The prognosis and health management of lithium-ion batteries are extremely important issues for operating performance as well as the cost of energy storage systems in vehicular applications. This is achieved through the estimation of the State-of-Health (SOH) and the prediction of Remaining Useful Life (RUL). This paper presents a lithium battery prognosis model considering the battery aging conditions. The proposed model is developed based on the Rao-Blackwellization particle filter, which is able to estimate the posterior values of the aging indicators, i.e., capacity and resistance, and to predict the RUL. The particularity of the proposed model is that it considers the batteries aging conditions of batteries as inputs of the prognosis model. In order to validate the proposed method, experiments have been carried out under different aging conditions for two types of lithium-ion batteries. The proposed model performances have been evaluated. A comparison against the particle filter prognosis model is presented. Results highlight the effectiveness of the proposed technique to predict the remaining useful life for different cases: initial conditions, types of lithium-ion batteries, and aging conditions. The remaining useful life prediction using the proposed prognosis model presents a maximum relative error of 6.64%, which is low compared to 14.3% when a simple particle filter prognosis model is used.

Additional Metadata
Keywords Aging, Battery Aging, Lithium-ion batteries, Lithium-ion Batteries, Particle Filter, Particle filters, Predictive models, Prognosis, Prognostics and health management, Rao-Blackwellization particle filter, Remaining Useful Life, State of charge
Persistent URL dx.doi.org/10.1109/TPEL.2018.2873247
Journal IEEE Transactions on Power Electronics
Citation
El Mejdoubi, A. (Asmae), Chaoui, H, Gualous, H. (Hamid), Van Den Bossche, P. (Peter), Omar, N. (Noshin), & Van Mierlo, J. (Joeri). (2018). Lithium-ion Batteries Health Prognosis Considering Aging Conditions. IEEE Transactions on Power Electronics. doi:10.1109/TPEL.2018.2873247