Collaborative Strategic Energy Management of Serial-Hybrid Electric Urban Buses in Operation

Gerfried Cebrat (Author) Energie- und Umweltconsulting

Graz, Austria gerfried.cebrat@energie-umwelt.at

Abstract—The paper presents a methodology of coupling serial

hybrid urban buses, propagating the location of forced stops as input for controlling the motor generator set in a strategic energy management systems. The hypothesis postulates that using data from vehicles driving head-to-head - adding information from other telematics services like intersection controllers - helps improving fuel economy. At first the control scheme and algorithms for the strategic energy management are described. Several potential telematics system architectures for acquiring the needed data input are evaluated and the data update process is validated. A backward looking black box simulation model together with pre-recorded trip data are used to determine the fuel consumption. By varying data quality of the potential stops, the existence of preceding buses, which are improving data quality, is simulated.

Keywords—Automatic vehicle location system, ITCS, GPS, V2V-communication, strategic energy management, serial hybrid, city transport, urban buses

I. INTRODUCTION Heavy vehicle emissions do contribute to air quality and

noise problems in cities. New hybrid propulsion solutions employing serial hybrid power trains may help alleviating problems and lowering fuel consumption, if drawbacks like additional weight from electrical power train components are compensated by optimized operation of internal combustion engines. Buses propelled by hybrid power trains are widely used in Northern America, thanks to financial incentives. Hybrid buses were used in trials for some years in Europe and since then, having matured, are beginning to penetrate the markets, mainly as two-axle parallel hybrid but also as serial hybrid concept, also for articulated buses.

The unfinished thesis behind the paper defines and investigates a new approach for a strategic control (e.g. energy management) of the operation of the motor-generator set (genset) of a serial hybrid power train, consisting of an internal combustion engine ICE and an electric generator. In its first part the thesis describes relevant developments of hybrid electric technology, especially for heavy duty transport/buses, secondly the main components explaining their choice and describing the basics of the power train architecture and the control options for the genset. This is followed by an analysis of the suitability of advanced telematics systems refining data accuracy of future presumptive forced stops. The main focus is then on optimizing and validating algorithms for the strategic energy management

e.g. operation of the genset. This control is processing also actual parameters of the power train and data resulting from telematics applications. This paper covers only the part of the thesis investigating the optimization of the set points for the hysteresis control of the state of charge and especially the influence of the data quality for the strategic energy management. The hypothesis to be tested states that the data quality i.e. the amount of presumptive forced stops taken for modulating the switching limits of the genset will improve the fuel consumption

II. BASIC SYSTEM FEATURES

A. Power Train Architecture Since there is much literature comparing different power train

architectures [1], discussion of the pro and cons is avoided here. The simulation of the bus operation was implemented for a serial hybrid electric power train of an urban bus as shown in Fig. 1. Such power trains, having not disclosed genset control, can be found in currently used urban buses in Europe [10], and in the U.S.A [11]. Fig. 1. Modeled serial hybrid electric power train

The shown power train architecture including a high torque

Permanent Magnet Synchronous Motor PMSM driving the vehicle is well suited for transient urban operation, having a high share of acceleration energy demand. Deceleration energy may be stored to a very high extent and used for acceleration afterwards. The lower complexity of the ICE control in this

Strategic Energy Management

ICE Generator

Gear

Energy storage (EDLC)

Electric-machine

Motor Controller

Generator Controller

Buck Boost Converter

2013 International Conference on Connected Vehicles and Expo (ICCVE)

978-1-4799-2491-2/13/$31.00 ©2013 IEEE DOI 10.1109/ICCVE.2013.104713

setting allows focusing more on the development of the strategic energy management, leaving out drivability issues. Energy storage may be implemented using secondary battery cells or electrolytic double layer capacitors EDLC, which do have higher power density figures compared to batteries and are suitable for low amounts of energy to be stored.

B. Tested Control Strategy of the Electric Replenishing Electric energy stemming from the genset and the

regeneration in the electric machine is stored in the EDLC, which does not limit regeneration because of the high power density. The state of charge SOC of the EDLC is floating between the upper level, where the genset is switched off and the lower level where the genset is engaging at the most fuel-efficient operating point. Thus, the chosen so-called hysteresis type control for the energy replenishing of the energy storage is using switching levels of the energy stored. Those levels shall account for the need to store energy into the EDLC when braking but also the need for taking out energy to power an upcoming acceleration process. Additionally, a medium SOC level coupled to the lower engaging limit was introduced in the strategic control, triggering the start of the genset operating at part load. Whilst published literature on energy management is focusing on elevation [2], here the switching limits for the on-off control are modulated using the distance to the presumptive forced stops. The distances used for altering the SOC limits are weighted by the estimated probability of halting, using Formula (1):

(1)

dw weighted distance to potential stops nStops number of potential stops PosStops location of the forced stops on the graph of the trip PosVehicle location of the vehicles position on the graph WeightStopType weight of the presumptive stop

The introduced weight is indicating the probability of a stop, which is different for a bus station compared to a passenger crossing (crosswalk), where buses have to stop only from time to time. The switching SOCs are altered, taking the weighted distance to the presumptive stops as parameter. In addition, the awaited energy turnover in the upcoming segments is taken into consideration influencing the SOCs. Thus, the genset is engaged and switched off at floating SOCs during the drive. Formula (2) and (3) are showing the SOC-modulation with increasing and decreasing influence of the distance:

(2)

(3)

Formula (2) and (3) are showing the SOC-modulation with increasing influence of the distance:

(4)

(5)

SOC0 hi/lo Basis value for SOC hi = higher lo = lower limit fSOC hi/lo Correction factor (to be determined via Monte Carlo Variation) Wfree energy amount that should be storable anyway for the current stretch of the trip Wnec Energy amount needed for accelerating in the next stretch WEDLC maximal energy content EDLC

The modulation of the switching limits of the genset was optimized via Monte-Carlo variation of the parameters, which is used to influence the switching limits. This allowed varying more than two dozen parameters automatically, also including strategic decision about the influence of the distance to the next predicted halt according to formula (2) to (5).

Connecting buses into a collaborative telematics system based on location based information helps identifying the location and probability of a forced stop more precisely. The exact positions of the presumptive forced stops might be improved via additional telematics system to be evaluated. GPS was also used to record the data to simulate the operation.

C. Genset Control The genset control was simulated using pre-defined and

prerecorded trip data and sets of presumptive forced stops. The weighted total of the distance to the presumptive forced stops was calculated for different data quality levels, assuming part of the stops left out. Having also pre-calculated energy data for formula (2) to (5) the genset control was simulated for all trips. By determining the operation of the genset, the fuel demand was totaled for all trips and each data quality variant.

In the thesis, the strategic energy management was assessed via different statistical elaborations, determining the optimal combinations for the influence factors on the genset control featuring an on-off operation scheme. Finally, the transfer of parameters resulting from optimizing parameters on the basis of a subset of measured trip segments towards the total set of measured trips was tested. After these extensive simulations, the model built from blocks using tabular measured efficiency and fuel consumption data for the motors and other electrical components, also considering resistive losses may be regarded as sufficiently valid for testing the hypothesis in this paper.

III. DATA SOURCE FOR THE SIMULATION As input to the simulation, we need basic vehicle data like

mass, drag coefficients and velocities for calculating the drag, and the related power. For determining the modulation on the on-off control of the genset, we also need the positions of the presumptive forced stops. Both data categories are described in the following.

A. Vehicle and Power Train The simulation model was implemented as backward looking

simulation from scratch, using Mathcad and in most cases using measured tabulated efficiency and emission data depending on

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load and RPM of the internal combustion diesel and both electric machines of the serial hybrid power train. The optimal working point for the genset for various loads was pre-calculated, determining the optimum RPM, depending on the voltage difference between EDLC and genset because of the variable losses of the buck boost converter. Efficiency data were stored in matrices, improving the performance of the simulation implemented in Mathcad.

Most of the bus data was taken from one supplier publication [3], details for modeling tire friction and aerodynamic drag had to be collected from literature. The aerodynamic drag has only a little share in the overall drag, due to the low velocities in urban operation. The modeled vehicle is a 13 metric ton two axle urban serial hybrid electric MAN bus of the first generation, having the EDLC on its roof. To allow a real on-off control, the energy capacity of the EDLC was chosen much higher (1.39 kWh) compared to the MAN-version (0.4 kWh [3]).

B. Longitudinal bus movement As from 2000 on, GPS was a good source for obtaining

vehicle data. Logging data from a Bluetooth-connected device positioned near the bus windows in the middle of the vehicle allowed measuring the longitudinal bus movements and its position. From the GPS raw data co-ordinates and velocity were extracted [4]. After overlaying the data (see Fig. 2) three measured trips were regarded as accurate enough to be used for the simulation. By combining legs of those three trips, finally nine data files could be used for simulating real world trips. Fig. 2 shows the original recorded trips and the assumed presumptive forced stops or strong decelerations.

C. Assumed presumptive stops The positions of the presumptive forced stops were derived

from the measured velocity data of the bus, but the co-ordinates were aligned to map data (OpenStreetMap) and classified according to their type (see Fig. 2). This way the errors stemming out of GPS signal refraction in road canyons could be decreased. The distance of the vehicle position to the fixed presumptive forced stops may be determined exactly [5], but mapping the position to the driven length, this information has a certain variance because of the deviation of the integral due to GPS errors and deviations in the trip course itself. This is countered by a correction in the calculation, securing that presumptive stops are identified correctly, even if the totaled trip distance is smaller than the actual vehicles position reveals. With inner urban buses, stops are served most of the time, and furthermore the need to stop may be signaled by the passengers too late for influencing the strategic energy management. All stops were included in the calculation with their weight.

Fig. 2. GPS coordinates of the bus position during the trips

IV. CONNECTIVITY (TELEMATICS ARCHITECTURE) In the following chapter, a feasible telematics architecture is

specified for generating data for the strategic energy management. Feasibility analysis is performed as check of the fitness for use.

A. Options Generating data of presumptive forced stops for the strategic

energy management may be done using static data derived from GIS, or data from other vehicles. Since urban buses are moving head to head, the age of the collected data is low, benefiting its quality. Fig. 3 depicts both the vehicle based position gathering and the collection of data from other sources, not yet greying out those methods having lower feasibility.

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Fig. 3. Potential data sources for the prediction of the vehicle movement

Intersection controllers (traffic light control) which also

handle public transport vehicle preemption, may be equipped with different sensors detecting the velocity of the vehicles near the intersection (Microwave Radar, Infrared, Acoustic array, Video Image Processing [9]) and estimating the arrival time at the intersection based on traffic models. So they are able to answer with the timespan to the next green light to the registered approaching bus. A live link to the bus may also be used to communicate updates of this timespan and potential congestions identified via sensors to the approaching bus like stated in patents [12] [13]. Advanced vehicle location systems AVLS may be integrated with the on board unit of the strategic energy management system, sharing the GPS/GNSS receiver. Radio Data System Traffic Message Channel RDS-TMC data would need a connection to a car radio using FM broadcast.

B. Evaluation In the following, the usefulness of the data sources presented

in Fig. 3 is evaluated for three alternatives using two criteria.

TABLE I. EVALUATION OF DATA SOURCES

Data provision from GIS

Learning system - fed from ITCS

Learning system - stand

alone Flexibility for

different applications

Good only for line based constant operation

Flexible, needs ITCS for improvement

Very flexible

Data quality for strategic

energy management

Starting with a high data quality

Fast increasing quality starting from a low level

Slowly increasing quality, starting from a low level

As shown in table I, there is an offset between flexibility and the ability to start with a high data quality. ITCS may provide

enough data with sufficient age in most cases achieving a good compromise.

All methods may be improved via data from other data sources like indicated in Fig. 3, contributing differently to the data quality. Least useful may be existing RDS-TMC because of the long update intervals and its granularity. Vehicle to vehicle V2V data from other preceding vehicles may be useful, if a longer period of time ahead may be covered. This would however require high penetration of V2V amongst all driving vehicles independent of their nature and require the existence of preceding vehicles in a medium to short time interval (some dozens of seconds). Intersection controllers may input valuable data about the timespan to a green light, but congestion information is necessary, so eventual halts disregarding of the green light may be identified. Finally yet importantly, AVLS (ITCS) using digital radio [6] will serve as data basis, especially for road sections were many buses circulate. Optimally the ITCS comprises all buses from all operators, but even doing so, substituting intersection controllers is not possible for stretches with little public transport coverage.

C. Synthesis of the System Architecture Excluding data source with small value for the strategic

energy management, we get a telematics system relying mostly on the collection of data by busses having the same stops to serve or sharing at least part of them. The data generation process using preceding buses and the central processing and redistribution process is depicted in Fig. 4. All buses are updated with the list of potential stops and the probability of their occurrence and will calculate the weighted distance from their position to the next stops in on-board units. Also in order not to overload the ITCS communication system, actual stops are transmitted triggered by surpassing a certain velocity, and not the whole velocity and position table over time. Fig. 4. Data collection, processing and propagation for the strategic energy management

1. Data acquisition

2. Data transmission

3.Data concentration

3.Data concentration

4.Integrity checking

4.Integrity checking

5. Evaluation of the data

basis

5. Evaluation of the data

basis

6. Propagation of changes

6. Propagation of changes

7. Integration of changes

Strategic energy management

On Board Unit

GIS/maps with forced

stops

RDS TMC Sender

Strategic energy management

Intersection controller

Other preceding Vehicles

RDS-TMC receiver

Wireless packet data receiver

GPS/GNSS- receiver

AVLS (ITCS)

Sensor

GPS/GNSS- receiver

Vehicle based GPS/GNSS and Intersection controller On Board

Controller

Central Server

716

As evaluated in the previous chapter, only data from intersection controllers do have a higher value, all other data could originate from preceding buses transmitted via ITCS.

V. RESULTS

A. Validation of the system architecture The derived system architecture needs to be validated in order

to see if it is fit for the purpose [7]. Table II shows the validation of the system characteristics against the functions of a strategic energy management system.

TABLE II. VERIFICATION AND VALIDATION OF THE FUNCTIONAL SYSTEM ARCHITECTURE

Requirements Validation Consequences Data usage in

the vehicle Data access should be possible even if an update is under progress

Without new data, a fixed data basis shall be used.

Stop communicated to the central

server

No data should get lost Non communicated stops shall be communicated later, aging shall differ for the type of stops

Data consolidated and weighed acc. to its age

A consistent data basis should be accessible

Updates shall be applied on a duplicate data base

Data redistributed

to vehicles

Access to data should not be hampered by data reception

Parallel processes are necessary

Data update in the vehicle

A valid data basis shall be accessible at all time,

A fallback data base shall be loaded

According to the results of table II, the system may function well, but needs a delta update in order to scale with larger fleets, where broadcasting all the data may overload the communication channel of the ITCS.

B. Simulation Results Unfortunately, no possibility was found to test the system in

practice. So in order to see whether data quality in a system, where preceding buses contribute to the data, influences the fuel consumption, simulations had to be run. For the comparison no influence of the status data of the power train (running time genset, EDLC voltage, current etc. ...) was taken to modulate the genset switching limits, but only the weighted distance to presumptive stops was taken.

Before performing the test, the optimal parameters for the influence of the distance to presumptive stops on the switching level were determined employing Monte Carlo Variation using pre-calculated sets of random factors. Fig. 5 shows how the internal losses for electric energy correlate with the fuel consumption for a sample of 2,500 calculations. Increasing the sample in the Monte Carlo variation shows even lower minima for the fuel consumption.

Fig. 5. Electrical losses and fuel consumption as result of the Monte Carlo simulation, circle shows basis variant with fixed switching limits

The marked variant haven no SOC modulation has 3.2%

higher fuel consumption, compared to the optimum derived via Monte Carlo simulation.

The validation test for the influence of the data quality then was made having constant optimal factors previously determined via the Monte Carlo simulation. Modifying the number of identified presumptive forced stops, leaving the factors unchanged, we see the influence of data quality on the fuel consumption. Assuming tabula rasa when a bus starts into its first service, little data of preceding buses is present and the fuel consumption is highest. Of course, in practice buses are not operated only on one line, so tabula rasa would occur too frequently. Therefore, it makes sense to transmit historic and map corrected data for the presumptive forced stop to the bus starting into a new service. During the day, data then data may be updated using weights for the presumptive forced stops and data quality is increasing. Fig. 6 shows the results when increasing the amount of fixed presumptive stops used in the simulation. The data quality was mimicked, excluding some, but always the same points for all simulated trips on a random basis. Via simulating nine trips, the derived total fuel consumption gives a good insight into practice, comparing operation of buses in reality with different data quality. Fig. 6. Fuel consumption depending on the share of potential stops considered

The effect of those data improvements taking information

from preceding buses, starting with 20% of the presumptive stops, however in this setting is small, only approximately one

6.4 6.6 6.8 7 7.2 7.4 7.6 7.8 81.50E+007

2.00E+007

2.50E+007

3.00E+007

3.50E+007

4.00E+007

0

Fuel consumption all trip segments in kg

Ele

ctric

ene

rgy

loss

es in

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717

percent. On the other hand, the overall savings to be expected when modulating the SOC-limits for the on-off control amount to approximately three percent. Therefore, using data from preceding buses via ITCS is not mandatory but it pays of for larger fleets, where the additional investment into the ITCS for managing data for presumptive stops is shared amongst all vehicles in the fleet profiting from it.

C. General Conclusions The simulation has shown that collecting data for potential

stops from other buses is helpful to improve fuel economy further through accounting for the distance to the next potential stop. However having similar driving curves in operation, Monte Carlo Optimization has shown that using electric genset and EDLC data in operation is sufficient for achieving significant savings. The added costs for collecting and processing data increasing the fuel savings by one percent point are acceptable, if larger fleets of serial hybrid buses are equipped with such a strategic energy management. Synergies with ITCS may be exploited, decreasing the cost of such a functionality to improve data quality for strategic energy management.

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