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Pressure Estimation Algorithms in Decoupled Electro-Hydraulic Brake System Considering the Friction and Pressure-Position Relationship
Technical Paper
2019-01-0438
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
This paper presents several pressure estimation algorithms (PEAs) for a decoupled electro-hydraulic brake system (EHB), which is driven by an electric motor + reduction gear. Most of the pressure control solutions are based on standard pressure-based feedback control, requiring a pressure signal. Although the pressure sensor can produce the pressure feedback signal, it will increase cost and enlarge installation space. The rotation angle of electric motor is available by the built-in sensor, so the pressure can be estimated by using the rotation angle. Considering the typical nonlinearities (i.e. friction, pressure-position relationship) and uncertainties (i.e. disturbance caused by friction model), the estimation-oriented model is established. The LuGre model is selected to describe the friction, and the pressure-position relationship is fitted by a quadratic polynomial. Based on the estimation-oriented model, the force-based PEA (FPEA) and the interconnected PEA (IPEA) are designed, respectively. What makes these two PEAs different is that the IPEA considers the pressure-position relationship. The comparison and analysis of the proposed PEAs have been conducted via some typical ordinary braking scenarios. The sensitivity analysis has also been done to obtain the influences on the performance of the IPEA, if the estimation deviation of the friction occurs. Most importantly, this study realized a pressure estimation without add-in sensors, which makes decoupled EHB realizable to become the actuator for automobile active safety systems, giving a promising way to achieve far better performance.
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Han, W., Xiong, L., and Yu, Z., "Pressure Estimation Algorithms in Decoupled Electro-Hydraulic Brake System Considering the Friction and Pressure-Position Relationship," SAE Technical Paper 2019-01-0438, 2019, https://doi.org/10.4271/2019-01-0438.Data Sets - Support Documents
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