This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combinedslip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators. ARTICLE HISTORY

This paper considers a novel approach to system identification which allows accurate models to be created for both linear and nonlinear multi-input/output systems. In addition to conventional system identification applications, the method can also be used as a black-box tool for model order reduction. A nonlinear Kalman filter is extended to include slow-varying parameter states in a canonical model structure. Interestingly, in spite of all model parameters being unknown at the start, the filter is able to evolve parameter estimates to achieve 100% accuracy in noise-free test cases, and is also proven to be robust to noise in the measurements. The canonical structure ensures a well-conditioned model which simultaneously provides valuable dynamic information to the engineer. After extensive testing of a linear example, the model structure is extended to a generalised nonlinear form, which is shown to accurately identify the handling response of a full vehicle model.

The method works iteratively in the time domain using an Extended Kalman Filter. The model retains a state space structure in modal canonical form, which ensures that a minimal number of parameters need to be identified and also produces additional information in terms of system eigenvalues and dominant modes. This structure is completely black-box, requiring no physical understanding of the process for successful identification, and it is possible to easily expand the order and complexity of nonlinearities, whilst ensuring good parameter conditioning. A simple nonlinear example illustrates the method, and identification of a highly nonlinear brake model is also presented. These examples show the method can be applied as a mechanism for model order reduction; it is equally well suited as a tool for nonlinear plant system identification. In both capacities this new method is valuable, particularly as the generation of simplified models for the whole vehicle and its subsystems is an increasingly important aspect of modern vehicle design.

• This is an Accepted Manuscript of a book chapter published by [1,2]. The validity of these (necessarily simplified) models also depends on many other fixed, estimated parameters. Usually, even if these other values are physically accurately set, the simplified model can be made to perform better if they are tuned or also identified.Here we embark on an ambitious attempt to identify all the independent parameters in a simplified whole vehicle handling model, including yaw and roll freedoms, independent combined-slip load dependent tyres and appropriate drivetrain lags. This is achievable, given recent findings that Kalman filter methods can be applied to identify all parameters in any wellconditioned model structure [3].In the extended abstract we demonstrated the principle by simulated identification of longitudinal tyre dynamics, including wheel-spin and lock, using an Extended Kalman Filter. In this final paper we consider data collected from a test vehicle carrying out medium to high magnitude manoeuvres including wheel-spin and terminal understeer, in order to build a model which is valid over the whole range of the tyres. We also consider the relative advantages over EKF of using the more computationally efficient, Unscented Kalman Filter for the identification process.

No abstract