2024PHM 基于物理信息的数据驱动方法预测海事电池系统健康状态

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Physics-Informed Data-Driven Approaches to State of Health
Prediction of Maritime Battery Systems
Azzeddine Bakdi
1
, Maximilian Bruch
2
, Qin Liang
3, 4
, Erik Vanem
3, 5
1
Corvus Energy, Porsgrunn, Norway
abakdi@corvusenergy.com
2
Fraunhofer ISE, Freiburg, Germany
maximilian.bruch@ise.fraunhofer.de
3
DNV Group Research & Development, vik, Norway
Qin.Liang@dnv.com
Erik.Vanem@dnv.com
4
Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology, Ålesund, Norway
qinlia@stud.ntnu.no
5
Department of Mathematics, University of Oslo, Oslo, Norway
erikvan@math.uio.no
ABSTRACT
Battery systems are increasingly being used for powering
ocean going ships, and the number of fully electric or hy-
brid ships relying on battery power for propulsion and ma-
neuvering is growing. In order to ensure the safety of such
electric ships, it is of paramount importance to monitor the
available energy that can be stored in the batteries, and clas-
sification societies typically require that the state of health
(SOH) of the batteries can be verified by independent tests
annual capacity tests. However, this paper discusses physics-
informed data-driven approaches to online diagnostics for
state of health monitoring of maritime battery systems based
on a combination of physical knowledge, physic-based mod-
els, insights from extensive characterization tests and opera-
tional sensor data collected from the batteries during actual
operation. This represents an alternative approach to the an-
nual capacity tests for electric ships that is found to be suffi-
ciently robust and accurate under certain conditions. Previous
attempts with purely data-driven models, including both cu-
mulative and snapshot models, semi-supervised learning and
simple models based on the state of charge did not achieve
the required reliability and accuracy for them to be utilized
in a ship classification perspective, as presented at previous
Erik Vanem et al. This is an open-access article distributed under the terms of
the Creative Commons Attribution 3.0 United States License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
PHM conferences. However, preliminary results from the
physics-informed data-driven method presented in this paper
indicate that it can be relied on for independent verification of
state of health as an alternative to physical tests. It has been
tested on battery cells cycled in laboratory degradation tests
as well as on field returns from batteries onboard ships in ser-
vice. Notwithstanding, further validation and verification of
the method is recommended to further build confidence in the
model predictions.
1. INTRODUCTION
The safety of battery-powered ships is important, and classi-
fication societies have rules for the safe design, construction
and operation of such ships. One crucial aspect of the safety
of electric ships is to ensure that sufficient energy is stored
in the batteries to cover the required demand for the intended
operation (Hill et al., 2015). Loss of propulsion power in a
critical situation can lead to serious accidents such as colli-
sion or grounding. Therefore, robust estimation and predic-
tion of the actual available energy of a battery is crucial for
ship safety.
Batteries are aging and the energy storage capacity degrades
over time. The aging process affects both the amount of
charge that can be stored and the available power. Most mar-
itime battery systems are designed with an expected lifetime
of 10 years and end of life is typically defined as State of
1
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