High ambient temperature and repeated heat-soak exposure can increase the thermal-management burden of electric-vehicle batteries and accelerate temperature-sensitive degradation mechanisms. This study develops a coupled simulation framework combining a second-order resistor–capacitor equivalent-circuit model, state-of-charge dynamics, Bernardi-type heat generation, a two-node core–surface thermal network, proportional active battery thermal management, and Arrhenius-weighted degradation exposure. A repeatable tropical boundary condition of 28–42 \( ^{\circ}C \) is imposed together with a synthetic driving/charging duty cycle. Under the reference parameter set, active thermal management limits the peak simulated core temperature to 35.86 \( ^{\circ}C \) compared with 44.62 \( ^{\circ}C \) for passive operation, a reduction of 8.76 \( ^{\circ}C \). The three-day equivalent-cell BTMS electrical demand is 0.166 kWh and the integrated heat removal is 0.243 kWh. Active cooling reduces the normalized cumulative degradation exposure by approximately 14.0% relative to passive operation. Setpoint sweeps from 28 to 36 \( ^{\circ}C \) quantify the trade-off between cooling energy and degradation exposure. Because cell-specific aging data are not yet available, long-term results are reported as normalized degradation exposure rather than experimentally validated percentage SoH. The framework therefore provides a reproducible basis for subsequent parameter identification, experimental validation, and pack-level lifetime optimization under tropical operating conditions.

Phase-asymmetric electric vehicle (EV) charging can aggravate voltage unbalance in radial distribution feeders, while a smart-inverter battery energy storage system (BESS) can provide both energy shifting and fast reactive-power support. This study designs and evaluates a proximal policy optimization (PPO)-based controller for coordinated active- and reactive-power dispatch under asymmetric EV demand. The analysis uses a corrected phase-decoupled three-phase extension of the IEEE 33-bus feeder, with voltage unbalance quantified using the negative-to-positive sequence voltage ratio. Active-power-only (P-only) and coordinated P–Q controllers are evaluated using identical BESS hardware, operating limits, and terminal state-of-charge (SOC) requirements, enabling the incremental contribution of inverter reactive-power capability to be assessed under matched conditions. Across three independent training seeds, P–Q control reduces daily feeder loss from 3874.822 to 3809.751 kWh/day, worst voltage unbalance factor (VUF) from 2.7879% to 2.5940%, and mean hourly maximum VUF from 1.5422% to 1.3628%, corresponding to reductions of 1.679%, 6.954%, and 11.632%, respectively, relative to the no-BESS case. The corresponding P-only reductions are 0.610%, 4.362%, and 8.427%, respectively. All reported learned-policy evaluations return the BESS to 50% terminal SOC, preventing the comparison from being biased by net battery depletion. Without retraining, the P–Q policies retain approximately 5.66–7.86% mean worst-VUF reduction under the tested load, photovoltaic, EV demand, and combined-profile perturbations. The results demonstrate repeatable voltage unbalance mitigation and show that coordinated P–Q operation provides an incremental benefit beyond active-power-only control under the investigated conditions. However, residual undervoltage under severe loading and the phase-decoupled feeder representation limit the scope of the conclusions.

The aim of this scientific study is to investigate the coupled effects of active battery thermal management on temperature regulation, auxiliary cooling energy demand, and long-term degradation-related exposure under a prescribed tropical heat-soak scenario. A reference 50 Ah equivalent lithium-ion cell is represented by a temperature- and state-of-charge-dependent second-order equivalent-circuit model coupled to a two-node core–surface thermal network. The numerical methods include Coulomb counting, irreversible electrical heat generation, proportional cooling control, time integration of normalized degradation-stress indicators, and ambient-temperature and cooling-setpoint sensitivity analyses. A synthetic 3-day charge–discharge duty cycle and a deterministic 28–42 °C ambient-temperature waveform provide repeatable boundary conditions. Relative to passive operation, the modeled active system lowers peak core temperature while incurring additional cooling electricity demand. The setpoint analysis demonstrates a trade-off between thermal protection, auxiliary energy, and normalized degradation exposure. The framework offers a consistent basis for comparative thermal-management assessment; because the reference parameters and exposure relationships are not experimentally calibrated, it does not establish cell-specific capacity fade, service life, or predictive accuracy.

Phase-asymmetric electric-vehicle (EV) charging can aggravate voltage unbalance in radial distribution feeders, while a smart-inverter battery energy storage system (BESS) can provide both energy shifting and fast reactive-power support. This study designs and evaluates a proximal policy optimization (PPO)-based controller for coordinated active- and reactive-power dispatch under asymmetric EV demand. The analysis uses a corrected phase-decoupled three-phase extension of the IEEE 33-bus feeder, with voltage unbalance quantified using the negative-to-positive sequence voltage ratio. Active-power-only (P-only) and coordinated P–Q controllers are evaluated using identical BESS hardware, operating limits, and terminal state-of-charge (SOC) requirements, enabling the incremental contribution of inverter reactive-power capability to be assessed under matched conditions. Across three independent training seeds, P–Q control reduces daily feeder loss from 3874.822 to 3809.751 kWh/day, worst voltage-unbalance factor (VUF) from 2.7879% to 2.5940%, and mean hourly maximum VUF from 1.5422% to 1.3628%, corresponding to reductions of 1.679%, 6.954%, and 11.632%, respectively, relative to the no-BESS case. The corresponding P-only reductions are 0.610%, 4.362%, and 8.427%, respectively. All reported learned-policy evaluations return the BESS to 50% terminal SOC, preventing the comparison from being biased by net battery depletion. Without retraining, the P–Q policies retain approximately 5.66–7.86% mean worst-VUF reduction under the tested load, photovoltaic, EV-demand, and combined-profile perturbations. The results demonstrate repeatable voltage-unbalance mitigation and show that coordinated P–Q operation provides an incremental benefit beyond active-power-only control under the investigated conditions. However, residual undervoltage under severe loading and the phase-decoupled feeder representation limit the scope of the conclusions.

Power flow analysis is essential for evaluating the steady-state operation of modern smart grids, particularly when distributed energy resources and vehicle-to-grid (V2G) systems are integrated into distribution networks. This study presents a hybrid quantum–classical framework that combines a Variational Quantum Linear Solver (VQLS) with the COBYLA optimizer for AC power flow analysis. The nonlinear power flow equations are linearized iteratively, and the resulting Jacobian-based linear system is approximated using a parameterized quantum circuit. The framework is evaluated using a modified IEEE 33-bus radial distribution system with an aggregated V2G unit connected at Bus 18. In the main optimization run, the VQLS cost decreases rapidly and approaches a stable nonzero value, while the resulting bus-voltage profile follows the overall trend of the Newton–Raphson reference solution. A separate 50-iteration convergence assessment also shows a substantial cost reduction, although the prescribed tolerance of 10⁻⁴ is not reached. The V2G scenario analysis indicates that active power losses during the evening peak are reduced as V2G participation increases, with the 50% smart-discharge case producing the lowest losses among the investigated conditions. In addition, a resource-scaling assessment for systems ranging from 14 to 300 buses shows different growth trends between the normalized classical computation index and the VQLS circuit-depth index. These results support the numerical feasibility of integrating a VQLS-based approximation into iterative power flow analysis. The study remains a simulation-based proof of concept and does not claim computational superiority or quantum speedup over established classical solvers.

This paper presents a simulation-based proof of concept for integrating an existing Variational Quantum Linear Solver (VQLS) with a Newton-type AC power flow procedure for smart grids with vehicle-to-grid (V2G) participation. The novelty of the work lies not in proposing a new VQLS algorithm but in its engineering integration with a reduced Jacobian-based power flow subproblem and V2G operating scenarios. The proposed framework linearizes the nonlinear AC power flow equations and applies a four-qubit Real-Amplitudes VQLS circuit with the COBYLA optimizer to approximate a selected 16 × 16 reduced Jacobian system. The complete voltage profile is then reconstructed through the hybrid quantum–classical procedure. The method is evaluated using a modified IEEE 33-bus radial distribution system with an aggregated V2G unit connected at Bus 18. The main optimization run shows a rapid reduction and subsequent stabilization of the VQLS cost, while the resulting bus-voltage profile follows the overall trend of the Newton–Raphson reference solution. A separate 50-iteration assessment also reduces the cost substantially but does not reach the reference tolerance of 10⁻⁴. The V2G scenario analysis shows that prescribed V2G active-power support can reduce active power losses under both normal and stressed operating conditions, with loss reductions of 26.26%, 27.85%, and 30.52% in the base peak-load, N-1 contingency support, and dynamic railway-peak support cases, respectively. A resource-scaling assessment using a normalized classical computation index and a VQLS circuit-depth index is included only to illustrate resource-growth trends, not to establish computational superiority. The results support the feasibility of the proposed integration under ideal statevector simulation, but no quantum speedup or advantage over established classical solvers is claimed.