Journal of Technical Education Science

Analysis of the CC–CV Charging Profile Influence on Electric Vehicle Battery Lifespan

2026-02-24T14:20:47+07:00

Batteries represent a critical and high-value component within modern energy systems, particularly in the domain of electric vehicles (EVs). The operational lifespan of batteries significantly influences overall system performance, reliability, lifecycle costs, and environmental sustainability. Among the various stressors affecting battery health, the charging process is recognized as a predominant contributor to electrochemical degradation. Adverse conditions such as elevated charging rates, non-optimal ambient or cell temperatures, and suboptimal state-of-charge (SoC) windows can accelerate capacity fade and internal resistance growth, thereby diminishing battery longevity. Despite substantial advancements in battery technology, accurately characterizing and quantifying the long-term effects of diverse charging parameters remains a complex challenge, necessitating advanced modeling. In this context, the authors conducted comprehensive simulations to evaluate battery degradation behavior under varying charging profiles and environmental conditions. Based on the simulation outcomes, the study proposes evidence-based strategies to assist EV users in optimizing charging protocols, ultimately enhancing system efficiency, reducing maintenance costs, and prolonging battery service life.

Design, Manufacture of Water Distillation Equipment by Spraying Using Solar Energy

2025-11-10T11:48:07+07:00

Solar energy is considered a renewable energy source and is widely used in industry and life. This article presents the results of research on the design and manufacture of a water distillation device, using solar energy to heat seawater, combined with a spraying flash evaporation to collect fresh water, towards applications in saline intrusion areas. The device includes a solar collector with an area of 2 m², a heating tank with a volume of 15 liters, a spray chamber with dimensions D x H = 52 x 65cm, spray pressure p = 3 bar, a spiral condenser with d = 6mm, l = 5m. The test results show that in the period from 8 am to 4 pm, the radiation intensity was from 300 to 1200 W/m², the amount of fresh water collected was from 0,5 to 1,12 liters/h, the average electricity consumption was 0,433 kWh/liter. The research result suggests that the designing and manufacturing water distillation equipment at temperatures without boiling and combining with renewable energy to save energy.

Toyota Yaris 2012 Structure Durability Simulation by Hypermesh With Optistruct Solver

2026-02-09T08:49:53+07:00

This paper focuses on using HyperMesh software to simulate and evaluate the structural strength of the 2012 Toyota Yaris, utilizing the OptiStruct solver with support from LS-Dyna for FEM modeling. The objective is to research and improve the structural design of this vehicle model and similar sedan types. The simulation process will yield strength assessment results through verification scenarios such as bending, torsion, cornering, and uphill driving under the influence of gravitational forces. According to the result, there are 2 cases among 4 tests that are used to test the structural strength of the chassis did not meet the allowable limit of the overall yield strength and displacement of components. Which is the test of torsional strength and the cornering situation test. From there, the solution that was chosen to solve the lack of strength in the 2 tests is replacing the initial material at the specific components with a stronger material to increase the overall strength and integrity.

Small-Signal Analysis and Control for a Single-Phase Buck – Boost Inverter

2025-12-24T15:34:11+07:00

This paper presents the configuration of a single-phase buck-boost inverter (1P-BBI), which combines a three-level boost converter (TLB) with a conventional single-phase H-bridge buck inverter. Unlike conventional two-stage topologies, the proposed structure does not require a constant DC-link voltage. The 1P-BBI operates in two modes. In the buck mode, when the input DC voltage is higher than the desired output voltage, only the inverter-stage switches are active to generate the AC output. In contrast, in the boost mode, when the input DC voltage is lower than the output voltage, the switches in the three-level boost converter regulate the DC-link voltage to match the required output level. This paper also presents a small-signal analysis of the 1P-BBI system to establish the transfer function that relates the post-filter output voltage to the input DC voltage. Based on the derived transfer function, the parameters of a Proportional–Integral (PI) controller are selected to regulate the output voltage across the load. Additionally, the paper provides detailed analysis of operating states, circuit calculations, and component selection. Simulation results and experimental verification with a purely resistive load are conducted to validate the proposed control strategy. The experimental results confirm that the inverter is suitable for single-phase applications in the low-to-medium power range.

Performance and Reliability, Security of One-Way Duplex Relay Network Using Artificial Noise

2026-01-18T09:39:27+07:00

The authors employ the artificial noise (AN) technique to enhance physical layer security (PLS) in one-way full-duplex (OWFD) networks. Specifically, an OWFD network incorporating AN and operating over Rayleigh fading channels is considered. The system consists of five nodes: a source node, a destination node, a relay node, an eavesdropper node, and a jamming node. To evaluate security performance, the authors analyze and assess several metrics, including the secrecy outage probability (SOP), secrecy throughput (STP), and the trade-off between outage probability (OP) and intercept probability (IP). Closed-form expressions are derived in the paper. The analytical results are validated through Monte Carlo simulations implemented in MATLAB, the results are illustrated through four graphs: the impact of the artificial-noise node’s position on the SOP under variations in the source node’s transmit power; the effect of the eavesdropper’s position; and the influence of the path-loss exponent on the system’s OP, IP, and SOP. In addition, a comparative graph of the SOP between the proposed model and the reference model is presented. All graphs demonstrate a significant improvement in security performance compared with previous studies. The proposed model confirms the feasibility of implementing PLS solutions in OWFD networks.

Simulation of Dispersed Pollutant From Incense Burning Within an Enclosed Space

2026-02-02T08:45:53+07:00

Indoor air pollution is a serious environmental and public health issue, as modern people spend approximately 80-90% of their time in enclosed spaces. In addition to common emission sources, incense burning – a traditional cultural practice, is a significant source of indoor air pollutants, particularly formaldehyde, but has not received adequate attention. This study focuses on evaluating formaldehyde emissions and dispersion from smoke generated by several commonly used incense types in Viet Nam under different mechanical ventilation conditions. Experiments were conducted in a closed chamber with a volume 1 m³, combined with numerical simulations using the finite volume method implemented in Ansys Fluent to validate and analyze pollutant dispersion. Experimental results showed that, under non-ventilated conditions, the average formaldehyde concentration reached 4.163 mg/m³, approximately four times higher than the short-term exposure limit specified in QCVN 03:2019/BYT. Simulation results obtained using the low-Reynolds-number k-ɛ turbulence model showed good agreement with experimental data, with a deviation of about 5%. The study demonstrates the effectiveness of mechanical ventilation and highlights the applicability of CFD modeling in assessing indoor air pollution.

ISSA-PID Optimization Algorithm Design for Mobile Robot Differential Motion Control

2026-02-23T15:39:20+07:00

In recent years, trajectory tracking for differential-drive mobile robots has been widely studied due to the growing demand for applications in logistics, autonomous transportation, and surveillance. Consequently, improving tracking accuracy and ensuring stable operation under disturbances and uncertainties have become important directions for autonomous navigation systems. To address this problem, various control methods have been applied, among which the PID controller remains popular thanks to its simple structure and ease of implementation. However, conventional control schemes often depend heavily on manual parameter tuning and are sensitive to disturbances and model uncertainties; moreover, their performance may deteriorate in the presence of actuator saturation and wheel slip, leading to oscillations and the integral windup phenomenon. Based on these considerations, this paper proposes a control strategy that combines an improved PID controller with an Improved Sparrow Search Algorithm (ISSA) to optimize the PID parameters for trajectory tracking. The effectiveness of the proposed method is validated through simulations on a figure-eight trajectory and evaluated using metrics such as RMSE, maximum tracking error, oscillation level, and control effort. The results demonstrate that the proposed PID–ISSA approach improves both tracking accuracy and stability compared with basic PID configurations.

Neural Network–Based Prediction of Mold Temperature Distribution Using a Cooling Layer During Cavity Heating in Injection Molding

2026-02-25T11:24:11+07:00

In the injection molding of thin-walled plastic parts, premature solidification of the polymer melt upon contact with the relatively cold mold surface reduces cavity filling capability and adversely affects product quality. Therefore, proper control and distribution of mold cavity temperature during the heating stage play a crucial role in improving melt flow behavior without significantly extending the injection molding cycle. However, studies focusing on layered heating channels for mold cavities and the application of artificial intelligence methods for predicting temperature distribution remain limited. This study investigates the feasibility of using artificial neural networks (ANN) to predict the temperature distribution of an injection mold cavity equipped with a layered heating channel. Temperature data were collected during the mold heating process and used to develop and train an ANN model, which was further compared with a random forest model. When the number of neurons in the hidden layer was increased from 7 to 150, MSE decreased from 15.2575 to 0.6670, while the R_all increased from 0.9579 to 0.9981. Meanwhile, the Random Forest (RF) model also achieved a low prediction error, with MSE values ranging from 0.24 to 0.64 and R_all of 0.9991. The results indicate that the artificial neural network achieves high prediction accuracy and effectively captures the nonlinear relationships governing heat transfer, demonstrating strong potential for application in mold temperature optimization in injection molding processes.

Optimal and Smooth Mobile Robot Path Planning Using GAN, A*, and Cubic Spline Interpolation

2026-02-23T14:51:57+07:00

This paper presents a trajectory-planning method for mobile robots that integrates a Generative Adversarial Network (GAN) with grid-based A*. The GAN generator samples obstacle coordinates while enforcing a 1.0-unit clearance and masking forbidden regions around the start (1,1) and goal (14,14). The workspace is discretized into a 30×30, 8-connected lattice; A* with an admissible and consistent Euclidean heuristic returns the globally optimal lattice path, which is subsequently converted into a smooth geometric trajectory via cubic-spline interpolation. On a 15×15 maze with 60 obstacles, the proposed GAN&A* pipeline achieves a path length of 19.26 units, improving over a Sparrow Search Algorithm baseline (21.8 units). To assess scalability, we further evaluate a 20×20 maze with 120 obstacles. Under identical collision models and smoothing, GAN&A* attains 28.81 units, outperforming two sampling-based planners RRT (30.63 units) and PRM (30.51 units). These results indicate that learned environment synthesis coupled with optimal lattice search yields reliable, short, and smooth trajectories, whereas RRT/PRM require substantially larger sampling budgets (RRT*/PRM*) to approach comparable quality.