Study on the Combustion Behavior of Inhomogeneous Partially Premixed Mixtures in Confined Space

Energies, Journal Year: 2025, Volume and Issue: 18(4), P. 899 - 899

Published: Feb. 13, 2025

Reasonably configuring the concentration distribution of mixture to achieve partially premixed combustion has been proven be an effective method for improving energy utilization efficiency. However, due significant influence non-uniformity and flow field disturbances, behavior mechanisms have not fully understood or systematically analyzed. In this study, characteristics methane–hydrogen–air mixtures in a confined space were investigated, focusing on key parameter variation patterns under different equivalence ratios (0.5, 0.7, 0.9) hydrogen contents (10%, 20%, 30%, 40%). The global ratio degree partial premixing controlled by adjusting fuel injection pulse width ignition timing, thereby regulating within chamber. constant-pressure was used calculate burning velocity. Results show that as formation time decreases, increases, accelerating heat release process, increasing velocity, shortening duration. It exhibits rapid characteristics, particularly during initial phase, where flame propagation speed rate increase significantly. velocity demonstrates distinct single-peak profile, with peak its occurrence advancing increases. Additionally, hydrogen’s preferential diffusion effect is enhanced premixing, making process more efficient concentrated. This pronounced low-equivalence-ratio (lean burn) conditions, reaction improves significantly, leading greater stability. occurs almost simultaneously second-order derivative pressure. phenomenon highlights strong correlation between dynamic variations

Language: Английский

Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review

Batteries, Journal Year: 2025, Volume and Issue: 11(3), P. 107 - 107

Published: March 13, 2025

Artificial Neural Networks (ANNs) improve battery management in electric vehicles (EVs) by enhancing the safety, durability, and reliability of electrochemical batteries, particularly through improvements State Charge (SOC) estimation. EV batteries operate under demanding conditions, which can affect performance and, extreme cases, lead to critical failures such as thermal runaway—an exothermic chain reaction that may result overheating, fires, even explosions. Addressing these risks requires advanced diagnostic strategies, machine learning presents a powerful solution due its ability adapt across multiple facets management. The versatility ML enables application material discovery, model development, quality control, real-time monitoring, charge optimization, fault detection, positioning it an essential technology for modern systems. Specifically, ANN models excel at detecting subtle, complex patterns reflect health performance, crucial accurate SOC effectiveness applications this domain, however, is highly dependent on selection datasets, relevant features, suitable algorithms. Advanced techniques active are being explored enhance improving models’ responsiveness diverse nuanced behavior. This compact survey consolidates recent advances estimation, analyzing current state field highlighting challenges opportunities remain. By structuring insights from extensive literature, paper aims establish ANNs foundational tool next-generation systems, ultimately supporting safer more efficient EVs robust safety protocols. Future research directions include refining dataset quality, optimizing algorithm selection, precision, thereby broadening ANNs’ role ensuring reliable vehicles.

Language: Английский