the 'solid-liquid' phase transformation in the melting of snow is created and the melting parameter α = 0.623. In comparative experiments, compared to a 4kW electromagnetic induction heating device with no preheating time which melts snow up to 5cm in 40 minutes and consumes 40kW · h of energy in 1 hour, a 13kW resistance heating device needs 150 minutes to preheat, 180 minutes to melt, and consumes 130kW · h of energy. Use 70% less energy and help with track and road de-icing and upkeep with this technology.
Song, Zongying, Li, Zhongming, Wei, Dong, Yang, Jin, Wang, Xingzhong, Liu, Jingwei, Zhang, Xiaoyu
Experimental Analysis of Tube Temperature and Heat Transfer Performance of L-Shaped Gravity Heat Pipes2026-99-15119/11/2026
Gravity heat pipes achieve efficient energy transfer through the evaporation and condensation of their internal working fluid, which steadily conducts underground heat to the surface and thereby provides a continuous and stable heat source for road pavements in winter. Considering the snow and ice melting demand of road surfaces in winter, this paper establishes an indoor environmental simulation experimental platform to systematically investigate the influence laws of different working fluids on the start-up temperature, start-up pressure, heat transfer power, and other key performance indicators of L-shaped gravity heat pipes. Through experimental research and analysis, it is revealed that heat pipes with R-134a and R245fa working fluids can operate stably at a shallow geothermal temperature of about 25 °C, while the acetone working fluid heat pipe operates unstably under this condition. The heat pipe filled with R-134a working fluid achieves the maximum heat transfer power under shallow geothermal conditions, followed by the heat pipe filled with R245fa. Although the heat transfer power of the acetone-filled heat pipe is generally relatively low, its heat transfer power increases most significantly with the rise of the evaporation section temperature. Under low-temperature conditions, the thermal conductivity of the evaporation section increases with the rise in the heating temperature of the evaporation section, while that of the condensation section decreases with the increase in the heating temperature of the evaporation section. Through experimental research and comparative analysis, this paper deeply explores the application potential of gravity heat pipe technology in green highway construction, and evaluates the feasibility and economic benefits of its engineering implementation, which provides a scientific basis and engineering guidance for the selection of green energy in future infrastructure construction.
Wang, Zhen-kun, Yuan, Zhi-ming, Wang, Kang, Zhang, Wen-jun, Wu, Xiang-song, Liu, Guang-bo
Analysis of Differences Between the Current CLTC and China’s Current Traffic Operation Characteristics2026-99-15419/11/2026
With the significant increase in the ownership and market share of new energy vehicles, the current characteristics of China’s traffic operation have undergone remarkable changes compared with those before 2020. This paper focuses on a systematic study of the differences between the current China Light-duty Vehicle Test Cycle (CLTC) and the current traffic operation characteristics. Firstly, the data are derived from the actual on-road operation data of nearly 400 new energy vehicles collected during 2020-2025. Based on this, a comparative analysis framework is established from two dimensions: differences in variable characteristics and differences in test energy consumption. The results show that due to the substantial rise in new energy vehicle ownership and market share, the maximum speed on roads has increased significantly, and the acceleration and deceleration have become more intense. The significant changes in traffic operation characteristics have further widened the deviation between the energy consumption tested under the existing CLTC and the actual energy consumption. Comprehensive research indicates that the increased market penetration of new energy vehicles has brought about obvious changes to the traffic operation characteristics formed during the era dominated by traditional fuel vehicles. Therefore, launching a new round of revision work on the CLTC is of great practical significance for promoting the high-quality development of the new energy vehicle industry in the future.
Yu, Hanzhengnan, Cao, Xiaofei, Zhang, Hao, Yi, Junyu, Zhang, Yongren, Wang, Yang, Wang, Chuanjin, Liu, Te, Ma, Dehui
Research on an Evaluation Method and Application of Autonomous Vehicles in Traffic Flow Environment2026-99-15629/11/2026
Traffic flow environment testing is indispensable in the research and evaluation process of intelligent vehicles. However, the current vehicle evaluation systems mostly focus on simple dynamic scenarios and lack a comprehensive assessment of vehicle performance in complex traffic flow environments. In view of this, this paper constructs a multi-dimensional comprehensive performance evaluation system for vehicles in traffic flow environments. Firstly, based on the randomness and dynamics of traffic flow, four core evaluation dimensions covering safety, comfort, efficiency, and economy are constructed. In terms of security, the collision time and Predicted Safety Metrics are adopted. This enables the dual quantification of immediate collision risks and dynamic obstacle avoidance capabilities. The comfort evaluation focuses on the impact of vibration frequency on the human body and constructs a graded quantitative index. Efficiency and economy evaluation models are built through time cost and energy consumption cost. The verification is carried out under different traffic flow scenarios, and the final results demonstrate the consistency between the evaluation method of this paper and the expert evaluation method, thereby verifying the rationality of the evaluation system presented in this paper.
Wang, Guangyu, Song, Shiping, Zhang, Cheng, Qu, Ge, Yu, Xiaojun
Application and Research of Steam Compressors in Heat Supply of Coal-Fired Power Plants2026-99-12419/4/2026
The application of steam compressors in heating supply of coal-fired power units demonstrates significant energy-saving benefits, while also effectively addressing the operational constraints of steam supply at low loads, thereby achieving thermoelectric decoupling.. This study analyzes and compares the performance characteristics and operational ranges of three distinct mechanical steam compression technologies. Although centrifugal compressors exhibit a low single-stage pressure ratio, their operational flow rate and temperature ranges are better suited for coal-fired heating requirements. Three typical application scenarios of steam compressors in industrial steam supply for coal-fired power units are proposed: Steam compressors enable waste heat recovery by compressing turbine exhaust steam for heating purposes, increase pressure in branch networks of steam supply pipelines to achieve high-pressure steam delivery, and enhance industrial steam extraction pressure under low-load conditions to realize thermoelectric decoupling. This enables substantial improvement in both the energy efficiency and steam supply capacity of coal-fired power units. Based on practical application cases of steam compressor systems, the objectives of steam pressure and temperature elevation can be achieved. Through collaborative adjustment of the drive motor frequency and the recirculation valve, dual-objective control of outlet flow rate and temperature can be realized. An analytical model was developed to simulate compressor performance, and validation against actual operational data confirmed the model’s applicability for guiding operational practices and control simulations. The centrifugal steam compressor in actual operation demonstrates normal performance across all operational indicators under high-speed conditions, confirming its technical reliability in coal-fired heating applications and providing crucial references for the widespread adoption of steam compressor technology in steam supply systems of coal power units. It expands the steam supply parameter range of electro-thermal conversion systems, thereby making a positive contribution to renewable energy integration.
Zhang, Pan, Liu, Yan
Assessment of Heat Transfer Correlations for Shell-and-Tube Heat Exchangers under Periodically Varying Exhaust Flow03-19-05-00229/2/2026
Waste heat recovery from internal combustion engines (ICEs) is one potential option to improve overall vehicle efficiency. Rankine cycles based on engine coolant and exhaust heat sources have demonstrated their effectiveness in enhancing brake thermal efficiency. Critical to their success is the design of the heat exchanger for the evaporator, with shell-and-tube heat exchangers (STHEs) a common hardware choice. However, little experimental data exists evaluating STHEs with the pulsating flow encountered in the exhaust of ICEs. In addition, correlations for periodically varying flow do not appear to be used in the modeling of STHEs. To alleviate this limitation, this study combined experiments using a pulsating exhaust heat source from an ICE under low loads at a single engine speed with a one+one-dimensional model to evaluate tube- and shell-side heat transfer correlations for a STHE without baffles. Four working fluids, water, ethylene glycol, propylene glycol, and a 50/50 ethylene glycol–water mixture, were examined. The combined thermodynamic properties of an ethylene glycol–water mixture were the most effective based on an evaluation of heat exchanger effectiveness, overall heat transfer coefficient, exergetic efficiency, and entropy generation. A Pearson correlation analysis identified the inlet working fluid temperature as the parameter most strongly correlated with STHE performance due to its higher enthalpy. From a modeling perspective, the pulsating flow correlation of Al-Haddad and Al-Binally predicted greater heat transfer rates in the STHE. In combination with all shell-side correlations tested, simulations still underpredict performance relative to experimental results. An optimized correlation developed specifically for the geometry of this unbaffled STHE matched the experimental data more closely but likely overpredicted shell-side heat transfer. Monte Carlo uncertainty propagation based on sensor uncertainties showed that the differences in effectiveness and overall heat transfer coefficient exceeded measurement uncertainty. Furthermore, sensitivity analysis demonstrated the importance of accurate thermophysical property values and indicated that the underprediction likely reflects coupled limitations in both tube- and shell-side formulations, with correlations on each side exerting a comparable influence on predicted heat transfer.
Spickler, Bailey, Segares Dominguez, Maria Luisa, McGowan, Raymond, Depcik, Christopher
Dynamic Response and Energy Absorption of a Novel Arc-Shaped Hourglass Re-Entrant Honeycomb under Impact Loading2026-99-04178/28/2026
This study introduces an arc-shaped hourglass re-entrant auxetic honeycomb (AHRH) and examines its impact-induced dynamic response and energy-absorption behavior via finite-element simulations. The conventional re-entrant honeycomb (RH) is adopted as the baseline, and side-by-side simulations are performed at impact speeds of 10, 20, and 30 m/s. The mechanical response of both lattices is assessed through force-displacement characteristics, absorbed-energy histories, and representative deformation modes. Results indicate that the AHRH significantly reduces the initial peak force, prolongs the plateau stage, and exhibits a distinct dual-plateau feature, thereby achieving the desirable crashworthiness mode of “low initial peak-extended plateau-high densification”. Compared with the RH, the AHRH achieves increases of approximately 42.9%-59.7% in total energy absorption and 42.8%-56.1% in specific energy absorption while maintaining nearly identical mass. The enhanced performance arises from the arc-edge geometry, which alleviates local stress concentrations, promotes progressive buckling, and generates multiple plastic hinges. These mechanisms lead to smoother load transfer, avoidance of excessively high initial impact loads, and more efficient crash energy management. Overall, the proposed AHRH structure demonstrates superior energy absorption capacity and deformation stability compared with the conventional RH, providing new insights and practical references for the lightweight design and optimization of advanced protective and crashworthy structures.
Jiang, Zhide, Chen, Long, Yu, Ping
Design, Simulation, and Experimental Evaluation of an R290-Based Dual Secondary Loop Thermal Management System for Electric Vehicles14-15-03-00188/28/2026
The transition toward low global warming potential (GWP) refrigerants, driven by increasingly stringent environmental regulations and carbon reduction targets, has imposed new requirements on thermal management systems (TMSs) for electric vehicles (EVs). These systems must ensure efficient operation across a wide range of ambient conditions while maintaining high energy efficiency and environmental compatibility. Among potential alternatives, R290 (propane) has emerged as a promising natural refrigerant due to its favorable thermophysical properties and low environmental impact. In this study, an R290-based dual secondary loop TMS is proposed and evaluated for wide-temperature-range EV applications. A one-dimensional system model was developed using Dymola and validated through experimental testing on a dedicated performance test bench. TMS performance was investigated under multiple steady-state operating conditions, including high-load cooling, battery fast charging, and low-temperature heating, and benchmarked against a conventional R1234yf-based direct TMS. The results demonstrate that the R290-based dual secondary loop system achieves improved performance compared to a conventional R1234yf direct system, with a coefficient of performance (COP) increase of 4.29% under high-load cooling conditions at 43°C and up to 27.27% under high-load heating conditions at −10°C. Furthermore, under extreme low-temperature conditions (−18°C), the system delivers a heating capacity of 7 kW with a COP of 1.8, demonstrating strong low-temperature adaptability without the need for auxiliary heating. The results confirm that the proposed R290-based dual secondary loop system provides significant advantages in energy efficiency and wide-temperature adaptability, offering a promising solution for next-generation EVTMSs.
Zhang, Yunpeng, Mohammed, Mustafa Mudassir, Gu, Yiliang, Zhou, Guoliang
Study on the Impact of Vehicle Fixation Methods on the Range Testing of Electric Vehicles2026-99-03268/14/2026
The range energy consumption testing of electric vehicles is usually completed in an environment where the environmental chamber and chassis dynamometer are built. The vehicle is bound to the chassis dynamometer to simulate the range performance on a real road, and the vehicle's fixing method is particularly important, as it even affects the test results. In order to investigate the impact of vehicle fixation as a key testing factor on the range test results of electric vehicles, this study conducted comparative experiments using rigid fixation test vehicles at different positions. By relying on a chassis dynamometer to simulate road resistance and following the Chinese Light Vehicle Test Code (CLTC-P), a range test is conducted on the same electric vehicle under strictly controlled environmental conditions. The experiment collected data on endurance mileage, total energy consumption, and segmented energy consumption. By comparing the differences in simulated resistance and electric energy change trends of chassis dynamometer under different binding methods of test vehicles during the test process, the comprehensive energy consumption results were different. The results showed that the rigid fixation at different positions significantly affected the sliding results of the test vehicle chassis dynamometer, leading to differences in the comprehensive endurance energy consumption results. The comprehensive endurance mileage difference reached 24 kilometers, and the comprehensive energy consumption difference reached 4Wh/km. This study reveals potential sources of system bias in laboratory testing and analyzes the impact of vehicle fixation methods on comprehensive range energy consumption results. The research conclusions can provide a theoretical basis and empirical reference for improving the current standards for energy consumption and range testing of electric vehicles, and enhancing the accuracy and reproducibility of test results.
Zhou, Meng, Jiang, Zhijie, Geng, Peilin
A Foundational CFD Validation for an Occupant-Centric HVAC Digital Twin: Paving the Way for Real-Time Control2026-99-10028/7/2026
The heating, ventilation, and air-conditioning (HVAC) systems are one of the main factors that contribute to the building’s energy usage. Achieving an effective balance between reducing energy use and maintaining acceptable thermal comfort is the key challenge in conventional HVAC systems. To overcome this challenge, integrating the occupant-centric controls coupled with digital twins into HVAC systems is another potential technique for this effective balance. For this purpose, computational fluid dynamics (CFD) offers the potential, in combination with other surrogate models for real- time applications to enhance the system's performance further. In general, the CFD is applied to investigate indoor airflow/temperature distributions. These are essential for occupant health, comfort, and energy optimisation for the HVAC design state. The objective of this study is to propose an initial step toward building an occupant-centric HVAC digital twin by validating a CFD model of an office against dense in-situ sensing data. The model has been used to resolve airflow and temperature stratification under conventional HVAC operations, using ANSYS Fluent. The boundary conditions have been derived from measured supply parameters, internal gains, and local weather conditions. The results from this study show that the air velocity and temperature at selected durations follow the same trend with low errors, compared to the sensing and measurement data. The model validation from this study establishes the basis for a weather- aware, occupant-feedback digital twin for larger floorplates and multi-zone systems. To achieve the target of the energy and comfort co-optimisation in Industry 4.0-ready buildings, the future work will focus on surrogate modelling to enable near-real-time inference for closed-loop occupant-centric controls, which will directly support dynamic set-point adjustments and multi-zone system ventilation.
Larpruenrudee, Puchanee, Hellany, Ali, Famakinwa, Tosin, Shrestha, Surendra, Attwater, Roger, Calheiros, Rodrigo Neves
Research on Parameter Matching of Electric Vehicle Powertrain System2026-99-09058/7/2026
This paper focuses on the parameter matching of key components and the improvement of overall vehicle performance for a certain front-wheel drive pure electric vehicle. Firstly, based on the target performance of the vehicle, the rated/peak power, speed, and torque of the permanent magnet synchronous drive motor, as well as the capacity, voltage, and series-parallel scheme of the LiFePO4 power battery, are systematically calculated. Meanwhile, the gear ratio of the transmission system is determined based on the dual constraints of the maximum speed and the maximum gradeability. Subsequently, the vehicle model is built using AVL Cruise, and the maximum speed, 0-100 Km/h acceleration time, maximum gradeability, and NEDC range are simulated and verified under steady-state and transient conditions. The results show that the maximum speed of the prototype vehicle reaches 139 Km/h, the 0-100 Km/h acceleration is 7.98 s, the maximum gradeability is 33.2%, the power consumption per 100 Km is 12.12 KWh, and the range is 485 Km, all of which are superior to the design indicators. The research verifies the rationality of the proposed parameter matching scheme and can provide a theoretical basis and engineering reference for the forward development of the power system of pure electric vehicles of the same level.
He, Yuefan, Zhang, Baoping, Tang, Shujian, Chen, Hanbang, Jin, Biao
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