Vehicle dynamics estimation via augmented Extended Kalman Filtering

• Published in: Measurement : journal of the International Measurement Confederation Vol. 133; pp. 383 - 395
• Main Authors: Reina, Giulio, Messina, Arcangelo
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.02.2019; Elsevier Science Ltd
• Subjects: Automotive parts; Combined state and parameter estimation; Cornering; Cornering stiffness identification; Driving conditions; Estimating techniques; Extended Kalman filter; Invariants; Kalman filters; Mathematical models; Nonlinear system identification; Nonlinear systems; Parameters; Performance evaluation; Sensitivity analysis; Stiffness; Vehicle dynamics and handling; Yaw; Cornering stiffness identification; Combined state and parameter estimation; Vehicle dynamics and handling; Extended Kalman filter; Nonlinear system identification
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2018.10.030

•Estimating vehicle handling dynamic states on-line using common onboard sensors.•Non-linear model-based observer for vehicle parameter identification and updating.•Evaluation of the system performance and sensitivity to operational conditions.•Comparison with a time-invariant parameter observer showing better accuracy.•Tested against a high-order model providing good results in moderate manoeuvres. The response of active safety systems of modern cars strongly depends on the estimation accuracy in the key motion states of the vehicle. One common limitation of current systems is the lack of adaptability in the parameters of the vehicle model that are usually treated as time-invariant, although they are not exactly known or are subject to temporal changes. As a direct consequence, time invariant-parameter control systems may achieve sub-optimal performance and/or deteriorate according to the driving conditions. This paper presents a non-linear model-based observer for combined estimation of motion states and tyre cornering stiffness. It is based on common onboard sensors, that is a lateral acceleration and yaw rate sensor, and it works during normal vehicle manoeuvering. The identification framework relies on an augmented Extended Kalman filter to deal with model parameter variability and noisy measurement input. Results are described to evaluate the performance and sensitivity of the proposed approach, showing an improvement in the estimation accuracy that can reach an order of magnitude compared to standard approaches.