To mitigate the issue of low accuracy and poor robustness of the master cylinder pressure estimation (MCPE) of the electro-hydraulic brake system (EHB) by adopting EHB’s own information, a MCPE algorithm based on vehicle information considering the evolution of the brake linings’ coefficient of friction (BLCF) is proposed. First, the MCPE algorithm was derived combining the vehicle longitudinal dynamics and the wheel dynamics, in which the inertial measurement unit (IMU) was adopted to adapt the MCPE algorithm to road slope change. In order to estimate the brake pressure accurately, the driving resistance of the vehicle was obtained through a vehicle test under coasting condition. After that, with the active braking function of EHB, the evolution of the BLCF was acquired through extensive real vehicle test under different initial temperatures, different initial vehicle speeds, and different brake pressures. According to the test results, a revised model of the BLCF is proposed. Finally, the performance of the MCPE based on the revised BLCF model was compared with that based on a fixed BLCF model. Vehicle test demonstrates that the former MCPE algorithm is not only more accurate at low vehicle speed than the later, but also robust to road slope change.
At present, the master cylinder pressure estimation algorithm (MCPE) of electro-hydraulic brake systems (EHB) based on vehicle dynamics has the disadvantages of poor condition adaptability, and there are delays and noise in the estimated pressure; however, the MCPE based on the characteristics of an EHB (i.e., the pressure–position relationship) is not robust enough to prevent brake pad wear. For the above reasons, neither method be applied to engineering. In this regard, this article proposes a MCPE that is based on signal fusion. First, a five-degree-of-freedom (5-DOF) vehicle model that includes longitudinal motion, lateral motion, yaw motion, and front and rear wheel rotation is established. Based on this, an algebraic expression for MCPE is derived, which extends the MCPE from a straight condition to a steering condition. Real vehicle tests show that the MCPE based on the 5-DOF vehicle model can effectively estimate the brake pressure in both straight and steering conditions. Second, the relationship between the hydraulic pressure and the rack position in the EHB is tested under different brake pad wear levels, and the results show that the pressure–position relationship will change as the brake pad is worn down, so the pressure estimated by the pressure–position model based on fixed parameters is not robust. Third, a MCPE based on the fusion the above two MCPEs through the recursive least squares algorithm (RLS) is proposed, in which the pressure-position model can be updated online by vehicle dynamics and the final estimated pressure is calculated based on the updated pressure–position model. Finally, several simulations based on vehicle test data demonstrate that the fusion-based MCPE can estimate the brake pressure accurately and smoothly with little delay and is robust enough to prevent brake pad wear. In addition, by setting the enabling conditions of RLS, the fusion-based MCPE can switch between driving and parking smoothly; thus, the fusion-based MCPE can be applied to all working conditions.
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