Hybrid Systems for Diesel Powered Ships Hybrid topologies for slow - - PowerPoint PPT Presentation

Hybrid Systems for Diesel Powered Ships

Hybrid topologies for slow speed ships Ship Voyage simulator for performance analysis

• Dr. Eleftherios Dedes

(Research performed at the University of Southampton, UK)

• ΕΝΩΣΙΣ ΕΛΛΗΝΩΝ ΕΦΟΠΛΙΣΤΩΝ

Presentation Contents

• The emission problem
• Methods to determine fuel consumption/ shipping emissions
• Industry methods to reduce fuel consumption
• Investigation on Hybrid Power Systems
• Description of Ship Voyage Simulator
• ECMS non-linear Optimisation
• Results and Discussion

Typical exhaust pollution (production of smoke) due to transient engine loading when fast ferries getting up to service speed

Currently…

3

CO2 SOx NOx PM

The emission problem

• Comparison of Shipping with most pollutant Countries:

Source: IMO

FC x 3.1144

Methods to determine fuel consumption/ shipping emissions

Top down method Vs bottom up approach Emission Factors (Endresen et al., 2003; 2007), Corbett and Coehler (2003)

5

Pollutant type Average Power based factor [g/kWh] Fuel based factor [tonnes/day]

PM10 1.5

• PM2.5

1.2

• DPM

1.5

• NOx

17 0.087 SOx 10.5 (for 2.7% S) 0.02 * % S CO 1.4 0.0074 HC 0.6

• CO2

620 3.17 N2O 0.031

• CH4

0.006 0.0003

• Relationship between NOx and SFOC

Methods to determine fuel consumption/ shipping emissions

Therefore…

• Research window:
• Emissions environment and fuel consumption 

Emission inventories, doubt and unreliable, IMO formulas introduce large error, strict environmental agenda by 2020

• Profitability of Shipping companies  Fuel consumption reduction,

imperative for future sustainability

• Other mature methods to reduce fuel consumption
• Concept visualisation and Data collection:
• Energy requirements (actual operations), statistics
• System sizing (power, density)
• Storage media selection, installation requirements
• Modelling, Simulation and Optimisation

7

Industry methods to reduce fuel consumption

• Operational Measures
• Trim optimisation
• Optimal routing, Just in Time arrival
• Course keeping
• Weather routing
• Technical Measures
• Propeller inflow, outflow optimisation
• Optimal Rudder shape
• Hull lines
• Fuel optimised/ De-rated/ Electronic Diesel Engines

Industry methods to reduce fuel consumption

…>100% Total Savings!! … Perpetuum Mobili “Αεικίνητον”

Ship-Owner Technical Manager

• Supt. Engineer

Concept Visualisation– Hybrid with batteries

• All Energy saving measures except Main Engine retrofits target in

reducing power demand

• Hybrid decouples propeller demand from the efficient operation of the

Main Engine  is fully compatible with current market options/retrofits

• Applies Energy Management strategy by:
• Operating prime movers near the most efficient thermodynamic points
• Given the scenario may utilise stored energy or merge electric loads to

main propulsion  utilises benefits of All Electric Ships

• Manoeuvrability, ultra slow steaming capability
• Redundancy
• Lower maintenance needs for T/Cs, fuel pumps, complete O/H
• Limited operation of Engines in transient loads
• Limit energy conversions by inserting strict performance parameters
• Allow easier operation of ships sailing to ECAs
• Has a scalable power output and scalable Energy capacity

10

Concept Visualisation– Hybrid with batteries

11

Investigation on Hybrid Power systems

• Data Collection from Greek Shipping Companies
• Energy and Power requirements Statistical Analysis
• Selection of Energy storage system
• Structural analysis and hydrostatic loss/ payload reduction
• Preliminary Financial viability assessment
• Development of Ship voyage simulator
• Setting up the Power handling problem in Matlab suite
• Non Linear Optimisation

12

13

Energy/ Power requirements statistical Analysis

• Determination of “off optimum” Engine operation point

14

Selection of Energy storage system

Type of Vessel: Energy [MWh]: Power [MW]: Handysize 8 1 HandyMax 8 1 Panamax 15 2 Post – Panamax 5 2 Capesize 4 1 Type

Wh/kg Cost [$/kWh]

Lead Acid 35 90 Vanadium - Bromine 50 300 Silver Cadmium 70

• Zinc - Bromine

70

• Sodium/nickel chloride

115 110 Lithium Ion 150 600

Sodium Nickel Chloride Battery

• Extreme tests were undertaken in EUCAR organisation
• Impact
• Penetration tests
• Complete immersion in water
• Spike penetration and spraying with water
• Vibration testing
• 30 minute gasoline fire test

15

Sodium Nickel Chloride Battery

• This produces nickel containing re-melt alloy used in the

stainless steel industry.

• The ceramic and salt contained in the cells collects in the slag

and is compatible with their process.

• The slag is sold as a replacement for limestone used in road

construction – nothing goes to the landfill.

• One of the weak links in any recycle process is the collection of

the spent units at the end of life.

16

17

Structural analysis

• Void spaces, Double bottoms, bosun store, poop deck

compartments, Top side tanks  sufficient free volume

• Weight distribution, Battery Rule compliance, Risk parameters

Ship Type HandySize HandyMax Panamax Post - Panamax Capesize Required Energy [MWh] 8 8 15 5 4 Required Battery Volume m

3

Sodium Nickel Chloride 190Wh/L 42 42 79 26 21 Vanadium Redox Flow 30Wh/L 267 267 500 167 133 Engine Room Volume[m

3]

3800 4530 4900 5150 9600 Free volume in current engine room: 35% of total volume 1300 1580 1650* 1760* 3350 Added Volume due to electric components: 1040m

3

Additional Engine Volume: 2x100.4m

3 + 4x59.30m 3 = 438m 3

Ship Type Handy Size Handy Max Pana max Post- Panam ax Capesi ze Required Energy [MWh] 8 8 15 5 4 Required Battery weight [tonnes] Sodium Nickel Chloride 150Wh/kg 70 70 130 43 35 Vanadium Redox Flow 50Wh/kg 160 160 300 100 80 Final Added weight to the vessel (propulsion system + storage) 323 323 384 297 288 414 414 554 354 334 Increase in Lightweight [%] 4.1% 3.4% 3.2% 2.0% 1.2% 5.2% 4.3% 4.7% 2.4% 1.4%

18

Hydrostatic impact - payload reduction

Condition Cargo [tonnes] Cargo Loss % Ballast [tonnes] LCB [m] LCG [m] MTC [tm] Trim [m] Normal Ballast Departure 0.00 23414.3 117.126 117.189 1040.2 2.08 Normal Ballast Arrival 0.00 26061.1 114.757 114.855 1068.1 3.045 Heavy Ballast Departure 16487.1 0.00 23411.7 115.991 116.059 1226.5 2.667 Heavy Ballast Arrival 16487.1 0.00 24476.8 116.88 116.682 1191.4 2.265 Grain Departure SF65 60188.4 0.00 2250.7 116.664 116.702 1346.4 1.674 Grain Arrival SF65 60188.4 0.00 2250.7 118.866 118.879 1298.4 0.514 69990 tonnes DWT cargo Departure 65152.1 0.54 115.448 115.501 1364.5 2.323 69990 tonnes DWT cargo Arrival 65152.1 0.54 117.517 117.548 1331.3 1.205 Homogenous Design Departure 67858.4 0.52 115.656 115.702 1364.6 2.09 Homogenous Design Arrival 67858.4 0.52 117.668 117.693 1336.3 0.983 Grain Departure SF42 87866.1 0.40 116.837 116.841 1392 0.335 Grain Arrival SF42 87866.1 0.00 939.4 117.483 117.482 1384.9

19

• Capacity determined by vessel type and statistical analysis
• Constraints of battery manufacturer e.g max 16 parallel units can

exist

• Nominal voltage of 557V
• 0.285 MWh energy density per bank.
• Possibility to connect multiple banks
• Highest operational efficiency at ultra low currents

NBB = Energyreq. 0.285

 

demand nominal discharge/ Charge

16

BB

P V I N  

max

nom BB bat

C N Q  

Efficient Sizing of the Battery Banks

Ship Voyage Simulator

• Matlab/ Simulink environment
• Scalable and modular approach (Blocks and signal flows)
• Each block represents machinery, weather, propeller, ship model
• Hull Resistance (Holtrop – Mennen, Hollenbach methods)
• Added Resistance (Aertssen, Kwon methods)
• Wind Resistance (Isherwood, Blendermann methods)
• Wageningen Series open water performance method
• Battery models, Kinetic Energy approach (Manwell, McGowan)
• Experimental Data for Sodium Nickel Chloride
• Shop Test Data and sea trials data for the propulsion machinery
• Wind and wave generator models based on average reported data

20

Ship Voyage Simulator

• Simulator is separated into 2 sub-models in order to reduce the

complexity (Power profile generation, Optimisation)

• Time domain
• Air Emissions are quasi static phenomena  No transients
• Generation of power demand using the voyage simulator:
• Random wind and wave generation (Hs, T, μ, Vwind, μwind)

based on captain observations (mean values reported)

• Mean value added resistance model
• RAO block if sea-keeping results are available
• Series 60 mean added resistance value for Tp, Hs and μ

21

Ship Voyage Simulator

ECMS non-linear optimisation

• SQP method, update of the Langrangian
• Converges rapidly to the solutions (compared to other methods)
• Single objective (minimisation of Fuel Consumption)
• Equivalent Cost Minimisation Strategy (ECMS) applications:
• A layouts (Hybrid Auxiliary loads, AES)
• B layout (Hybrid – Conventional system) (propulsion loads only)
• C layout (Fully integrated Hybrid system (Propulsion and

auxiliary loads. Propulsion system coupled with auxiliaries)

23

ECMS non-linear optimisation

• Successful use in automotive industry
• Converges in automotive applications with results of DP
• No a priori knowledge of engine loading or vessel speed 

need for time step optimisation

• Unconstrained or constrained
• Principles of ECMS algorithm
• Battery usage has an equivalent fuel cost
• Battery charging has an equivalent fuel saving in the future
• The λ is determined during simulation and is describing the

percentage difference of the SoCt from the SoCref

• Determines how quickly the charging will occur
• SoC reference is predefined setting for SoC (e.g. 35% for

safety while for automobiles is ~65%)

24

Optimisation Algorithms

• Optimisation Scenarios (A1,B, C)

25

MAIN 2-STROKE DIESEL ENGINE AUXILIARY GENSET AUXILIARY GENSET AUXILIARY GENSET ELECTRIC MACHINE PTO - PTI SYSTEM POWER CONVERTER CONVERTER TRANSFORMER BATTERY BANKS GEAR BOX TO AUXILIARY LOADS TO PROPULSION LOADS A B C RECTIFIER/ INVERTER TRANSFORMER

Optimisation Algorithms

• Power layout (D-A2) “All Electric Ship”

26

AUXILIARY GENSET AUXILIARY GENSET AUXILIARY GENSET PROPULSION MOTOR POWER CONVERTER CONVERTER TRANSFORMER BATTERY BANKS GEAR BOX TO AUXILIARY LOADS TO PROPULSION LOADS RECTIFIER/ INVERTER TRANSFORMER PROPULSION MOTOR POWER CONVERTER CONVERTER TRANSFORMER AUXILIARY GENSET AUXILIARY GENSET AUXILIARY GENSET HYBRID CONTROLLER

• Multiple algorithm starts
• Find all the local minima
• Converge to the global minimum
• Efficiency static values

27

Component Description Necessary in layout Efficiency Battery Converter and Transformer All layouts 98% Transmission losses All layouts 99.5% Gearbox efficiency A1, B, C 98% Motor Converter Transformer B, C 99% Electric Machine Power Converter A2, B, C 96% Electric generator All layouts ~ 96%

Optimisation Algorithms

• Efficiency curves (polynomials based on real data fit)

28

Optimisation Algorithms

• Battery Efficiency curves (Based on laboratory measurements)

29

Optimisation Algorithms

Equivalent Cost Minimisation Strategy

• Layout C (Discharging/Charging respectively):
• Objective Functions (Motor/ Generator)

30

   

  

    

1 1 / 4 6 m-dis. 4 4 / 2 min M/E / , . 5 9 5 9

min 10

M E i i i i A E sim i T F inv conv m

d x x MCR f g x x x x MCR t SFOC x x w x x   

   

                                     



           

1 2 1 2 / 5 6 gen-dis. / 3 min M/E / , . 6 6

min 10

M E i i A E sim i T F inv conv m

d x x x x MCR f g x x MCR t SFOC x w x   

 

                                   



           

1 2 1 2 / 5 6 gen,charg. / 3 / , . 6 6 min M/E

min 10

M E i i A E sim i T F inv conv m

d x x x x MCR f g x x MCR t x w x SFOC   

 

                              



 

6 4 4 / , min 1

min 10

n i i T F inv sim i BB bat

x f g x x x w SFOC t N V 

 

                      



Results

• Ship Voyage Simulator
• Hybrid Power System ECMS optimisation outputs

31

Results

• Ship Voyage Simulator
• Results for Estimation of Power/ fuel consumption

32

Results

• Ship Voyage Simulator
• Results for Estimation of Power/ fuel consumption

33

Laden Voyage Ballast Voyage Vessel 1 Vessel 2 Vessel 3 Vessel 1 Vessel 2 Vessel 3 Simulated Fuel consumption 616.11 614.07 1302.70t 978.45t 276.75t 390.22t 498.66t Measured fuel consumption 653.40 642.20 1359.10t 1206.80t 354.60t 438.20t 594.40t Fuel difference

• 37.29
• 28.13
• 56.35t
• 228.35
• 77.85t
• 47.98t
• 95.74t

Percentage difference

• 5.71%
• 4.38%
• 4.14%
• 18.92%
• 21.95%
• 10.95%
• 16.11%

Ballast voyage Re-analysis of ballast voyage Vessel 1 Vessel 2 Vessel 3 Vessel 1 Vessel 2 Vessel 3 Simulated Fuel consumption 978.45t 276.75t 390.22t 498.66t 1159.28t 307.43t 461.50t 600.44t Measured fuel consumption 1206.80t 354.60t 438.20t 594.40t 1206.80t 354.60t 438.20t 594.40t Fuel difference

• 228.35
• 77.85t
• 47.98t
• 95.74t
• 47.52
• 47.17

23.30t 6.04t Percentage difference

• 18.92%
• 21.95%
• 10.95%
• 16.11%
• 3.93%
• 13.30%

5.32% 1.02%

Results

• Ship Voyage Simulator
• Further improvements in Estimation of Power/ fuel

consumption

• Metrological Models, statistical models for wind/waves
• Not suitable for swell effects

34

Results

• Ship Voyage Simulator
• Results for Estimation of Power/ fuel consumption
• Performance analysis

35

Results

• Ship Voyage Simulator
• Results for Estimation of Power/ fuel consumption
• Performance analysis

36

Results

• Determination of power profile for Hybrid scenarios
• On board data collection for auxiliary loads

37

Results

• Layout A1 (Power split for 14.4MWh)
• Battery output, 1 D/G output instead of 2 initially running

38

• Layout A1 (Power split for 2MWh Vs Conventional system)
• Battery output significantly increases the degree of Hybridisation

39

Results

Results

• Layout D-A1
• Feasible solution with non negligible savings that reach up to

6% of auxiliary fuel bill excluding additional savings due to single D/G operation

• Savings depend on installed power
• quadratic relationship which concaves downwards
• Battery efficiency, charging effect reduces the energy

efficiency

• For 2MWh installed energy
• No significant losses in DWT
• 0.50% of the total A/E fuel bill, excluding again further

savings due to number of engines in operation

40

Results

• Layout D-B
• Taking into account the conversion losses, the Hybrid

solution is not feasible (electromechanical conversions)

• If improving sub component efficiencies, excluding the

battery:

• Daily reduction given the examined load profile is 0.095%
• Layout D-C
• Propulsion fuel consumption savings reaching up to 4.44%
• Cost effective to absorb power with high conversion

losses from the A/E than operate the M/E in less efficient load

• Is directly dependent on the SFOC curve shape and

steepness

41

Constraints alternation and sensitivity analysis

Battery Degradation Battery capacity 2MWh 4MWh 7MWh 10MWh 14.4MWh Baseline 0.53% 2.62% 4.88% 5.52% 5.70% 1% 0.19% 1.66% 2.98% 3.59% 3.79% 2%

• 0.77%

1.34% 1.61% 1.81% 3%

• 0.23%

0.28% 0.25% 0.30% 4%

• 5%
• 10%
• Case

Capacity 24h vector, 2h sample rate, tref = 72h 24h vector, 2h sample rate, tref = 48h 24h vector, 2h sample rate, tref = 24h 2MWh 0.53% 0.52% 0.45% 4MWh 2.62% 2.94% 0.60% 7MWh 4.88% 4.91% 1.90% 10MWh 5.52% 5.52% 5.52% 14.4MW h 5.70% 5.70% 5.70%

Conclusions

• Hybrid potential is feasible for Auxiliary loads
• Battery usage is not suitable for application for direct Main

Propulsion due to conversion losses

• Sensitivity analysis show potential and viability
• Coupling the auxiliary generators and the main propulsion

including batteries is a promising alternative that yields to daily savings of approximately 4.4% (dependent on loading)

• The battery system is working in ultra low currents  maintains

high efficiency

• Degree of Hybridisation: ~50% discharging, ~40% charging,

~10% no action (auxiliary loads)

• Feasible system in term of construction

43

Conclusions

• The DWT is not significantly affected
• For A1 scenario, DWT effect is negligible
• For A2 scenario (AES), the Hybrid system is feasible due to the

absence of conversion losses

• However, this system was not simulated due to absence of

Engine data

• Ship Simulator is a useful tool for propulsion assessment and

performance monitoring

• The scalable and the extendable blocks allow great flexibility in

increasing the system’s complexity and the simulator’s precision for future applications

44

Further details (including reference list)

• E. K. Dedes, D. A. Hudson, and S. R. Turnock. Design of hybrid diesel-electric

energy storage systems to maximize overall ship propulsive efficiency. In Practical design of ships and other floating structure symposium, 2010. PRADS’10, pages 703–713. COPPE UFRJ, 2010.

• E.K. Dedes, D.A. Hudson, and S.R. Turnock. Assessing the potential of hybrid

energy technology to reduce exhaust emissions from global shipping. Energy Policy, 2012; 40: 204-218

• E. K. Dedes, D. A. Hudson, and S. R. Turnock. Technical feasibility of Hybrid

Powering systems to reduce exhaust emissions of bulk carriers. IJME transactions of RINA, 2013.

• E. K. Dedes, S. R. Turnock, D. A Hudson. A modified activity based approach for

accurate estimation of fuel consumption from global shipping. International Journal of Transportation Research part D. Under review, 2014.

• E. K. Dedes, S. R. Turnock, D. A Hudson. Diesel Hybrid systems for increase of

fuel efficiency and reduction of exhaust emissions from ocean going ships. To be submitted to Journal of Energy, 2015.

Hybrid Systems for Diesel Powered Ships

Thank you for your attention!

• Dr. Eleftherios Dedes

Further questions: el.dedes@gmail.com