Advances in Control and Optimization for Engine Combustion
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "I2: Energy and Combustion Science".
Deadline for manuscript submissions: 30 July 2026 | Viewed by 572
Special Issue Editor
Special Issue Information
Dear Colleagues,
Engine combustion is the core process determining the efficiency, emissions, and reliability of internal combustion engines (ICEs), hybrid powertrains, and emerging alternative-fuelled engines. In the context of global carbon neutrality goals, stringent emission regulations and the rapid development of electrified transportation, optimizing engine combustion has become critical—not only to enhance thermal efficiency and reduce pollutant emissions but also to adapt to diverse fuels (biofuels, hydrogen, ammonia, e-fuels) and complex operating conditions.
Recent advances in control theory, optimization algorithms, and sensing/actuation technologies have revolutionized combustion management: from model-based control (MBC) and data-driven strategies (machine learning, reinforcement learning) to real-time optimization of fuel injection, air management, and thermal conditions. These innovations are enabling more precise control of combustion phasing, stability, and homogeneity, while addressing challenges such as knocking, misfire, and fuel-to-fuel variability. Additionally, the integration of digital twins, in-cylinder sensing and adaptive control systems is pushing the boundaries of combustion efficiency and robustness in both conventional and next-generation engines.
This Special Issue aims to showcase the latest breakthroughs in the theory, methodologies, technologies, and applications of control and optimization for engine combustion. We welcome contributions that bridge fundamental research with practical engineering, advancing the field toward more efficient, low-carbon, and reliable engine systems.
Topics of interest for publication include, but are not limited to, the following:
- Advanced combustion control strategies (e.g., for HCCI, PCCI, RCCI, SACI, and stratified combustion);
- Data-driven and machine learning-based combustion optimization (e.g., neural networks, reinforcement learning, transfer learning);
- Model-based control (MBC) and predictive control (MPC) for real-time combustion phasing/timing adjustment;
- Multi-objective optimization of efficiency, emissions, and durability in dynamic operating conditions;
- Combustion control for alternative fuels (biofuels, hydrogen, ammonia, methanol, e-fuels) and fuel blends;
- Adaptive control systems for handling fuel variability and aging effects;
- In-cylinder sensing (pressure, temperature, species) and feedback control integration;
- Thermal management and its coupling with combustion optimization;
- Combustion stability control and fault diagnosis (e.g., misfire, knocking, pre-ignition);
- Digital twins and virtual calibration for combustion control development;
- Optimization of fuel injection (pressure, timing, multiple injections) and air management (EGR, turbocharging);
- Combustion control for hybrid powertrains and range extenders.
Dr. Zhaojie Shen
Guest Editor
Manuscript Submission Information
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Keywords
- advanced combustion control strategies
- combustion stability control
- fault diagnosis
- adaptive control systems
- multi-objective optimization
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