A method for estimating the state-of-charge in batteries of electric vehicles is developed using a Kalman Filter-based disturbance observer. Estimation of the state-of-charge is important for the safe functioning of electric vehicles and for minimizing their charging time. The computation of the state-of-charge of the battery at each time instant becomes a non-trivial problem because one can measure only an output voltage. In the present article the equations of Kirchhoff's voltage and current laws are used first to obtain the electric dynamics of the battery and to formulate the associated state-space model. Next, the Kalman Filter is redesigned as a disturbance observer, so as to estimate the state-of-charge despite the effects of model uncertainty terms, The proposed method allows for computing not only the battery's state-of-charge but also for identifying perturbations and model uncertainty about the charging process.
|Title of host publication||2018 110th AEIT International Annual Conference, AEIT 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 17 Dec 2018|
|Event||110th AEIT International Annual Conference, AEIT 2018 - Bari, Italy|
Duration: 3 Oct 2018 → 5 Oct 2018
|Conference||110th AEIT International Annual Conference, AEIT 2018|
|Period||3/10/18 → 5/10/18|