@article{GENERAL,
      author = {Mitropoulou, D and Elling, L},
      url = {http://library.imarest.org/record/7622},
      journal = {Conference Proceedings of INEC},
      title = {New developments in Energy Management – battery lifetime  incorporation and power consumption forecasting},
      abstract = {In the context of a continuously developing electrical  world where the systems are becoming more complex, the  demands on fuel consumption and greenhouse gases reduction  are also becoming stricter. Additionally, more vessels are  looking into using various energy sources –  batteries, variable speed generators, etc. – to  improve their operation. For such systems, an energy  management system (EMS) becomes a vital component. The task  of an EMS is to optimize the operation based on a specific  goal or goals. Mostly it is the optimization of fuel  consumption, and thus also the exhaust pollution reduction,  that the EMS is striving to achieve. After implementing the  EMS on seagoing ferries (predictable load cycle) and a  super yacht (non-predictable load cycle), RH Marine has  analysed the measured data and has seen a considerable  reduction of fuel consumption, of even up to 38%. With the  initial goals achieved, the EMS operation can now be  expanded to include more tasks.

A further development of  the EMS is to incorporate lifetime, wear and tear, and  maintenance requirements of equipment into it.  Doing  so makes it possible to optimise on total cost of ownership  (TCO). As a proof of concept the battery lifetime has been  incorporated. Extensive simulations demonstrate that the  battery lifetime can be extended, or better: a required  lifetime can be reached. This can be realised by carefully  observing lifetime determining quantities, like state of  charge (SOC), magnitude of charging and discharging  currents. This is achieved with a minimal impact on the  previously obtained reduction in fuel consumption. The same  is valid for other energy sources, such as diesel-generator  sets (DGs). By incorporating effects of start-stop cycles  on wear and tear, the use of these can be optimised  regarding TCO as well.

The better the load can be  predicted, the better the EMS will perform. The  self-learning function of the EMS will make use of all  available data, both from past and future, like a planned  combat mode. The paper describes how the above has been  integrated into the EMS. The simulations that were  performed to prove the concept as described are presented  and further developments in the near future will be  announced. },
      number = {GENERAL},
      recid = {7622},
      address = {2018-10-03},
}