An Electric Vehicle Battery State-of-Health Estimation System with Aging Propagation Consideration

Abstract

The battery pack in an electric vehicle (EV) usually contains thousands of single cells and these cells are connected together to generate the required voltage and capacity. Cells usually become aged after a certain number of charging and discharging processes and State-of-Health (SOH) of each cell needs to be monitored periodically to ensure normal work of EVs. However, SOHs of cells cannot be directly measured in practice. Besides, aging propagation phenomena between cells (cells in the same battery pack have different SOHs and the aged cell accelerates aging processes of other nearby cells) exists in a battery pack. In this paper, we propose a data-driven Battery Health Estimation System (BaHeS) to estimate SOH of each cell in a battery pack based on cell information (e.g., voltage and temperature). BaHeS firstly proposes a voltage entropy based detection method to detect abnormal cells by calculating voltage entropies of all cells in a time period. And then, BaHeS builds a cell aging propagation model to model the effect of the detected abnormal cell on its nearby cell by estimating SOH change. Lastly, BaHeS applies a Long Short Term Memory (LSTM) based neural network with the estimated SOH change and cell information as inputs to estimate SOH of each cell. We used battery pack usage datasets from total 50 EVs to evaluate SOH estimation accuracy of BaHeS. The experimental results demonstrate that BaHeS has good SOH estimation performance with as high as 93% accuracy and improves estimation accuracy by 18% compared with existing methods.