On battery state estimation algorithms for electric ship applications

• Published in: Electric power systems research Vol. 151; pp. 115 - 124
• Main Authors: Misyris, George S., Marinopoulos, Antonios, Doukas, Dimitrios I., Tengnér, Tomas, Labridis, Dimitris P.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2017; Elsevier Science Ltd
• Subjects: Algorithms; Batteries; Electric power; Electric propulsion; Electric ships; Electrical equipment; Energy storage; Fossil fuels; Marine electrical equipment; Parameter identification; Ships; State estimation; State estimation algorithms; Storage systems; Marine electrical equipment; State estimation algorithms; Batteries; Electric ships; Energy storage
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2017.05.009

•Overview of BESS integration within an onboard DC system.•Battery model parameter identification using RLS-based method with variable forgetting factors.•Kalman filter and TLS-based techniques for the SOC and capacity estimation.•The combined solution indicates promising results, suitable for demanding ship operation.•Experimental validation is provided to further prove the authors claim. In the last decade increasing concerns about the environment, financial reasons based on fuel prices and application-specific operational challenges have been driving the development of electric propulsion and hybrid or full-electric ships. The use of battery energy storage systems (BESS), which are suitable for a broad range of ship applications with different requirements, can reduce the use of fossil fuels. In this paper the benefits of an onboard DC grid, as applied by ABB, are briefly presented. The integration of BESS and the challenges for ship applications are also discussed. The focus of this paper is on a parameter identification method for an electric model of a battery and the evaluation and validation of a battery state estimation method, in respect to the accuracy requirements for ship applications.