Abstract
Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery’s kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries.
Highlights
IntroductionIt is expected that a large number of “spent” electric vehicle batteries will become available
The number of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) has been growing at a rapid rate in recent years driven by the need to curb air pollution caused by petroleum-based vehicles.As a result, it is expected that a large number of “spent” electric vehicle batteries will become available.These batteries will have capacities in the range of 70%–80% of their initial value, which may not be acceptable for electric vehicle use, but may be usable in other less demanding applications, in terms of energy density requirements, such as grid reinforcement or buffering of intermittent renewable energy sources
This study aims to investigate the viability and effectiveness of artificial neural networks (ANNs) for estimating the available capacity of spent second-life EV lithium ion batteries that have been used on EV buses
Summary
It is expected that a large number of “spent” electric vehicle batteries will become available These batteries will have capacities in the range of 70%–80% of their initial value, which may not be acceptable for electric vehicle use, but may be usable in other less demanding applications, in terms of energy density requirements, such as grid reinforcement or buffering of intermittent renewable energy sources. This could in turn help reduce the cost of power batteries used in EVs, paving the way for faster EV development. Neubauer and Pesaran [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
