FROM PLANNING TO OPERATION OF
ELECTRIC BUS DEPOTSINTEGRATED IT SYSTEMS FOR DEPOT AND CHARGING MANAGEMENT
DR.-ING. ENRICO LAUTH

M2G-ITS Lab Workshop 2024, Kyoto 7th May 2024

PLANNING OF DEPOTS FOR ELECTRIC BUSES◼ „Everything is connected to Everything”(German quote, Alexander von Humboldt)•Line bundle fordepot•Vehiclesizes
Timetable andcirculation •Range of e-buses•…
planning◼ Aim is an overall concept optimised interms of costs and operation◼ Difference between new depot (greenfield) and conversion of existing depot
Charging
strategy◼ Simulation environments are necessary

for larger projects (e.g. eFLIPS)•Opportunity
Charging•Depot Charging•Charging Hubs•…

Vehicle andinfrastructure
requirements
at depot•Grid connection•Parking area (size)•Processreorganisation•…

NEED TO USE IT SYSTEMS◼ Challenges◼ Requirements are increasing significantly due todigitalization, automation and electrification◼ Technical requirements are becoming more
complex, just like IT systems◼ The complexity is concentrated at the depot◼ Secondary goals◼ Increase efficiency◼ Increase operationalsafety and quality◼ Keeping costs under control◼ Compensating for staff shortages
and a lack of qualifications

INTERFACESCOMPLEXITY REDUCTION THROUGH STANDARD INTERFACES
Vehicle and charging data

0#0

Planning & Disposition(P&D)

1
Operational management

2Fleetmanagement(ITCS)

Systems

Depotmanagement(DMS)

Standard
P&D

1P&D

ITCS, DMSVDV452, VDV455

2

ITCS

DMS

VDV461

3

ITCS, DMSLMS

VDV4634LMS

TSO/DSO

IEC-60870-5-104

5LMS

LIS

OCPP, VDV261

6LIS

BUS

ISO15118, VDV261

7BUS

ITCS

VDV238, VDV435

3Chargingmanagement
(CMS)

4

Network operator(TSO / DSO)

◼ Many systems and components

increase complexity◼ Standardised interfaces help to link

756Charging
infrastructure(CIS)

and control systems

INTERFACESREDUCING COMPLEXITY THROUGH INTEGRATION
0IVU.suite

Vehicle and charging data
#0

Planning & Disposition(P&D)

Operational management

Fleetmanagement(ITCS)

Systems

Depotmanagement(DMS)

Chargingmanagement
(CMS)

4

Network operator(TSO / DSO)

Standard
P&D

1

P&D

ITCS, DMSVDV452, VDV455

2

ITCS

DMS

VDV4613ITCS, DMSLMSVDV4634LMS

TSO/DSO

IEC-60870-5-104

5LMS

LISOCPP, VDV261

6LIS

BUS

ISO15118, VDV261

7

BUS

ITCS

VDV238, VDV435◼ Interfaces increase complexity, costproject time and (ongoing) money◼ Integration eliminates this risk

756Charging
infrastructure(CIS)◼ Furthermore, algorithms and data in all

subsystems match in the integratedsystem

OPERATIONINTEGRATED DEPOT OPTIMISATION FOR OPERATIONAL STABILITY◼ Practical experience: Manual assignments are only manageable

to a certain amount due to their complexity◼ Park vehicles in suitable parking spaces◼ Allocate rotations to suitable vehicles◼ Charge and prepare vehicles in the best possible way◼ Consider additional criteria: Vehicle type, topology, ...

◼ Electromobility increases the requirements◼ Compliance with the target SoC◼ Plan for preconditioning◼ Integrated depot optimisation solves these challenges◼ Algorithmic solution finding in form of multi-criteria optimization◼ Automatically adjusts vehicle circulation allocations on an ongoing basis◼ Conflicts are eliminated or their effects minimised

OPERATIONSMART CHARGING FOR ROBUST OPERATION AND COST OPTIMISATION◼ Practical experience: Smart charging

requirements cannot be handled manually◼ Prioritisation of charging power and load distribution◼ Grid connection of depot◼ Better distribution of power consumption to reduce grid◼
◼

◼
◼
◼

charges (peak shaving, atypical load)
Utilisation of time-variable electricity tariffs
Preconditioning
Minimisation of battery ageing
Opportunity charging en route
Self-generated electricity e.g. PV◼ Close interaction between depot andcharging management is essential◼ Enables intelligent allocation of rotations,vehicles and charging infrastructureDATA DATA DATADATA ACQUISITION AND ANALYSIS OPTIMISE PROCESSES◼ Analysing vehicle and charging data inconjunction with operating data
◼ Aggregation of information in a central

data pool◼ Context-related evaluation◼ Data analysis requires professional

and technical expertise◼ Based on this, optimisation ofoperation (of electric buses)

DATA DATA DATACONTINUOUS IMPROVEMENT THROUGH SELF-LEARNING MODELS
Forecast

ModelLearningmodelsContinuous
improvement

TripData◼ Conservative forecasts with large safety buffers◼ Forecasts based on data and algorithms aremuch more accurate◼ Self-learning models continue to do this

THANK YOU VERY MUCH
FOR YOUR ATTENTION◼ Dr.-Ing. Enrico Lauth

Project Manager Public Transportenrico.lauth@ivu.de◼ IVU Traffic Technologies AG

Bundesallee 88
12161 Berlin
Deutschlandwww.ivu.de