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In this paper, we suggest a novel algorithm to distinguish semi-steady states from various steering patterns and to estimate the stability factor. The algorithm also estimates each stability factor in left and right turns because there could be a case where they differ based on uneven tire wear and so on. The stability factor, which is the turning characteristic of a vehicle, has been treated as constant for most vehicle control systems. However, in fact, it may change in some situations, for example when a vehicle is overloaded. So there is a chance that a driver may be aware of an unusual sensation when vehicle control is designed based on a constant stability factor. We have succeeded in developing an algorithm to estimate the stability factor accurately enough to be able to compensate for it and have confirmed the effectiveness of the algorithm by simulation and vehicle testing as well.

This paper proposes a vehicle state detection method for improving the stability of vehicles equipped with electric power steering (EPS) and electronic stability control (ESC) systems. ESC is an effective vehicle stability control system that operates within a vehicle's stability limitations. Generally ESC uses a vehicle state signal such as yaw rate. To enhance the ESC function so that it can alleviate understeer, a process that is capable of detecting understeer is required. This concept motivated us to develop a vehicle state detection algorithm based on estimated self-aligning torque using EPS. It is well known that maximum self-aligning torque occurs before maximum cornering force is reached. We have confirmed that the proposed algorithm can detect understeer earlier than conventional means based on vehicle yaw rate.