Power Line Communication in a Full Electric Vehicle: Measurements, Modelling and Analysis S. Barmada, M. Raugi, M. Tucci, T. Zheng Department of Electric Systems and Automation University of Pisa Pisa, Italy
[email protected] The applications of PLC systems aboard of vehicles have been investigated only in recent years. As for the automotive field, the available literature mainly consists in a detailed analysis of advantages and disadvantages of the PLC technology used in this special environment [1], a feasibility study relative to the use of a commercial standard into a vehicle [2], an analysis of the power grid of a vehicle and its noise characterization [3] - [5]. [6] and [7] present an analysis of the issues and some field tests; in addition some studies relative to the implementation of a CAN bus over PLC [7] and some patents [8] – [13] of PLC onboard cars, confirming the big industrial interest for this topic.
Abstract—The Power Line Communications technology is now considered as a good alternative for implementing communication grids also in vehicles, with the main scope consisting in reducing the cable harness. In this paper we present the first results relative to a study performed on a fully electric vehicle. Due to the peculiar characteristics of the power grid in electric vehicles (topology and noise), this study is important to verify whether the PLC technology is applicable also in this case. Keywords-Channel modeling, Measurements, in – vehicle power line communications, Electric vehicles.
I.
INTRODUCTION
Nowadays, one of the most promising applications of the PLC technology is its use aboard vehicles. As a matter of fact, in modern transportation systems the data transmission inside a vehicle is operated for different purposes. Data exchange is performed between sensors and actuators, for the operation of devices, for the engine and temperature control, for information and entertainment (the so called infotainment), for the traction control, and in general for the so called x – by – wire applications, in which many driver – machine interfaces are transformed from mechanical to electrically driven. The technological implementation is nowadays done by using different data bus (Local Interconnect Network, Controller Area Network, Media Oriented Systems Transport, FlexRay), depending on the required communication speed and reliability. A modern vehicle of average dimension is characterized by a communication grid of several km, with a constantly increasing number of connection points (more than 200 nowadays). The weight of the wiring harness is second only compared to the engine – gearbox weight. It is not difficult to understand that the complexity of this structure will soon become an issue difficult to manage, also from the diagnostic and maintenance point of view. For this reason the use of power lines to transmit data could reduce this problem, since it would remove part of the cables (or all of them in the best case) for command and control with enormous advantages in terms of weight, space and cost.
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All the previously mentioned contributions ([1] – [13]) are relative to vehicles equipped with an internal combustion engine, i.e. the “standard” vehicles which are the ones most widely available in the market. The trend in nowadays society is driven by both an environmental consciousness and the need of facing the problem of the ever increasing oil’s cost; this leads to a strong increase in the interest towards electric and hybrid cars. Some brands have already commercialized hybrid vehicles (with both an internal combustion and an electric engine) or entirely electric cars. In both cases, the characteristics of the power grid of such cars are completely different from the standard ones, first of all for the presence of an additional sub-grid dedicated to supply and control the electric engine. In the literature applications of the PLC technology in electric and hybrid cars are practically absent, and the study of this new technology is characterized by extreme innovation and potentially important technological advances. The Department of Electric Systems and Automation is involved in the design, study and optimization of electric and hybrid vehicles (cars and motorcycles), for this reason it has been possible to perform a measurement campaign on a fully electric vehicle (in particular a Piaggio Porter), with the aim of evaluating the applicability of the PLC technology. The paper is organized as follows: section II describes the measurements performed; in section III the model used for the simulations is shown, while section IV is dedicated to the
331
analysis of the simulations and measurements data in order to evaluate the performances of the channel.
-30
MEASUREMENTS
A. Channel’s measurement The set of measurements has been performed on different possible channels on the available vehicle. In particular special attention has been dedicated to the connection between the front and rear part of the vehicle, which averagely presents the electrically longer connection. TABLE I.
Channel #
-40 Magnitude (db)
II.
-20
-60
VNA AWG + digitizer
-70
SET OF MEASUREMENTS
End 1
-50
End 2
-80
1
Rear light
Cigarette lighter
2
Rear break light
Cigarette lighter
3
Rear indicator
Cigarette lighter
4
Rear light
Courtesy light
-10
5
Courtesy light
Cigarette lighter
-20
0
5
10
15 Frequency (MHz)
20
25
30
Figure 1. Channel #1 insertion gain
-30 Magnitude (db)
The reason why the cigarette lighter and the courtesy light have been chosen as terminals in the front part of the vehicle is simply for easy access if compared to the fuse box, which is the most common choice. Each single channel has been measured in different operating conditions (lights on and off, ignition key in different positions etc), hence a wide set of data is now available. The measurement setup consisted in a Agilent Vector Network Analyzer (VNA) (which can be connected to both ends because of the dimensions of the vehicle) and in an Arbitrary Waveform Generator (AWG) at the transmitter size with a National Instruments digitizer at the receiver side. These two different setups have been used in order to evaluate the effect of the averaging performed by the VNA on the insertion gain with respect to the measurement performed by the AWG, which operates on a single recording in time and performs a post processing in order to obtain the frequency domain data. In both cases the terminations were set to 50Ω. The VNA frequency range was set to [100kHz – 30 MHz], with 1024 frequency samples; regarding the second setup, the AWG works imposing a multitone within a frequency band of [1 30] MHz, using a sampling time of 100 MS/s. The post processing leads to a number of 2000 frequency samples in the above range In this first contribution relative to the study, we focus our attention on channel #1 (rear light – cigarette lighter): in Figure 1 the insertion gain measurements performed by the use of the two different setups are shown; the averaging operation performed by the VNA is clearly evident, however a satisfactory agreement is obtained. The same consistency between the two methods is present also for the other measurements, hence from now on only the VNA results will be shown.
-40
-50
-60
Keys off key position I key position II key position II + lights
-70
-80
0
5
10
15 Frequency (MHz)
20
25
30
Figure 2. Channel #1 in different configurations (insertion gain)
Figure 2 shows the measurements performed in different vehicle’s configuration:
all off;
key in position I (battery connected for a limited number of services)
key in position II (battery connected)
key in position II with front light on.
The key position I gives energy to some of the car auxiliary services (lights, radio, cigarette lighter etc.) while the key in position II energizes all the auxiliary services. Figure 3 shows the Cumulative Density function relative to channel #1 in the same configurations as Figure 2, defined as follows:
CDF G G
332
f : S
f f
21
G
(1)
where G is the threshold and f is the set of discrete frequencies at which the insertion gain has been measured by the VNA.
0.15
0.1 0
Voltage (V)
Cumulative density function
10
-1
10
0.05
0
-0.05
-2
10
-0.1 -6
Keys off key position I key position II key position II + lights
-4
-70
-60
-50
-40 Threshold G (db)
-30
-20
0 time (s)
2
4
6 -5
x 10
Figure 4. Time domain noise
-3
10
-2
-10
-70 -75
Figure 3. Cumulative density function of channel #1
-80
As we can see from Figure 3, 90% of the insertion gains are under -25 db with an average value between -27db and -33db. As a first result of this analysis we can state that channel #1 is a potentially good channel for the implementation of PLC technology, and the presence of a fully electric engine does not constitute a problem in terms of channel’s performance. This result can be drawn because also the other measured channels exhibit a good behavior. B. Noise measurement The most important unique feature of this vehicle is the fully electric engine, for this reason the power grid is structured in two different levels (auxiliary services and motor drive) which are not electrically independent. This causes the noise produced by the electric drive (the switching of the semiconductors) to be present in each of the analyzed channel. The noise has been separately and accurately characterized by measuring its level at both terminals of channel #1. In particular the noise has been recorded during a significant time interval, with a sampling frequency of 200MHz. Figure 4 shows the time domain noise at one end of channel #1, while Figure 5 shows the Power Spectral Density in the whole frequency range [0 100] MHz. The PSD calculation has been performed offline for the noise measured at both ends of channel #1, and no significant difference has been evidenced. The noise of Figures 4 and 5 is created by the motor drive, hence it is present with this magnitude during the regular operating conditions on the vehicle.
333
Noise level (dBm/Hz)
-85 -90 -95 -100 -105 -110 -115 -120
0
20
40
60 Frequency (MHz)
80
100
120
Figure 5. Power spectral density of the noise
III.
NETWORK MODEL
A. Per unit length parameters calculation Since information on the loads are not fully available, and a load characterization has not yet been performed, the channel model which we have implemented and compared with the measurements is the one relative to the “all off” configuration, in which all the terminations are open circuits. This also represents the worst case for frequency selective characteristics of the transfer function. In order to construct the model, the p.u.l. parameters of the cables need to be known: for this reason a set of measurement of each cable type has been performed by the use of the VNA. The S-parameters s11 and s12 have been measured and according to the standard propagation equations ([14], [15]), the parameters K, Z and have been calculated by the use of
Let us consider a wavelet basis on the interval 0,Tmax . As
1/ 2
S 2 S 2 12 2S 2 21 11 11 K 2 2S21 Z 2 Z 02
1 S 1 S 11
e
L
2
11
2
S 212
instants t1 , t2 ,..., tM . Let w t be the vector of the elements of the basis. Throughout the paper the symbol “^” denotes quantities in the wavelet domain while bold characters and symbols are used for vectors and matrices. Given a function of f t its wavelet expansion is: time
(4)
S 212
1 S112 S 212 K 2 S 21
functions known the basis consists of M 2n w1 t , w2 t ,..., wM t which are defined at equally spaced
1
M
f t f k wk t w t fˆ
The measurements were carried out by an Agilent VNA which uses 50 input and output reference impedances. On the basis of these quantities, knowing that
R j L G jC j
Z
R j L G jC
to
this
The representation of the scattering matrix in the wavelet domain is widely discussed in [16]. It consists of a real matrix relating the wavelet coefficients of the incident and reflected power waves at the terminals of the multiport. Let us consider the wavelet coefficients of the incident and reflected waves and stack them to obtain two vectors of elements:
(5)
bˆ1 aˆ1 ˆ a2 ˆ bˆ2 , b aˆ ˆ bˆ a N N
R Re Z G Re / Z
according
T convention, fˆ f1 , f 2 ,..., f M is the vector of the wavelet coefficients and the superscript T denotes the transposition.
it is possible to calculate the per unit length parameters straightforwardly as L Im Z /
where,
k 1
(6)
C Im / Z /
(2)
(where N is the number of the ports of the complete network). The scattering matrix in the wavelet domain Sˆ is a square matrix of order N M defined by:
With the topology of the network and the p.u.l. parameters available the model has been created in using a wavelet based scattering matrix approach.
ˆˆ bˆ Sa
B. Channel model A power distribution system can be considered as a network constituted by the connection of multiports as shown in fig. 6. Let us suppose that the scattering matrices of each multiport, with respect to a reference impedance matrix, are known either by measurements or numerically evaluated. Throughout the paper we will indicate the single multiports composing the network as Si, while the whole interconnection with S.
(3)
Its dimension depends on the dimension M of the adopted wavelet basis whose choice has to be carried out as a tradeoff between accuracy (higher for greater values of M) and CPU time (shorter for lower values of M). As shown in [17] section II.E, in most applications for the frequency of interest for PLC systems, a basis of M 128 functions on the interval gives good results in terms of accuracy. The scattering matrix of a network composed by an interconnection of multiports, i characterized by their scattering matrices Sˆ , can be efficiently evaluated by using an incremental procedure described in [18]. The characteristics of the resulting scattering matrix in the wavelet domain and its sparse nature are extensively addressed in [19] – [20]. The model build as described has been used to perform some data transmission simulations in order to evaluate the channel’s performance. IV.
SIMULATION RESULTS
At first, a simulation of channel #1 in condition 1 (all off) has been performed and compared to the measured data. The result of this comparison is shown in Figure 7 showing a satisfactory agreement between the insertion gains.
Figure 6. An example of interconnect of multiports
334
-20
-65
0.9
-60
0.8
-30 -55
Power (dbm/Hz)
Magnitude (db)
0.7
-50
-40
-50
-60
0.6
-45
0.5
-40
0.4
-35
0.3
-30
-70
-80
measured simulated 0
5
10
15 Frequency (MHz)
20
25
0.2
-25
0.1
-20 0
30
0.5
1
1.5 Frequency (Hz)
2
2.5
3 7
x 10
Figure 8. Symbol error rate as a function of frequency and transmitted power
Figure 7. Comparison between simulated and measured data for channel I.
A set of simulations have been performed to evaluate the channel’s performance in a PLC transmission operations. The simulation are performed as follows: a number of 1024 random symbols are modulated according to a 16 QAM scheme, then the corresponding OFDM frame is “transmitted”; the signal received is then transformed into a time series by the use of the IFFT. Afterwards, the noise measured at the receiver (the one reported in Figure 5) has been added to the received signal, and the so obtained data have been equalized in frequency according to the transfer function of the channel. The result is then demodulated and compared to the transmitted QAM constellation allowing the calculation of the symbol error rate. This whole process has been performed for a statistically significant set of data.
0 [0 30] MHz [2 20 MHz]
Symbol Error Rate (log10)
-0.5
In Figure 9, the transmission is operated over the whole frequency range [0 30]MHz and in the reduced range [2 20] MHz: it is clear that if the transmission is performed in the range [2 30] MHz satisfactory performances are obtained with transmitted powers above -20dbm/Hz. In this case a complete study on the possible violations of EMC constraints related to the presence of other devices and the passengers should be performed.
-1.5
-2
The results are shown in the greyscale map of Figure 8, while Figure 9 shows the symbol error rate in logarithmic scale as a function of the the transmitted power. The following considerations can be done analyzing the simulations results: looking at Figure 8 it is evident that good performances (in terms of symbol error rate) are obtained in those subranges where the noise has a low PSD. On the other hand, at frequencies where the noise is high (centered on 0.8 MHz and 2.5 MHz) the symbol error rate is higher.
-1
-2.5 -65
-60
-55
-50 -45 -40 -35 -30 Power Spectral Density (dBm/Hz)
-25
-20
-15
Figure 9. Symbol error rate a s a function of PSD
V. CONCLUSIONS In this paper a first analysis of PLC on a fully electric vehicle has been performed: a set of measurements of channel’s insertion gain and of the drive’s produced noise (peculiar of this environment) has been carried out. The result of this study is that the implementation of PLC technology onboard this vehicle is possible, however the noise introduced by the motor drive leads to the need of an advanced transmission technique, in order to achieve reliable communications performances.
On the other hand, better performances with lower transmitted power could be obtained by selectring a subrange avoding the presences of strong fading. The choice of the range [2 20]MHz was mad analyzing Figure 8, and the difference in the overall symbol error rate is absolutely evident.
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