Piecewise model based intelligent prognostics for state of health prediction of rechargeable batteries with capacity regeneration phenomena

State of health prediction of rechargeable batteries is an important topic in battery health management systems to infer remaining charge-discharge cycles (RCDC) and ensure high reliability of rechargeable batteries. To investigate various prognostic algorithms, battery life-cycle fade experiments were conducted in the NASA Ames Prognostic Center of Excellence. Even though some prognostic algorithms were proposed in the past years to predict RCDC, most of them omitted the influence of battery capacity regeneration phenomena (CRP) on RCDC prediction. Battery CRP cause some sudden capacity increments at unexpected charge-discharge cycles, which may further result in inaccurate RCDC prediction if such CRP are not considered. In this paper, piecewise model based intelligent prognostics for RCDC prediction of rechargeable batteries with CRP is proposed. Results show that the proposed prognostic model can precisely estimate NASA battery capacity fade data, automatically detect CRP and accurately predict RCDC. (C) 2019 Elsevier Ltd. All rights reserved.