State-of-charge and parameters online estimation is one of the key features of battery management systems for hybrid-electric vehicles applications. Using model-based approaches, simultaneous sequential Bayesian estimation of battery state and parameters has been shown to be a very powerful tool for the tracking, even in the presence of non-perfectly known models. Monte Carlo implementations are very suited to strongly nonlinear and unreliable dynamics, such those of batteries. In this framework, current paper proposes the use of a Rao-Blackwellized Particle Filter (RBPF) for the joint estimation of battery state and parameters. The results are compared with the existing approaches, highlighting the appealing features of RBPF, both in terms of performances and robustness.
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