Skip to Main content Skip to Navigation
Journal articles

Lithium-Ion Batteries Health Prognosis Considering Aging Conditions

Abstract : 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 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 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 three 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 RUL for different cases: initial conditions, types of lithium-ion batteries, and aging conditions. The RUL 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.
Complete list of metadatas

https://hal-normandie-univ.archives-ouvertes.fr/hal-02157700
Contributor : Christine Rouil <>
Submitted on : Monday, June 17, 2019 - 11:53:55 AM
Last modification on : Thursday, June 4, 2020 - 11:46:02 AM

Identifiers

Collections

Citation

Asmae El Mejdoubi, Hicham Chaoui, Hamid Gualous, Peter van den Bossche, Noshin Omar, et al.. Lithium-Ion Batteries Health Prognosis Considering Aging Conditions. IEEE Transactions on Power Electronics, Institute of Electrical and Electronics Engineers, 2019, 34 (7), pp.6834-6844. ⟨10.1109/TPEL.2018.2873247⟩. ⟨hal-02157700⟩

Share

Metrics

Record views

92