Remaining Useful Life Prediction Driven by Multi-source Data for Batteries in Electric Vehicles

Predicting battery remaining useful life (RUL) is used for early warning of battery aging failure and providing instructions of battery maintenance and recycling. The existing RUL prediction focus too much on decreasing the dependence of aging tests, neglecting the value of test data. In this regard, a battery RUL prediction method driven by multi-source data is proposed for EVs to make full use of the aging test data from other cells. Six lithium-ion batteries were used to verify the effectiveness of the method. The results show that the prediction error is less than only 1 cycle in the case of capacity ‘diving’. In conclusion, the proposed method effectively improves the performance of RUL prediction by using multi-source data, and provides a solution for battery management in the era of big data.

[1]  Haritza Camblong,et al.  A critical review on self-adaptive Li-ion battery ageing models , 2018, Journal of Power Sources.

[2]  Xin Zhang,et al.  An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction , 2018, Microelectron. Reliab..

[3]  M. A. Hannan,et al.  A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations , 2018, Journal of Cleaner Production.

[4]  Hongwen He,et al.  Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2018, IEEE Transactions on Vehicular Technology.

[5]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

[6]  Mohamed Ahwiadi,et al.  An Enhanced Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction , 2019, IEEE Transactions on Instrumentation and Measurement.

[7]  Jean-Michel Vinassa,et al.  Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications , 2012, Microelectron. Reliab..

[8]  Dong Wang,et al.  Battery remaining useful life prediction at different discharge rates , 2017, Microelectron. Reliab..

[9]  Hongwen He,et al.  Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries , 2019, Journal of Cleaner Production.

[10]  Hongwen He,et al.  Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles , 2019, IEEE Transactions on Vehicular Technology.

[11]  Pham Luu Trung Duong,et al.  Heuristic Kalman optimized particle filter for remaining useful life prediction of lithium-ion battery , 2018, Microelectron. Reliab..