Advanced Driver Aid System for Energy Efficient Electric Bus

Operation

Teemu Halmeaho, Marko Antila, Jari Kataja, Paula Silvonen and Mikko Pihlatie

VTT Technical Research Centre of Finland Ltd, Espoo, Finland

Keywords: Electric Bus, Driver Assistance, Simulation, Energy Efficiency.

Abstract: Electric bus energy consumption is mainly due to the vehicle traction. Additionally, auxiliary systems such

as cabin heating-cooling, air compressor, and power steering consume energy. One way to optimize the

consumption is a Driver’s Aid System (DAS). Based on the route information, DAS provides the driver the

optimal driving suggestions, and simultaneously may optimise the energy use of auxiliary systems. These

approaches are discussed in the paper. When the optimal air compressor operation was introduced, vehicle

energy consumption was decreased 1.6 %. In addition to guiding the auxiliary devices and the driver,

prospects of using DAS as a communication hub for managing buses, their charging and to share

information for a bus operator are discussed.

1 INTRODUCTION

In the near future, hundreds of electric buses will be

operating in a single city by different bus operators.

A well planned system is needed to manage the re-

charging of the fleets. Even the carefully planned

system is vulnerable e.g. for issues in electricity

distribution, which could lead into situation where

charging demand exceeds the capacity. The most

promising approach for electric city bus charging

infrastructure is believed to be the opportunity

charging concept, where the high power quick

charging would take place during the bus stops and

in the bus hubs. Buses designed for this concept

have small capacity batteries to solve the issue of

higher price of the high power batteries. With these

small batteries, there is always a risk of running out

of charge leading into stoppage.

As the Public Transport Authority (PTA) is

responsible for management of the transport system

and ensuring smooth operation, it should have a

strategy also for abnormal operation situations. For

this, data from in-service buses is needed for the

charging management system to (automatically)

identify e.g. buses in most urgent need for re-

charging. Therefore, on-line data collection is

needed for every electric bus. In addition, the driver

should have an access to the required information

related to the dynamic charging plan of the bus in

hand. The driver’s assistance system can therefore

work as a central information system to guide the

driver not just about the optimal driving style, but

also inform about the charging need. The bus

operator on the other hand is interested on remote

fault diagnostics of the bus components, monitoring

the performance of the drivers in sense of energy

consumption, maintaining the speed limits and

timetable.

Although originally developed for diesel buses,

the driver’s aid device can be used for electric buses

also. The monitoring system collects the well

performed driving sections which are used for

learning the best driving profile for a specific bus

route. The driver’s assistance system will be even

further developed to include optimal operation of the

subsystems. In the normal conditions, the operation

will follow the pre-defined optimal guidance. For

abnormal conditions, the guidance should adapt and

change into corresponding mode.

The energy efficient control of subsystems can

be found either with simulations or using the same

“learning during operation” principle as the original

driver’s aid. As an example of the subsystems,

auxiliary components is chosen for the purpose. The

potential of intelligent operation in this case is

discussed in the paper.

Halmeaho T., Antila M., Kataja J., Silvonen P. and Pihlatie M..

Advanced Driver Aid System for Energy Efﬁcient Electric Bus Operation.

DOI: 10.5220/0005494600590064

In Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS-2015), pages 59-64

ISBN: 978-989-758-109-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

2 FUTURE NEEDS FOR

COMMUNICATION AND DATA

SHARING WITH ELECTRIC

BUSES

In quest of increasing the share of low-emission

buses in the Metropolitan Helsinki area, Finland, the

local PTA, Helsinki Region Transport (HSL), has

announced that they are aiming at ramping up the

share of fully electric busses to be 1 % in 2015, 10

% in 2020 and 30 % in 2025 (Figure 1), (HSL,

2014). In various other major European cities,

similar actions are taken as well.

For fully electric operation, the two main

alternatives for the charging strategy are an electric

bus with a large battery (depot charging concept)

and one with a small battery (opportunity charging

concept). The choice between these two has

significant impact on the designed infrastructure and

cost of the system. Practical issues, such as charging

standards when connecting the charger and the

vehicle physically, but also protocols for the

information exchange, are yet to be solved. The bus

operators and other stakeholders being the end users

and responsible of the costs are interested on the

total cost of ownership if diesel buses are replaced

with electric buses. As the best choice tends to be a

case-specific, careful planning is needed. However,

designing the system beforehand is extremely

difficult, and therefore some flexibility should be

available instead of hard coding the plan. The

proposed driver’s assistance system could work as a

framework for the required communication platform

enabling flexibility for the bus operation.

Currently the driver’s aid systems are focusing

on assisting the driver to survive in special situations

or to add safety functions into driving. Using

machine vision, pedestrian detection has been

studied by Geronimo (2010). For preventing lane

crossing, multi sensor approach was proposed in

(Mcall and Trivedi, 2006). To perform better in

Figure 1: Fleet strategy of Helsinki Region Transport for

ramping up the share of fully electric busses (HSL, 2014).

demanding tasks, such as parallel parking, driver’s

aid was researched (Ozkul et al., 2008, Vorobieva et

al., 2015).

For fixed schedule applications where route is

predetermined (e.g. buses) and the task is to

minimise the energy consumption, there is not much

research activity, although some general patents

exist such as (Franchineau and De Verdalle, 2008).

The closest research activity is related to active

acceleration pedal (Várhelyi et al., 2004), which is

effectively having similar target.

3 DRIVER ASSISTANCE

3.1 Optimal Driving Style for Electric

City Buses

For studying the optimal driving style for electric

city buses, measured data collected from real-world

operation on actual bus line is used. The purpose of

the study is to find out how the driving style affects

electric bus energy consumption and to compare the

identified optimal driving style with the optimal

driving style of diesel buses. The basis of the study

is the real-time driver assistant device, originally

developed for use on diesel buses.

To identify the optimal driving style, the driving

performances involving the lowest possible

consumption are sought and their speed profiles are

then analysed. The optimal driving style for electric

busses is anticipated to differ from that of diesel

buses. The main reason is the possibility for

regenerative braking and differences in efficiency

maps between an electric motor and a diesel engine.

An example of variation in energy consumption

is seen in Figure 2 where two drivers have been

driving the same fully electric city bus on Line 11 in

Espoo, Finland.

Figure 2: In-service electric energy consumption of a test

bus running on Line 11, in city of Espoo.

VEHITS2015-InternationalConferenceonVehicleTechnologyandIntelligentTransportSystems

3.2 Guiding towards Optimal Driving

Style

When the optimal driving style for electric bus has

been defined, the following procedure is identical to

the diesel buses’ data collection, back office

calculation and other required operations to achieve

the driver assistance system to guide the bus driver

for energy efficient driving, maintaining the speed

limits and timetable (Figure 3). With aid of driver

assistance system, it is possible to affect the driver’s

manner of driving in real-time. In a diesel city bus,

economical driving is achieved by quick

acceleration and constant speed that is as low as

possible. The system provides real-time guidance to

drivers, taking into account vehicle position

compared to scheduled position, speed limit and the

travelling comfort of passengers using

recommendations on the intensity of acceleration

and feedback on current speed and its relation to the

target speed. When determining the speed it

dynamically takes into account the timetable: if the

bus is ahead of schedule the constant speed can be

lower.

On diesel buses, saving potential of 5-10 % has

been realized when the driver assistant system has

been demonstrated and tested. The better the driver

follows guidance, the greater savings can be

achieved.

The collected data during the operation is

transferred wirelessly to a back office system

consisting of a server software and browser-based

user interface. The server software automatically

processes and analyses data recorded on the bus line.

The analysis reports can be viewed in the user

interface. The on-board terminal device manages the

measurement data collection and sending it to the

server, and also the actual display of guides for the

driver. To operate, the guidance needs route-based

instructions. For this, the necessary data, such as

timetables and speed limits, are collected from other

systems by the server software. After a learning

period, the target speeds for the bus route will be

calculated using the data that is collected during the

operation. The location information as Global

Positioning System (GPS) coordinates and vehicle

speed with energy consumption are the essential

variables for the monitoring system to compare with

timetable. Using this background data, location-

based target speed profiles will be created to each

bus line and departure. Initial target speeds for the

route can be, schedule permitting, e.g. 5% lower

than the respective speed limits. The system will

then adjust these speed instructions according to the

learned optimal driving style. The route can be

edited in the user interface, if some of the

background data cannot be collected automatically.

The route is presented to the terminal device as a list

of GPS coordinate points with their target speeds,

having information about the bus stops, speed limit

changes and other possible factors.

The most recent addition for the driving assistant

system is the functionality that enables the partial

comparison of driving performances, allowing the

analysis to focus on optimally driven stretches

between bus stops or on even shorter stretches in

order to construct optimum overall speed profiles. If

these driving stretches and corresponding partial

speed profiles are categorised to form general

results, they can be used for adaption to other bus

lines as well. Categories can be based on e.g. speed

limits, stops and slow downs (traffic lights, speed

bumps, pedestrian crossings and intersections) or

length and shape (turns and hills) of the stretch.

Figure 3: Communication between the components of the driver assistance system.

AdvancedDriverAidSystemforEnergyEfficientElectricBusOperation

4 AUXILIARY DEVICE ENERGY

CONSUMPTION

In diesel buses, auxiliary device energy consumption

consists of engine cooling fan, air compressor, air

conditioning, power steering and alternator to run

the 24 V devices such as lightning. For fully electric

buses, the engine cooling fan can be taken out from

the list and separate alternators are not used, but the

equivalent 24 V auxiliary device consumption does

exist. In addition, the electric buses use this low

voltage source for many low power auxiliaries e.g.

fans and pumps of powertrain component cooling

circuit. Instead of alternator, regenerative braking

energy via drive motor is used to produce also the

low voltage electricity.

As reported by Erkkilä et al. (2012), the energy

consumption of auxiliary components in diesel

powered city buses is only marginal. During summer

time, the average consumption for power steering

was under 1 % (in relation to energy available on

crankshaft). In the same study, air compressor was

consuming 2 % and air conditioning 3 %. During

winter time, the auxiliary heater was responsible for

20 % of the total energy consumption.

Albeit the relative energy consumption of the

auxiliary components is low in diesel buses, the

same amount of energy in an electric bus means

higher relative portion from the total energy

consumption. In Figure 4 can be seen a measured

example of how the auxiliary components are using

energy during operation in an actual bus line.

In this example, Heating Ventilation and Air

Conditioning (HVAC) consumption is minimal,

Figure 4: Measured share of auxiliary component energy

consumption in electric city bus during real-life operation

on Line 11 in Espoo, Finland at 15 °C ambient

temperature.

because the outside temperature was 15 °C, which

does not require much heating or cooling. Some of

the energy required by the HVAC system is seen in

the 24 V AUX consumption, as the fans of the

HVAC system (air source heat pump) is connected

there. In this particular case, the fans were

responsible for half of the 24 V AUX energy

consumption. To produce maximum heating or

cooling power, the combined energy consumed by

the fans and the actual HVAC will be at least four

times as much. During a cold winter day, even

additional heating would be needed to maintain the

cabin in desired temperature. The need can be four

times the maximum power of the air source pump,

and therefore needs to be produced with a fuel

operated heater.

5 SUBSYSTEMS’ OPTIMAL

OPERATION

The optimisation of the subsystems’ operation adds

further potential to energy savings. Even though the

driving losses due to the vehicle-traction are

responsible for a large part of the battery energy

consumption, the subsystems waste most of the

energy. The biggest driving losses in city buses are

caused by the rolling resistances of the tyres. In the

subsystems, powertrain components, such as

mechanical driveline and electric motor with an

inverter, produce losses during the operation due to

the limited efficiency in power transmission. Using

mechanical brakes for deceleration, significant

losses are generated, which can be reduced with

regenerative electric braking. Besides powertrain

components, subsystems include also the auxiliary

components. These are not mandatory for vehicle

traction, but may in some cases consume a

considerable amount of energy.

To study and optimize the power consumption of

the auxiliary subsystems a MATLAB Simulink

model was developed. The dynamics of the bus has

been modelled with Simscape (Halmeaho et al.

2014). Main subsystems of the model are shown in

Figure 5.

During the simulation, the bus is driven on an

actual bus route, Line 11 in Espoo, Finland, with bus

stops and other traffic, whose effect into simulation

is included via measured speed profile and auxiliary

component energy consumption. Stopping at bus

stops and traffic lights and following the road with

junctions and turnings require steering, braking and

opening the bus doors. Auxiliary device

consumption is thus dependent on the bus route and

VEHITS2015-InternationalConferenceonVehicleTechnologyandIntelligentTransportSystems

also on the ambient conditions which demand using

HVAC for passenger and driver comfort.

Power consumption of the auxiliary devices is

modelled using a system identification approach.

The electricity consumption of the power steering

and air compressor has been measured. Based on the

measurement data and the route of the bus, simple

mathematical models of the energy consumption of

the given auxiliary devices can be created.

The model of the power steering gives the

consumption as a function of turnings based on GPS

coordinates. The air compressor is used by brakes,

suspension and doors. Usage of brakes has been

measured and opening of doors can be estimated

based on speed and locations of the bus stops. Based

on that information, a model of the air compressor

can be created.

Potential of utilizing optimal air compressor

operation to reduce overall energy consumption was

selected for closer examination. The potential for

energy savings comes from the battery efficiency

and limited charging current. The efficiency is

usually around 96 %, hence using a battery to store

energy will not waste much energy. However, the

battery charging current limit is extremely sensitive

for the battery temperature. During the winter time,

the permitted current can decrease into at least one

third of the maximum, which will limit the

regenerative motor power. However, the

regenerative power could be increased if there was

alternative electric load available. For this purpose,

the compressor was modified to activate only at long

and hard enough decelerations, when it is normally

activated based on air consumption. The cumulative

energy consumption was equal in both strategies.

The battery charging limit was set to 45 A, which

represents the actual observed limit permitted by the

Battery Management System (BMS) in +3 °C

ambient condition.

Figures 6 and 7 compare the results of using the

modified and normal compressor activation strategy.

Figure 6 shows the used speed profile together with

the cumulative battery energies separately for

charging and discharging directions. In Figure 7 is

visible a partial stretch of the cycle, where can be

seen the differences for battery and motor operation

when baseline or modified air compressor activation

strategy is used. The energy savings are achieved

because of the higher regenerative motor power and

avoiding the unnecessary charging and recharging

losses of the battery.

The realized cumulative savings during the

whole cycle was 0.13 kWh, which means 1.6 %

decrease for the overall vehicle energy consumption.

Figure 5: Subsystems of the auxiliary device energy

consumption model. The coordinate data is included in the

model, shown in a map at the top of the figure.

Figure 6: Simulated cumulative energy consumptions and

realized speed profiles for baseline and optimal air

compressor operation during the whole cycle length.

Figure 7: Simulated battery current, motor power and air

compressor power for baseline and optimal air compressor

operation during part of the cycle length.

The total consumption of the compressor was 0.56

AdvancedDriverAidSystemforEnergyEfficientElectricBusOperation

kWh during the cycle. All of this potential is not

available for recovery, because the system is already

in baseline operation inherently feeding the energy

for consumers during the regenerative braking. In

addition, the timing of the compressor usage in the

baseline is often occurred during the hardest

decelerations, and on the other hand, many of the

additional compressor activation in the modified

strategy occur in low power regeneration and current

limit is not met. Although the achieved energy

saving was small on this particular case, higher

savings can be anticipated when also the power

steering and HVAC usage will be optimized. This

could include switching off the power steering pump

on straight driving sections and boosting HVAC

power when slowing down. In addition, the speed

profile of the cycle had only modest decelerations,

and therefore the battery current limit was exceeded

only few times.

6 CONCLUSIONS

Electric buses in comparison with conventional

buses have potential for increased energy efficiency,

zero tail pipe emissions and decreased noise caused

by city transport. This paper described a driver

assistant system to be used for guaranteeing optimal

driving style for electric city buses. The system will

be even further developed to include also intelligent

control of various subsystems. Optimal operation of

auxiliary components was discussed in the paper.

Auxiliary components in electric buses are

inherently controlled in more energy efficient

manner than in diesel buses. Some improvements in

efficiency using intelligent control can be still

achieved. A simulation model that is used for

studying the optimal operation of electric vehicle

subsystems was described. Simulation results when

using optimal air compressor activation was

presented. Overall energy consumption of the

vehicle was reduced 1.6 %. The savings can be

further increased when other auxiliaries are also

considered for optimization.

Based on the bus route information, the proposed

advanced driver’s aid system could combine the

driver guidance and the optimal use of auxiliary

components to achieve more energy efficient electric

city bus driving. In the future, it could include also

communication interface for bus fleet management.

ACKNOWLEDGEMENTS

The authors acknowledge the financial support for

the research work that received from the Electric

Vehicle Systems (EVE) programme funded by

Tekes – the Finnish Funding Agency for Innovation.

REFERENCES

Erkkilä, K., et al. 2012. Energy efficient and intelligent

heavy vehicle – HDENIQ Final report. (pdf) Espoo,

Finland: VTT Technical Research Centre of Finland.

Available at: http://www.transeco.fi/files/783/

Energy_Efficient_And_Intelligent_Heavy_Vehicle_H

DENIQ_Final_Report_VTT-R-08344-12.pdf

(Accessed 6 February 2015).

Franchineau, J. L., De Verdalle, E., 2008. Driving

Assistance Method and Device for a Vehicle for

Travelling Along a Predetermined Path Between a

First Point and a Second Point. U.S. Pat. Application

12/601,600.

Geronimo, D., Lopez, A.M., Sappa, A.D., Graf, T., 2010.

Survey of Pedestrian Detection for Advanced Driver

Assistance Systems. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 32(7), pp.1239-

1258.

Halmeaho, T., Rahkola, P., Pippuri, J., Tammi, K., 2014.

”Hybrid City Bus Design Evaluation Using System

Level Simulations,” In Proc. ISIE 2014, 2014.DOI:

10.1109/ISIE.2014.6864866.

HSL, 2014. HSL mukana Maailman ympäristöpäivässä:

Joukkoliikenne muuttuu päästöttömäksi. (HSL News

bulletin) 5 June 2014. Available at <https:

//www.hsl.fi/uutiset/2014/hsl-mukana-maailman- ym

paristopaivassa-joukkoliikenne-muuttuu- paastottoma

ksi-4934> (Accessed 6 February 2015). (in Finnish).

McCall, J.C., Trivedi, M.M., 2006. Video-based lane

estimation and tracking for driver assistance: survey,

system, and evaluation. IEEE Transactions on

Intelligent Transportation Systems, 7(1), pp.20-37.

Ozkul, T., Mukbil, M., Al-Dafri, S., 2008. A fuzzy logic

based hierarchical driver aid for parallel parking.

In Proceedings of 7th WSEAS Int. Conf. on Artificial

Intelligence, Knowledge Engineering and Data Bases.

University of Cambridge, UK, 20-22 February 2008.

Várhelyi, A., Hjälmdahl, M., Hydén, C., Draskóczy, M.,

2004. Effects of an active accelerator pedal on driver

behaviour and traffic safety after long-term use in

urban areas. Accident Analysis & Prevention, 36(5),

pp.729-737.

Vorobieva, H., Glaser, S., Minoiu-Enache, N., Mammar,

S., 2015. Automatic Parallel Parking in Tiny Spots:

Path Planning and Control. IEEE Transactions on

Intelligent Transportation Systems, 16(1), pp.396-410.

VEHITS2015-InternationalConferenceonVehicleTechnologyandIntelligentTransportSystems