EV battery degradation forecasting based on high dimensional data
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
CHANCHAIPOL, PONGSAKORN
SIRIKUL, LEELAWADEE
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This study is made to investigate the benefit of using high-dimensional data from
sensors in electric vehicles (EVs) in State-of-Health (SOH) forecasting. By the term
"High dimensional data", it means the data has a considerably large dimension that
the computation on the data is very difficult or very time consuming without a
proper handling. EV is one of the biggest trends in the automobile industry in
the past few years. One of the biggest concerns about the EVs is their lithium-ion
battery. The lithium-ion batteries will degrade over time based on several factors
such as calendar ageing, accumulated charge, temperature, and etc. These factors
can lead the Li-ion batteries to degrade, which can lead to several problems for the
EVs. The study is made on the Diagnostic Read Out (DRO) dataset from real-world
Volvo Cars’ customers. Several machine learning models were used to predict and
analyzed to find the significant causes of battery degradation. Principal Component
Analysis (PCA) was used to reduce the dimension of data before fitting it into the
prediction models. For the results, this study found that Long Short-Term Memory
(LSTM) is the most suitable machine learning model for SOH forecasting from all
machine learning models considered in this research. Moreover, applying PCA to
the data significantly improves the models’ performances than using the whole data
without dimensionality reduction. Furthermore, adding a suitable number of lag
features to the input also increases the models’ performance considerably. Last but
not least, SOC and the temperature while cranking and starting the engine of the
hybrid EVs are considered to be important to the battery degradation problem.
Beskrivning
Ämne/nyckelord
Lithium-ion Battery degradation , Electric Vehicle , State-of-Health forecasting , Machine learning , Linear Regression , Recurrent Neural Network , Random Forest Regression , Long Short-Term Memory , Principal Component Analysis