Multicell state estimation using variation based sequential Monte Carlo filter for automotive battery packs

Abstract

Accurate state monitoring is required for the high performance of battery management systems (BMS) in electric vehicles. By using model-based observation methods, state estimation of a single cell can be achieved with non-linear filtering algorithms e.g. Kalman filtering and Particle filtering. Considering the limited computational capability of a BMS and its real-time constraint, duplication of this approach to a multicell system is very time consuming and can hardly be implemented for a large number of cells in a battery pack. Several possible solutions have been reported in recent years. In this work, an extended two-step estimation approach is studied. At first, the mean value of the battery state of charge is determined in the form of a probability density function (PDF). Secondly, the intrinsic variations in cell SOC and resistance are identified simultaneously in an extended framework using a recursive least squares (RLS) algorithm. The on-board reliability and estimation accuracy of the proposed method is validated by experiment and simulation using an NMC/graphite battery module.