A redundant sensor system with driving dynamic models for automated driving

Bechtloff, Jakob; Isermann, Rolf

doi:10.1007/978-3-658-08844-6_50

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…with the correlation time τ c and an unknown disturbance w p,µ,i ∼ N 0, σ 2 p,µ,k , which is assumed to be GAUSSIAN white noise [6]. To ensure that µ road,i has physically plausible values, it is constrained by the Tangens hyperbolicus function.…”

Section: Estimator Design and Tuningmentioning

confidence: 99%

“…With the same sampling time, the UKF is able to approximate nonlinearities better than the EKF while being easier to implement [4] and requiring comparable computational effort [5]. An approach for simultaneous vehicle state and tire model parameter estimation using the EKF is shown in [6]. The authors use a spatial double track model with unknown road inclination and slope as well as MAGIC FORMULA tire models with uncertain parameters and wheel-individual adaption.…”

Section: Introductionmentioning

confidence: 99%

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Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle

Heidfeld

Schünemann

2021

Energies

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Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown disturbances, such as changes in road conditions, is crucial for realization of such control systems. This article shows the design, tuning, implementation, and test of a state estimator with individual tire model adaption for direct drive electric vehicles. The vehicle dynamics are modeled using a double-track model with an adaptive tire model. State-of-the-art sensors, an inertial measurement unit, steering angle, wheel speed, and motor current sensors are used as measurements. Due to the nonlinearity of the vehicle model, an Unscented Kalman Filter (UKF) is used for simultaneous state and parameter estimation. To simplify the difficult task of UKF tuning, an optimization-based method using real-vehicle data is utilized. The UKF is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation. High precision even in severe driving maneuvers under various road conditions is achieved. Nonlinear state and parameter estimation for all wheel drive electric vehicles using UKF and optimization-based tuning is shown to provide high precision with minimal manual tuning effort.

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Section: Estimator Design and Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle

Heidfeld

Schünemann

2021

Energies

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“…He provides a taxonomy for fault diagnosis systems and related areas, describes the advantages which can be obtained by fault diagnosis, discusses the relevant approaches, and illustrates a number of applications in this field [4,43]. Other applications contain condition monitoring of rotating electrical machines [44,45], electrical power supplies [46,47], intelligent transportation systems [48,49], or communication networks [50,51]. …”

Section: Related Workmentioning

confidence: 99%

Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

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Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.

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