Estimation of Remaining Travel Range of Electric Boat using Extended Kalman Filter

Estimation of Remaining Travel Range of Electric Boat using Extended Kalman Filter

With the increasing demand for electric mobility, marine transportation modes are beginning to implement electric propulsion. Range anxiety is one of the more common consumer concerns regarding the adoption of electric propulsion in transportation. Remaining range estimation capabilities can reduce...

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Bibliographic Details
Published in:2022 IEEE International Power and Renewable Energy Conference (IPRECON) pp. 1 - 5
Main Authors: De Vera, Adrian Martin Z., Cruz, Febus Reidj G.
Format: Conference Proceeding
Language:English
Published: IEEE 16-12-2022
Subjects:
Anxiety disorders
Batteries
battery
Boats
Electric boat
Estimation
extended Kalman filter
green technology
marine transport
Predictive models
Propulsion
range estimation
Renewable energy sources
state of charge
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Summary:With the increasing demand for electric mobility, marine transportation modes are beginning to implement electric propulsion. Range anxiety is one of the more common consumer concerns regarding the adoption of electric propulsion in transportation. Remaining range estimation capabilities can reduce range anxiety and provide vehicle passengers with insights into their vehicles. Given the nature of water-based transportation being different from that of land transportation, being stranded at sea poses different challenges than being stranded on land. As electrically propelled boats grow in popularity, systems that monitor the remaining energy capacity of the batteries used in such boats and the remaining capable travel range of the said vessels become increasingly important. This study utilizes an extended Kalman filter (EKF) to monitor the battery state of charge (SOC) while incorporating travel distance-based models to estimate the remaining range at given values of state of charge. The results from the study show that the EKF is capable of accurately determining SOC during operation. The range estimation model is also shown to predict the travel distance, with the error value from the total actual distance traveled being 3.29%.
DOI:10.1109/IPRECON55716.2022.10059494