1 INTRODUCTION

Contemporary trends in technological development

have been defined by the long-term policy of the

European Union by legal acts, which will most likely

direct the development of the sector related to electric

propulsion systems supported by hydrogen sources.

The development concept of the European Union

aims to achieve climate neutrality by 2050. As part of

the activities carried out, such strategies and

regulations as the "Green Deal"” [1] or the "Fit for 55"

package” [2, 3] were developed. The task of the

adopted strategies is to transform the EU into an

energy-efficient and highly technological climate-

neutral economy [4, 5]. The above policy also includes

issues related to zero- or low-emission propulsion

systems of vessels.

In order to achieve the presented goals,

technological solutions that would be able to meet

such high environmental requirements are sought.

Such activities are supported by the governments of

many countries, as well as various types of

organizations. An example of it is the organization

ZESTAs [6], whose goal is to promote the rapid and

massive implementation of Zero Emissions Ship

Technology (ZEST). As part of the activities carried

out, pro-ecological, pro-effective and pro-economic

activities are promoted, aimed at supporting

technologies related to electric drive systems,

powered mainly from electrochemical energy storage

and hydrogen cells. The obvious reason for this is the

advantage of minimal carbon emission of electric

machines, in comparison with thermal machines.

Electric motors do not emit any pollutants during

operation and do not require the use of oxygen from

the environment. They also generate significantly

lower levels of noise and vibration than internal

combustion engines. The most important feature of

electric machines is their much higher mechanical

efficiency, exceeding 95% compared to internal

combustion engines. Electric motors also have a better

power-to-weight ratio, are less mechanically complex

and have fewer components, and do not require many

Reliability Evaluation of Electrochemical Energy Storage

Systems Supplying the Ship's Main Propulsion System

P. Szewczyk & A. Łebkowski

Gdynia Maritime University, Gdynia, Poland

ABSTRACT: The paper presents the structure of hybrid and electric modern ship propulsion systems. Types

and configuration of electrochemical cells for selected electric energy storage facilities on the ship were

presented. The method and results of reliability analyses, such as failure mode effect analysis (FMEA),

reliability block diagram (RBD) and fault tree analysis (FTA), used to estimate the probability of failure of the

energy storage systems supplying the ship's main propulsion, are presented. Methods of evaluation and

verification of the proposed reliability model using a laboratory model and available operational and service

data are discussed. A proposal for a quantitative risk analysis of potential damage during the operation of the

energy storage has been presented.

http://www.transnav.eu

the

International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 17

Number 1

March 2023

DOI: 10.12716/1001.17.01.

auxiliary installations to operate. In addition, due to

their properties, electric motors offer more precise

ways to control their speed, which translates into

more efficient maneuvering of the ship.

Unfortunately, in relation to heat engines, the electric

drive system has one drawback, which is related to its

power sources. Current green electricity sources are

characterized by a much lower level of energy density

(0.17-1.8 MJ/kg) compared to fossil fuels (40-47

MJ/kg). For this reason, various types of

configurations are built, using diesel power

generating sets, Electrochemical Energy Storage

System (EESS), supercapacitors, hydrogen fuel cells,

as well as hybrid systems consisting of various

combinations of the above-mentioned energy sources.

Hybrid solutions contribute to reducing fuel

consumption and emissions. They also enable

periodic elimination of exhaust fumes and flexible

operation of the ship's propulsion system [7, 8].

Figure 1. An exemplary configuration of a modern diesel-

electric propulsion system for a ship.

Additionally, in order to minimize energy

consumption, energy storage systems (ESS) can be

supported by other ecological technologies, using for

example: Flettner rotors [9–12], soft sails, hard sails,

kite towing, suction wings, turbines, modified ship

hull structures, foils, aeration systems, or optimization

of transit routes [13–18]. Thanks to the applied design

solutions, it is possible to reduce the demand for

energy consumption, and thus reduce the emission of

toxic gases at the level of 10% to approx. 60%.

Thanks to the use of EESS, it is possible to reduce

the energy consumption of ship propulsion systems,

both diesel-electric hybrid and purely electric. The

configuration of the diesel-electric ship's power

propulsion system is shown in Figure 1.

The publication presents the methods and results

of reliability analyses, i.e. FMEA, RBD and FTA, used

to estimate the probability of failure of EESS energy

storage units used on ferries with electric propulsion.

For the selected configuration of the energy storage,

qualitative analyses were supported by calculations

carried out for the quantitative analysis of the risk of

potential damage during the operation of the energy

storage.

2 CONFIGURATIONS OF SELECTED

PROPULSION SYSTEMS OF SHIPS

Energy storage systems (ESS) are the main

technological element on an electric ship. ESS can be

built on the basis of electrochemical cells,

supercapacitors, hydrogen fuel cells or mixed

structures, the so-called hybrid (HESS - Hybrid

Energy Storage System). Lithium-ion (Li-Ion) cells are

mainly used to build the Electrochemical Energy

Storage System (EESS). Depending on the chemical

composition, Li-Ion cells have different parameters

and cost. For example, we can distinguish the

following types of cells:

− NMC (Lithium Nickel Manganese Cobalt Oxide

(LiNiMnCoO

2), characterized by an average level

of safety, average cost, low number of life cycles -

approx. 1500, average energy density - approx. 220

Wh/kg);

− LFP (Lithium Iron Phosphate (LiFePO

4),

characterized by a high level of safety, average

cost, average number of life cycles - approx. 3000,

average energy density approx. 120 Wh/kg);

− LTO (Lithium Titanate (Li

2TiO3); characterized by

a high level of safety, high cost, high number of life

cycles - approx. 10,000, low energy density approx.

80 Wh/kg);

− LCO (Lithium Cobalt Oxide (LiCoO

2):

characterized by a low level of safety, medium

cost, low number of life cycles - approx. 1000,

average energy density - approx. 200 Wh/kg);

− LMO (Lithium Manganese Oxide (LiMn

2O4):

characterized by an average level of safety, low

cost, low number of life cycles - approx. 700,

average energy density - approx. 150 Wh/kg);

− NCA (Lithium Nickel Cobalt Aluminum oxide

(LiNiCoAlO2): characterized by an average level of

safety, low cost, low number of life cycles - approx.

500, high energy density - approx. 260 Wh/kg)

[19];

− G-NMC (Graphene / Lithium Nickel Manganese

Cobalt Oxide): characterized by an average level of

safety, average cost, high number of life cycles -

approx. 8,000, average energy density - approx.

130 Wh/kg) [20].

Depending on the type of ship (tug boat, Ro-Ro

ferry, passenger ship, off-shore ship, etc.) and due to

the thermal conditioning system, NMC, G-NMC, LFP

and LTO cells are most often used.

Figure 2 shows an exemplary, illustrative

configuration of the electric drive system, using a

combustion generating set, a hydrogen fuel cell and

EESS, which can also be used as HESS.

A characteristic feature of the presented solution is

usage of a DC power supply system for the ship.

Thanks to this solution, it is possible to obtain greater

stability of power supply and quality of electricity, as

well as to obtain economic and environmental savings

[21–28]. In addition, the use of a direct current system

enables the reduction of: up to 20% of fuel; up to 30%

of the weight and area occupied by the electrical

power system; up to 40% of the mass of transmission

cables; up to 85% of the volume of cable corridors. In

addition, there is no need to synchronize generating

sets connected to the busbars, as in the AC power

system.

Figure 2. An exemplary configuration of the ship's electric

propulsion system.

2.1 Selected configuration of EESS

In the electrical propulsion systems of ships, the

voltage level on the main busbars can range from 400

to as high as 1,200 VDC. The capacity of the energy

storage for a ship with a length of approx. 80 meters

and a width of approx. 15 meters is approx. 6.6 MWh

[29]. For larger Ro-Ro ferries with a length of approx.

190 m and a width of 28 m, the energy storage

capacity - depending on the navigation area - may be

approx. 33 MWH (360 tons), and for diesel-electric

hybrid systems - approx. 23 MWh. Reliability

analyzes were carried out for an energy storage

consisting of 32,640 G-NMC cells with a capacity of 55

Ah, a nominal voltage of 3.65 V, with an average

weight of one cell with a housing of approx. 1.5 kg

(130 Wh/ kg). The configuration of the analyzed

energy storage, built of 20 strings, consisting of 51

modules with 32 cells, connected in the 4s8p

configuration, is shown in Figure 3.

Figure 3. Configuration of the analyzed energy storage

powering a ship with an electric drive.

The basic parameters of the module include:

Nominal Capacity 440Ah, Nominal Energy 6.1 KWh,

Max. Energy 6.42 kWh, Max. Voltage 16.4V, Nominal

Voltage 14.6V, Min. Voltage 12.4V, Weight 48.2kg.

The weight of a single string is approx. 2,458.2 kg, and

the weight of the entire energy storage is 49,164 kg

[20]. Thanks to the development of the presented

configuration, redundancy for the electric propulsion

system is ensured, which is a key element for the

operation of the ship.

3 RELIABILITY ANALYSIS METHODS

System reliability is defined as the probability that the

required system functions will perform under

specified conditions for a specified period of time.

Mathematically, reliability (R) is defined as:

( )

Rt e

−

(1)

where R – reliability, λ – failure rate (1/year).

The probability of ensuring failure-free operation

of the system is determined by the product of the

probabilities of failure of each of the analyzed system

components and can be written as:

R RR R= ⋅ ⋅…

(2)

In order to ensure the reliability of a given product

at the appropriate level, a systemic design process is

important, which aims to identify design hazards and

properly assess the risk of the structure. The

introduction of this process is particularly important

when implementing new technologies that determine

the security and reliability of a given system. This

process is widely discussed in the scientific literature,

as well as undertaken more and more often by

practitioners [29–31], which accounts for its

effectiveness. The construction of a safe and reliable

energy storage for the main propulsion of a ship is a

major challenge in which DFR should be an important

part of the construction process.

The process of designing for reliability (DFR) of a

complex system (i.e. ESS) requires detailed planning

already at the stage of conceptual work and defining

product requirements. At this stage, in addition to the

functional assumptions, other requirements should

also be established, i.e.: the target working

environment, failure rate, serviceability, expected

service life. Determining the requirements at an early

stage of R&D works allows for proper planning of

analyzes and tests, which significantly shortens the

product design time, ensuring that reliability

requirements are met. The process of designing for

reliability requires the use of many analyzes and tests,

they are a requirement of applicable standards in a

given industry or target region for which a given

product is intended. For the discussed example of an

energy storage, three methods of reliability

assessment were proposed: reliability prediction

analysis, FMEA and FTA. The proposed methods are

a small part of the entire DFR process for the

construction of an energy storage.

3.1 Failure Mode Effect Analysis

Failure Mode Effect Analysis (FMEA) is a popular

cause and effect analysis of potential failures and

assessment of the risk of their occurrence. Risk

assessment consists in assigning appropriate values of

the probability of a given risk (Occurrence), threats

caused by their occurrence (Severity) and the degree

of detection (Detection). The total risk rating of a

single failure mode, defined as the Risk Priority

Number (RPN), is the product of these three values.

This method is used in many industries and is

standardized through e.g. MIL-STD 1629, SAE J1739,

etc., however, internal processes and risk assessments

are used in the early design stage, based on the

experience of a given organization. FMEA is a

qualitative method, the main purpose of which is a

systemic analysis of potential threats and which helps

to assess the risk, based on predetermined criteria and

weights for a specific product or product line. FMEA

allows for systemic analysis of potential threats and

assessment of the value for each of the categories:

Severity, Occurrence and Detection. Risk scales for a

given category are defined by accepted standards or

internal procedures, however, in the case of a

structural FMEA, it is important that they are the

same in a given project or group of assessed risks. In

the FMEA method, each analyzed risk is assessed by

the RPN determinant (risk priority number), which

consists of the product of the following weights:

severity, occurrence and detection.

RPN S O D=⋅⋅

(3)

The RPN value allows to assess and assign

appropriate priorities in the analyzed risk group, and

then to make appropriate decisions regarding the

reduction of these threats. In the case of Design FMEA

(DFMEA), the RPN is a measure of design

improvement in the next design iteration (risk

reduction or acceptance).

One of the main objectives of the FMEA is to

identify and assess the risk of failure (failure mode).

In the analyzed sample energy storage, a study of

failure modes for basic elements was carried out. The

table presents the results of the analysis in a shortened

form.

The result of the analysis indicates that the highest

risk is a failure of BMS management system. The

probability of such an event is estimated to be

medium (5), but the importance of failure mode for

the entire system is high (9), and its detection before

the failure is low (9). Therefore, in this example, the

RPN parameter, which defines the total estimated

risk, is rated at 405. The cause of this mode is a failure

of the electronics responsible for controlling and

managing the batteries or software. The next highest

rated risk at the RPN=245 level is a short circuit of the

battery cell, the probability of such an event being

medium (5), the threat to the system (7), and the

detection at (5). Increasing the internal resistance of

the cell to a level causing an open circuit is a relatively

low risk (3), its importance is medium (5), and its

detectability is limited due to its easy serviceability

(7). The most frequently observed problem in the

operation of battery cells is an underestimated change

in capacity. This may be the result of technology,

improper operation or the quality of the cells. The

probability in the analysis was assessed at (7),

medium importance (5) due to redundancy ensuring

energy reserve, and detection at (7). The analysis also

presents the risk for the battery module assembly,

damage to the internal electrical connections of

individual batteries at the level of RPN=225, and the

mechanical assembly at the level of RPN=105.

Assigning numerical parameter values for

potential failures on design stage is often subjective

and should not be used as a benchmark for comparing

products from other manufacturers. The described

examp

le of the FMEA analysis allows for the

identification and classification of failure modes,

which may result in changes in the structure,

appropriate planning of tests or the introduction of

preventive service actions.

The FMEA analysis is a good example of systemic

risk analysis. However, the presented example shows

that this method is not a universal way of assessment

and additionally, the conditions of use, environment,

etc. must be taken into account. Therefore, the risk

tables must reflect the target working environment,

operator and carrier conditions, and the costs of a

potential failure. In case range is important, the

capacity change parameter will have a different RPN

than when speed is more important, etc.

3.2 Reliability prediction and modeling

Reliability prediction is one of the methods used in

the Design for Reliability (DFR) process. It is a method

that allows a detailed analysis of the structure of the

system, consisting of many components, properly

cooperating with each other. Failure Rate (FR) data

used in reliability prediction can come from the

following sources: observations of similar systems,

observed field data, laboratory tests ALT (accelerated

life test) or from published standards, e.g. SR322,

MIL-HDBK217, FIDES or 217Plus. The RBD method is

used to model dependencies between subsystems and

components. The reliability block diagram (RBD) is a

graphical representation of the dependence of the

functional states of the analyzed system in terms of

the reliability risk of its components. The analysis is

used to identify and allocate potential reliability

issues and their impact on the overall system

reliability. The advantage of the RBD method is the

possibility to carry out a quantitative risk analysis in

systems with redundant subsystems "k out of N".

Table 1. FMEA selected results

___________________________________________________________________________________________________

Subsystem Failure Mode Failure Cause Occurrence O Severity S Detection D RPN

___________________________________________________________________________________________________

BMS Control failure Major electronics failure 5 9 9 405

Battery cell Short Overload 5 7 7 245

Open Overload 3 5 7 105

Limited capacity Degradation, wear out 7 5 5 175

Battery pack Electrical connections Vibration, shock, 5 9 5 225

manufacturing issues

Mechanical assembly Design issues, overload, 3 5 7 105

improper installation

___________________________________________________________________________________________________

In the analysis, the model of Li-Ion cell was used

according to the FIDES standard, the parameters used

are described in Table 2, details of the model in the

standard [32, 33].

Table 2. Parameters of the Li-Ion battery cell model

________________________________________________

Parameter Symbol Value

________________________________________________

Failure Rete of battery λ0-battery 0,21

Activation Energy (eV) Activation Energy (eV) 0,40

Thermal stress Thermal stress 0,85

Component quality λ

Cst 0,14

Mechanical stress ϒ

Mech 0,01

Thermal stress ϒ

Thermal 0,85

Weibull shape factor β 5,0

________________________________________________

Table 3. ESS parameters in reliability prediction analysis

________________________________________________

Parameter Symbol Nominal Minimum

value value

________________________________________________

No of cells 26 112

No of strings 20

No of modules 1120

Nominal ESS Voltage V 744,6 632,6

Nominal ESS Capacity Ah 14 892 12 648

Nominal ESS Energy kWh 11 088 7 998

________________________________________________

Based on the assumptions made for the single cell

model, the reliability value R(t) for individual energy

storage modules was estimated, and are presented in

Table 4.

Table 4. Reliability prediction of individual ESS

components.

________________________________________________

ESS Component R(t) Failure Rate 1/y

________________________________________________

String 4s 0,999706 0,000147

Moduł 4s8p 0,997651 0,001176

Sting 51s 0,885426 0,060843

ESS 0,087556 1,218088

________________________________________________

Reliability of a single module consisting of 4

battery cells is R(t) = 0.999706. The reliability values of

the remaining modules presented in Table 3 assume a

series connection in the reliability block diagram. In

this configuration, failure of a single block affects the

performance of the entire system. The lack of

redundancy is the worst-case scenario in this case and

indicates the limit value for R(t) = 0.087556. It shows

the impact of damage to a single string, consisting of 4

cells, on the failure of the entire energy storage. In

fact, the developed RBD model allows for a detailed

analysis of the occurrence of individual failures in a

system with redundant subsystems.

The analysis assumed that the necessary condition

to fulfill the assumed mission is to ensure the

minimum parameters of the energy storage specified

in the specification. Nominal parameters were

adopted as the initial state, and the difference between

these parameters determines the acceptable risk of

damage, ensuring operation in accordance with the

assumptions (fail-free operation). Nominal and

minimum values are shown in Table 3. To ensure

minimum voltage and energy storage capacity, it is

acceptable to disable 4 of the 20 strings in this energy

storage example. In this configuration, the storage

capacity is 11,913Ah, the voltage is 144V and the

energy is 8,871 kWh. The developed RBD model

assumes the possibility of redundancy and calculates

the probability of completing a given mission -

ensuring the minimum capacity and voltage of the

energy storage, with various damage variants. The

model uses the “k out of n” equation for redundancy

calculations in the form:

where m is the number of fail-safe modules needed to

complete the mission, n is the total number of

modules in the system, and λ is the failure rate.

Table 5. Dependence of system reliability on string 51

redundancy consisting of 51 modules

________________________________________________

Configuration R(t) Failure Rate 1/y

k-out-of-n

________________________________________________

20 out of 20 0,0875556 1,218088

19 out of 20 0,314151 0,834944

18 out of 20 0,592703 0,515722

17 out of 20 0,808970 0,277477

16 out of 20 0,927905 0,12569

________________________________________________

The analysis shows that the reliability of the

energy storage, assuming that there are 4 redundant

strings in the system, is R(t) = 0.927905, and the failure

rate is 0.125690/year.

The developed model allows for the analysis of the

risk of failure at each level of the listed modules and

the impact on the failure rate of the entire ESS.

The performed reliability prediction analysis

indicates the reliability of the entire energy storage at

the level of R(t) = 0.927905 and failure rate FR =

0.12569/yr. This means that the availability

(availability) of the energy storage in the first 24

months is at the level of 93%. The mean time between

failures (MTBF) is 8 years. The graphs in Fig. 4 show

an estimated decline in reliability after approximately

24 months and a median lifetime of approximately 4.5

years. Fig. 5 shows the failure rate increase over a

time.

Figure 4. Reliability and failure rate in configuration 16-out-

of-20

The worst configuration in terms of system

reliability is the one that requires full functionality of

the energy storage and ensuring the nominal

parameters of the energy storage, i.e. the minimum

voltage of 744V and the capacity of 18,892 Ah. In this

case, a 20-out-of-20 configuration is required, i.e. no

string redundancy. Figure 5 shows the reliability R(t)

over time and the failure rate, which is constant over

time in the absence of redundancy. The reliability

graph shows a significant drop since the start of

commissioning. The reliability of such a system is R(t)

= 0.087556, failure rate FR = 1.28088/yr, MTBF is less

than one year (0.82 years). The results of the analysis

are very low and indicate the limit values considered

in the model as the worst-case scenario, in which

failure-free operation of all components included in

the energy storage is required, i.e. at least 32,640

batteries, 1,020 BMS modules and additional modules

supporting system management (21 pcs. ). This is an

example of how reliability prediction analysis allows

risk assessment and appropriate design planning to

optimize and minimize risk. Due to the very large

number of components included in the energy

storage, the structure of the system should be planned

in such a way as to protect against the implementation

of a high-risk scenario of system failure, in this case

devoid of redundancy.

Figure 5. Reliability and failure rate in configuration 20-out-

of-20

3.3 Fault Tree Analysis

Fault Tree Analysis (FTA) is a qualitative risk analysis

method used in the DFR process to identify and

evaluate the severity of a failure in a system. The FTA

model presents a graphical relationship of events in

the form of a logical tree that shows the relationships

of factors affecting the risk of failure. Using this

method, the dependencies of factors affecting

potential failures are analyzed and it is often a

complement to the FMEA analysis, in which the

causes and effects of potential failures are analyzed in

detail. FTA allows you to visually demonstrate the

relationship between them, which is particularly

important when assessing the reliability of power

supply systems and assessing risk in energy storage

[34].

Figure 6. Simplified FTA model of ESS

Figure 5 shows a fragment of the FTA Logical Tree

for the energy storage. The analysis graphically shows

the dependencies of the impact of individual system

failures on the total inability to perform the assumed

function, i.e. the inability to provide the minimum

voltage of 632 V and the capacity of 12,648 Ah, which

is provided by a minimum of 16 efficient branches out

of 20 in the system.

4 CONCLUSIONS

The presented methods of assessing the reliability of

the ESS are part of the analyzes used in the process of

design for reliability. Performing a reliability

assessment at an early stage of product development

helps reduce the risk of faulty design. The described

methods significantly help to properly define and

evaluate reliability requirements.

The developed reliability prediction model allows

for a quantitative risk analysis of individual modules,

the impact of their failure rate on the overall reliability

of the systems and the risk of failure of each of them.

Due to the complexity and high number of

components that are included in the structure of the

energy storage (33,682 analyzed components), the use

of redundancy allows for a significant improvement

in reliability from 9% in a configuration without

redundancy to 93% assuming 16-out-of-20

redundancy. The model allows for the optimization of

the structure in terms of the risk of failure in relation

to other important parameters, i.e. cost, total weight,

power, paid operation time (COPEX vs. OPEX), etc.

The analysis also allows for the appropriate planning

of preventive service actions, planning warehouse

stocks, determining the cost-effective operation time,

etc. Reliability prediction analysis is mainly used to

assess the technology used, construction and quality

of components and subassemblies used in the

construction of a given energy storage. The risk of

failure in each of the energy storage subsystems is

quantitatively estimated on the basis of data for the

model of basic components, i.e. the battery cell or

BMS. This model allows to indicate the possibility of

reducing the risk through the selection of components

or the use of redundant solutions.

FMEA analysis identification of risk, its effects and

causes of occurrence, and then classification and

assessment in relation to other risks. It allows to

qualitatively determine the importance of individual

emergency modes and determine their importance for

the failure rate of the energy storage. FTA allows for

the analysis of dependencies between events causing

a potential failure risk.

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