Integrating electric vehicles (EVs) into power grids requires efficient, compact high-frequency transformers (HFTs) for solid-state-transformers (SST) and charging stages. This paper combines a physics-informed multi-objective genetic algorithm (NSGA-II, an evolutionary AI method), two-dimensional finite-element (FE) evaluation and dual-active-bridge (DAB) simulation to size HFTs at 85 kHz for ferrite 3C94 (Fe), Metglas 2605SA1 (Me) and Vitroperm 500F (Vi). In a first, FEMM-based stage (10 kW), analytical power density differed from FEMM by 73.7% (Fe), 22.7% (Vi) and 11.4% (Me); Vi reached 11.84 W/cm3 at a simulated peak transformer-only efficiency of 99.46%, and Litz optimisation identified AWG 44–46 and AWG 38–42 operating points. A second, independent stage (22 kW, five seeds per material) shows that the FE model raises the total loss by 3–11%, lowers the leakage inductance by about 48% and reveals hotspots about four times the lumped rise, excluding 31–54% of the designs at a 100 K limit. Fe and Vi then reach 6–37 and 7–27 W/cm3 at 99.8–99.9% efficiency and Me 5–9 W/cm3, and a designed DAB current loop is well damped (55° phase margin). The ranking is sensitive to loss fits and cooling prediction.
Extended producer responsibility (EPR) establishes obligations for end-of-life electric vehicle battery recycling, yet stable cooperation depends on stakeholders’ costs and benefits. This study develops a tripartite evolutionary game of automakers, recyclers, and government regulators to examine how digital capability, information sharing, and government incentives shape recycling cooperation. Stability analysis and numerical simulations also examine responsibility-fulfillment costs. All parameters are illustrative and have not been calibrated to industry data. Under the illustrative baseline, the system converges to an equilibrium in which automakers adopt Active Responsibility, recyclers adopt Deep Participation, and regulators adopt Routine Regulation. The adoption proportions for automakers and recyclers reach 0.90 at approximately 15.97 and 21.95 time units, respectively, while regulatory adoption falls below 0.10 at about 8.05. Digital capability raises automakers’ relative payoffs but may slow recyclers’ adjustments through government-strategy feedback. Information sharing directly benefits recyclers and benefits automakers conditional on recycler participation, whereas insufficient sharing may prevent stable cooperation without additional subsidies. Higher responsibility-fulfillment costs slow automakers’ adjustments. Government incentives primarily shape the transition and have asymmetric effects depending on the recipient. Long-run cooperation without additional incentives requires positive net cooperation payoffs for both firms. These findings inform digital capability development, information sharing, and differentiated incentive design.
Estimation of State of Health (SoH) of lithium-ion batteries plays a very crucial role in the efficient running of the battery and performance assessment of the battery. This paper proposes a Deep Neural Network (DNN) model for estimation of the SoH of batteries using an experimental data set containing 1945 data points with variation in battery type, temperature, load current, minimum voltage (Vmin), voltage variance (VVar), and cycles. The input features are normalized through Min-Max scaling, and the DNN structure is tuned through the activation functions, number of hidden neurons, number of hidden layers, batch sizes, optimizers, drop out rates, and kernel L2 regularization parameters. In terms of generalization capabilities, LOTO and LOBO tests are conducted, from all the configurations, 43 °C temperature the activation function of sigmoid attains the testing MAE of 2.16 with only one hidden layer having 12 neurons, drop out rate of 0.2, a kernel L2 of 0.000001, a batch size of 1, and Adam optimizer. Alongside comparisons of results from the other machine learning algorithms, such as Linear Regression, Decision Tree, Random Forest, and XGBoost. The DNN structure obtains the smallest Kullback-Leibler divergence from all the battery types and temperatures tested, implying high similarity in the distributions of SoH from the predictions and reference data. In addition, actual versus predictions charts of 43 °C indicate that the DNN is able to capture the battery SoH trend.
To solve the problems of low energy utilization efficiency, high equivalent hydrogen consumption, and significant power battery capacity degradation in hydrogen fuel cell tractors under row-crop seeding operations, a High-Efficient Low-Degradation Energy Management Control Strategy based on the Deep Deterministic Policy Gradient (HELD-DDPG) was proposed in this paper. Firstly, a tractor dynamics model was established based on the row-crop seeding operations. Subsequently, a power battery model was established based on the equivalent internal resistance theory to evaluate the state and energy consumption of the power battery, and a battery life model was established that accounts for the impact of capacity degradation on long-term system operation. A hydrogen fuel cell model was constructed according to the characteristics of the hydrogen fuel cell. To achieve a unified quantitative assessment of energy consumption across different energy sources in the entire power system, an equivalent hydrogen consumption model was established. On this basis, the HELD-DDPG was proposed with the objectives of minimizing the equivalent hydrogen consumption of the whole machine, stabilizing the SOC of the power battery, and minimizing battery capacity degradation, subject to the battery discharge current constraint. Finally, a rule-based energy management control strategy was selected as a comparative strategy. Simulation experiments on the HELD-DDPG were conducted using the MATLAB platform, and the effectiveness of the strategy was further validated through the hardware-in-the-loop (HIL) test platform. The simulation results showed that, compared with the comparative strategy, the HELD-DDPG significantly improved energy utilization efficiency of the tractor. Specifically, the chemical energy consumption of the hydrogen fuel cell was reduced by 45.43%, the cumulative energy throughput of the power battery was reduced by 32.92%, the total equivalent hydrogen consumption of the power system was reduced by 15.57%, and the battery capacity degradation was reduced by 19.76%. Meanwhile, the real-time implement ability of the HELD-DDPG was evaluated through the HIL test.
This paper presents a critical review of the policy, infrastructure, and adoption frameworks needed to integrate smart electric vehicle (EV) charging and vehicle-to-grid (V2G) systems into South Africa’s stressed electricity grid. Drawing from the recent peer-reviewed literature, technical reports, and international best practices, the paper examines South Africa’s EV adoption trajectory against global benchmarks, including a dedicated cross-country comparison with Kenya, Nigeria, India, Indonesia, and Brazil. It analyses the operational, infrastructural, and regulatory constraints facing South Africa’s ageing, capacity-constrained grid, including the country’s public charging-station distribution, regional disparities in infrastructure rollout, household-level techno-economics of embedded solar and V2G, and Eskom’s EV pilot project as a case study of utility-led deployment. The socioeconomic and environmental implications of smart EV and V2G deployment are explored in relation to rising urban energy demands, renewable energy integration targets, and national decarbonisation goals. The paper concludes with tailored policy recommendations, derived directly from the cross-country benchmarking, and a strategic outlook to facilitate the scalable adoption of V2G and smart charging solutions in South Africa, including prioritising local manufacturing capabilities, supporting dynamic tariff structures, and aligning national energy and transport frameworks.
