In gasoline engines, the spark timing is often advanced to increase fuel economy under certain heavy load engine operating conditions. As a compromise between the risk of knock and the power output, spark timing is regulated at the boundary where a low knock probability is tolerated. Due to the stochasticity of binary knock events, it is necessary to have a large number of engine cycles for probability estimations, which can slow down the response speed of a controller to operating condition changes. To speed up the spark timing regulation and to reduce the spark timing variance, in this article, a knock probability feedforward map learning method and a spark timing control method are proposed under a unified framework. A learning method that applies the beta distribution is the key contribution of this work. The beta distribution in the map learning part is used to describe knock probabilities with uncertainties and to determine the next engine operating condition for sampling and map learning. In the spark timing method, the beta distribution is applied in the conventional control method to adjust the control gains. The proposed methods are experimentally validated on a test bench equipped with a production Toyota 1.8 L, 4-cylinder SI engine.

在汽油发动机中，在某些重载发动机工况下，通常提前点火正时以提高燃油经济性。作为爆震风险和功率输出之间的折衷方案，在允许低爆震概率的边界处调节火花正时。由于二元爆震事件的随机性，有必要使用大量发动机周期进行概率估计，这可能会减慢控制器对工况变化的响应速度。为了加快火花正时调节并减小火花正时变化，本文提出了在统一框架下的爆震概率前馈映射学习方法和火花正时控制方法。应用Beta分布的学习方法是这项工作的关键贡献。地图学习部分中的beta分布用于描述具有不确定性的爆震概率，并确定用于采样和地图学习的下一个发动机工况。在火花正时方法中，在常规控制方法中应用贝塔分布来调节控制增益。所提出的方法在配备了生产的丰田1.8升4缸SI发动机的试验台上进行了实验验证。