## 6 Conclusions and Future Work  
In this paper, a system identification procedure is introduced to determine the parameters of a vehicle that are essential for determining its ride characteristics. A _7-dof_ mathematical model for the vehicle is developed, and after careful investigation, 12 vehicle parameters are observed to require identification to achieve comprehensive understanding of the vehicle behaviour. A novel system identification procedure is proposed in the paper, which does not necessitate expensive test rigs and uses only accelerometer and gyroscope measurements obtained from the vehicle running different road test profiles at different  
Figure 11: MATLAB model versus field test results for low-severity herringbone track test at low speed.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \(\mathbf{P}\) & **True Value (\(\mathbf{P}\))** & **Estimate (\(\mathbf{\tilde{P}}\))** & **Error (\%)** \\ \hline \(I_{xx}\) & 257 & 215 & 16 \\ \(I_{yy}\) & 246 & 239 & 3 \\ \(\hat{M}_{s}\) & 240 & 225.6 & 6 \\ \(l_{f}\) & 0.85 & 0.91 & 7 \\ \(\hat{K}_{f}\) & 10.5 & 11.2 & 7 \\ \(K_{r}\) & 6.5 & 5.7 & 12 \\ \(C_{f}\) & 0.63 & 0.67 & 6 \\ \(C_{r}\) & 0.59 & 0.65 & 10 \\ \(M_{uf}\) & 15 & 13.1 & 13 \\ \(M_{ur}\) & 12.5 & 10.5 & 16 \\ \(K_{t}\) & 40 & 37 & 7 \\ \(C_{t}\) & 0.2 & 0.19 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Parameter estimation errors obtained by comparing the time-series responses with experimental measurements.  
speed profiles. The proposed system identification method is based on track and response prioritization. The effects of each parameter of interest on each vehicle response and the capabilities of each track to excite these responses are determined. An optimization routine based on JADE is used in time-domain parameter estimation. The weights of the tracks are adaptively changed according to the spread of the parameters in the population generated in each optimization iteration. The method achieves faster convergence by reducing the sampling impoverishment. A hierarchical test procedure is followed, including a proof-of-concept test run in a MATLAB environment, verification tests run in a MATLAB-ADAMS co-simulation environment, and validation tests run on data collected from the test vehicle. All the results, including the experimental tests, support the superiority of the proposed method.  
In the present approach, we simulated and tested the vehicle traversing on deterministic tracks and used time-series response data for parameter estimation. In the future, the system identification results will be further validated with new experimental data for the vehicle traversing on non-deterministic tracks. The track-to-parameter weight mapping can be analytically acquired based on characteristics of the non-deterministic tracks, including the road excitation frequency characteristics. Furthermore, the proposed method can be extended to include a frequency-domain parameter estimation approach, as in [38].  
[PERSON] developed the vehicle prototype, performed experiments, developed the vehicle model and helped write the paper; [PERSON] conceptualized the algorithm, developed the prioritization scheme and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The vehicle data used in the paper are reported in the manuscript. The time-series test data sets generated during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.