This paper presents a physics-informed, tacholess pipeline for smartphone-grade Noise, Vibration, and Harshness (NVH) diagnosis in electric vehicle powertrains. A configurable generator synthesizes labeled signals with order components (1×/2×/3×), AM/FM modulation, sub-harmonics, impact-driven ring-down near resonance, and realistic white/pink/ambient noise at phone bandwidths. A ridge-guided harmonic comb recenters orders without a tachometer and splits tonal from residual content. Interpretable features—order-invariant ratios ($E_{2\times }/E_{1\times }$, ${\mathrm{S}\mathrm{B}}_1$/$E_{1\times }$, $E_{0.5\times }/E_{1\times }$) and residual descriptors (band-power, kurtosis, cepstrum/WPT)—feed light-compute models. A reproducible benchmark stresses SNR (−5…+10 dB), RPM profiles (ramp/steps/cycles), and simulated domain shift; parameter-to-feature analyses (with Sobol sensitivity and a delta-method identifiability proxy) quantify measurability under phone constraints. Across a five-fold CV, tacholess order tracking increases tonal SNR by ≥+6 dB and yields macro-F1 ≈ 0.86 with Random Forest, while ordinal severity achieves QWK ≈ 0.81 (ECE ≈ 0.06) and regression attains MAE ≈ 0.12 (R² ≈ 0.78). All code, datasets, figures, and tables regenerate from fixed seeds with one-command builds; a data card and a sim-to-real guide are included. The result is an open, low-compute standard that couples reproducibility with physics-aligned interpretability, providing a practical baseline for EV NVH diagnostics with smartphones and a common ground for future field validation.

Thermal performance of air-cooled electric-vehicle (EV) packs and inverters deteriorates at altitude, yet design guidance remains calibrated to sea-level conditions. This study quantifies the altitude penalty on convective cooling and translates it into practical, probability-based design rules. Hourly meteorology (temperature, pressure, wind) from ERA5/ERA5-Land was extracted for Andean and Pacific/Caribbean cities and coupled to a property-aware 0D/1D thermal model using modern flat-plate Nusselt correlations. A physics-informed scaling for the convective coefficient yields a compact altitude penalty law, ϕ, and a sea-level equivalence mapping U_eq that preserves h. The framework is validated multi-city and embedded in uncertainty (Sobol’) and extreme-value analyses, with dependence-preserving copula sampling. Results show systematically higher thermal severity in the Andes: city-level P95(ΔT) and exceedance of ΔT > 20 K increase, and median guard-band factors for required heat-exchange area A^\*are 20.8 % higher than at the coast. The Ueq mapping reproduces altitude–sea-level equivalence with ~1–3 % mean relative error, enabling sea-level bench testing of altitude operation. Practical design charts report A^\*by city and vehicle archetype (hatch–bus), with linear slopes of +0.04 to +0.14 A^\*per km. EVT return levels confirm that altitude amplifies not only percentiles but also rare thermal peaks. A fan-sizing layer links A_flow, RPM, and power to maintain ΔT targets across sites. The study delivers reproducible Python tools, validated mappings, and city-resolved guard-bands that convert altitude physics into deployable requirements for EV thermal management and inverter cooling.

This study analyzes the integration of systems thinking with the quantitative scientific method in the teaching of research in engineering and applied sciences. It demonstrates that systems thinking enables a better understanding of complex problems by considering the interrelation between variables, facilitating system modeling in various fields.The results obtained through statistical analysis indicate a significant improvement in students' academic performance after implementing the systems approach. The Wilcoxon test showed a p-value of 5.539 × 10⁻¹⁰, confirming that grades significantly improved in the second evaluation. Additionally, the Shapiro-Wilk normality test revealed that the analyzed variables (nota-1, nota-2, dt1, dt2) do not follow a normal distribution, justifying the use of non-parametric methods.Overall, systems thinking-based teaching reduces learning variability, helping students acquire a more structured knowledge. The findings suggest that this approach can be a valuable tool for teaching research methodology in engineering and applied sciences.