.png){kind=icon}
.png){kind=icon}
Return
Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements
.png){kind=icon}
OA
AI
A
E
A
D
DOI：10.3390/electronics11091414.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV operation. In this work, machine-learning techniques are applied to estimate this parameter in EV applications and in diverse scenarios. After thoroughly analysing cell ageing in different storage conditions, a novel approach based on impedance data is developed for SoH estimation. A fully-connected feed-forward neural network (FC-FNN) is employed to estimate the battery's maximum available capacity from a small set of impedance measurements. The method was tested for estimation in long-term scenarios and for diverse degradation procedures with data from real EV batteries. High accuracy was obtained in all situations, with a mean absolute error as low as 0.9%. Thus, the proposed algorithm constitutes a powerful and viable solution for fast and accurate SoH estimation in real-world battery management systems.
Keywords:
electric vehicle
Li-ion battery
state-of-health
SoH
machine learning
neural networks
battery ageing
capacity degradation
battery management system
battery impedance
Journal
IF:
2.6
Papers:
9.2K
Citations:
4.7W
Organization
Cited Papers