This study aims to investigate the impact of combustion chamber geometry and biodiesel on the performance of diesel engines under various load conditions. Simulations were conducted using AVL FIRE software, followed by experimental validation to compare the performance of the prototype Omega combustion chamber with the optimized TCD combustion chamber (T for turbocharger, C for charger air cooling, and D for diesel particle filter). This study utilized four types of fuels: D100, B10, B20, and B50, and was conducted under different load conditions at a rated speed of 1800 revolutions per minute (rpm). The results demonstrate that the TCD combustion chamber outperforms the Omega chamber in terms of indicated thermal efficiency (ITE), in-cylinder pressure, and temperature, and also exhibits a lower indicated specific fuel consumption (ISFC). Additionally, the TCD chamber shows lower soot and carbon monoxide (CO) emissions compared to the Omega chamber, with further reductions as the load increases and the biodiesel blend ratio is raised. The high oxygen content in biodiesel helps to reduce soot and CO formation, while its lower sulfur content and heating value contribute to a decrease in combustion temperature and a reduction in nitrogen oxide (NOx) production. However, the NOx emissions from the TCD chamber are still higher than those from the Omega chamber, possibly due to the increased in-cylinder temperature resulting from its combustion chamber structure. The findings provide valuable insights into diesel engine system design and the application of oxygenated fuels, promoting the development of clean combustion technologies.

Accurate prediction of lithium battery remaining useful life (RUL) is crucial for battery management systems to monitor battery health status. However, RUL prediction remains challenging due to capacity non-stationarity caused by capacity regeneration phenomena. Therefore, this study proposes a novel RUL prediction framework that combines a two-stage decomposition strategy with a parallel Informer-LSTM architecture. First, STL decomposition is employed to decompose the capacity sequence into trend, seasonal, and residual components. The VMD method further refines the residual component from STL, extracting the underlying multiscale subsignals. Subsequently, a parallel dual-channel prediction network is constructed: the Informer branch captures global long-range dependencies to prevent trend drift, while the LSTM branch models local nonlinear dynamics to reconstruct fluctuations associated with capacity regeneration. Experiments on the NASA dataset demonstrate that this framework achieves an MAE below 0.0109, an RMSE below 0.0160, and an R² above 0.9950. Additional validation on the Oxford battery dataset confirms the model’s robust generalization capability under dynamic conditions, with an MAE of 0.0017. This further demonstrates that the proposed RUL prediction framework achieves significantly enhanced prediction accuracy and stability, offering a reliable solution for battery health status detection in battery management systems.