Estimating Environmental Parameters in Connected Electric Powertrains using Set-Membership Filtering

This paper presents a novel approach to assessing some of the electric vehicles(EVs) critical environmental conditions without relying on physical sensors. The proposed approach employs distributed estimation in networked vehicles to estimate parameters that characterize the EV's environmental conditions which impact the range of an EV. The parameters to be estimated are the highly uncertain quantities such as wind speed and rolling resistance. Key to this approach is the set-membership filtering (SMF) method, which is quite different from the traditional adaptive filtering methods. The SMF aims to meet a specification on the filtering error magnitude, instead of minimizing the time average or ensemble average of the squared errors. Moreover, the SMF algorithms feature data-dependent selective update of parameter estimates, which is very useful in estimating the environmental parameters of networked vehicles. The estimation schemes developed here use a vehicle longitudinal model and commonly available vehicle sensor signals. Simulations are performed to demonstrate the advantages of the proposed algorithms in terms of computational complexity, residual parameter uncertainties, and steady-state mean-squared errors in comparison to existing algorithms.