To develop an effective control and monitoring scheme for automotive engines, a precise knowledge of the parameters and unmeasurable states of the nonlinear model capturing the overall dynamics of engines is of utmost importance. For a new vehicle out of the assembly line, the nonlinear model has constant parameters. However, in the long run, due to regular wear-and-tear, and for other unpredictable disturbances, they may change. The main challenges are how to obtain the information of parameters and states under the influence of process noise and measurement noise. To address these challenges, we present a new integrated state and parameter estimation algorithm in this paper for spark ignition (SI) engines based on the constrained unscented Kalman filter and the improved recursive least square technique. The system under consideration is a highly nonlinear mean value SI engine model consisting of the throttle, intake manifold, engine speed dynamics, and fuel system. The performance of the proposed algorithm in terms of root-mean-square-error and robustness with regards to initial conditions and random noises is analysed through exhaustive simulation scenarios considering constant, and time-varying parameters. In addition, the performance of other state-of-the-art estimation algorithms is also compared with that of the developed integrated algorithm.

为了为汽车发动机开发有效的控制和监测方案，精确了解捕捉发动机整体动力学的非线性模型的参数和不可测量状态至关重要。对于下线的新车，非线性模型具有常数参数。但是，从长远来看，由于定期磨损以及其他不可预测的干扰，它们可能会发生变化。主要挑战是如何在过程噪声和测量噪声的影响下获取参数和状态信息。为了应对这些挑战，我们在本文中提出了一种新的集成状态和参数估计算法，用于基于约束无迹卡尔曼滤波器和改进的递归最小二乘技术的火花点火 (SI) 发动机。所考虑的系统是一个高度非线性的均值 SI 发动机模型，包括节气门、进气歧管、发动机转速动态和燃油系统。通过考虑常数和时变参数的详尽模拟场景，分析了所提出算法在均方根误差和鲁棒性方面的初始条件和随机噪声的性能。此外，还将其他最先进的估计算法的性能与开发的集成算法的性能进行了比较。通过考虑常数和时变参数的详尽模拟场景，分析了所提出算法在均方根误差和鲁棒性方面的初始条件和随机噪声的性能。此外，还将其他最先进的估计算法的性能与开发的集成算法的性能进行了比较。通过考虑常数和时变参数的详尽模拟场景，分析了所提出算法在均方根误差和鲁棒性方面的初始条件和随机噪声的性能。此外，还将其他最先进的估计算法的性能与开发的集成算法的性能进行了比较。