With the increasingly stringent environmental issues and regulations, there are higher requirements for improving engine performance and reducing pollution. Combining artificial neural network and particle swarm optimization algorithm to optimize the fuel consumption and emissions for micro-ignition dual-fuel engines. A model-based calibration scheme is maintained to reduce the number of experimental points by employing space-filling and V optimization design, to save the experimental cost and improve efficiency. The experimental data used to establish an RBF neural network prediction model that achieves a perfect mapping of engine input and output parameters. Controllable variables such as speed, torque, main injection timing, pilot injection timing, pilot injection quantity, rail pressure, excess air coefficient, and substitution rate limit parameters are input as neural networks. Subsequently, the combination of control parameters was optimized through PSO, thereby to achieve fuel consumption and emissions trade-off. Matching experiment results show actual emissions of NO_x, THC, and CO decreased by 20.5%, 30.3%, and 43.1%, respectively, and the BSFC declined by an average of 2.1% contrasted with the original data. It achieves the optimum of emission and fuel consumption at the same time.

随着越来越严格的环境问题和法规，对提高发动机性能和减少污染提出了更高的要求。结合人工神经网络和粒子群优化算法，优化了微点火双燃料发动机的油耗和排放。维持基于模型的校准方案，以通过使用空间填充和V优化设计来减少实验点的数量，从而节省实验成本并提高效率。用于建立RBF神经网络预测模型的实验数据可实现发动机输入和输出参数的完美映射。可控变量，例如速度，扭矩，主喷射正时，先导喷射正时，先导喷射量，导轨压力，空气过剩系数，替代率限制参数作为神经网络输入。随后，通过PSO优化控制参数的组合，从而实现燃料消耗和排放权衡。匹配实验结果表明实际排放的一氧化氮_x，THC和CO分别下降了20.5％，30.3％和43.1％，而BSFC与原始数据相比平均下降了2.1％。同时达到最佳的排放和燃料消耗。