As mentioned in section 5.2.1, STC is the PV industry convention for bench- marking the conversion efficiency of a PV cell under a set of specific testing conditions. Although STC is widely accepted as the industry standard, there are certain researchers asserted that these measuring conditions do not correspond to the real operating conditions (Bucheret al., 1998; Lamet al., 2004). The following section shall then discuss various factors that would affect the electrical char- acteristics and performance of PV cells to help the readers in better planning for harvesting solar energy for EVs.
5.3.1 Does PV technology matter?
Different types of PV technology were briefly introduced in section 5.2. Due to the longer history of development, crystalline silicon PV modules are still more easily available as proprietary products and thus widely being applied. But due to their fragility, they have to be well protected by layers of front glass and backing or substrate. This intrinsic nature of crystalline silicon determines the formation and may limit its applications on EVs. Thin-film technology, on the other hand, can be applied on more options of substrate. For thin-film PV, flexible modules are also available besides glass-glass modules and glass-film modules that are common in crystalline silicon technology. The size, form and material used are all factors to be considered during the design process. Hence, a wider range of choices of materials for embedding the PV cells is an advantage of thin-film modules over crystalline
silicon. Nevertheless, the cost and ease in fixing the PV cells onto the body of the vehicle is another important factor to consider.
Furthermore, the electrical properties also vary with different technology other than the structural formation. The ability of capturing the light from the sun and convert it into electricity differs from technology to technology. Even with the same technology, the power it delivers is not a constant when the environmental conditions are changing. Durischet al.(2001) proposed that different technologies of PV could be characterised in a general formula relating the cell efficiencyhof the PV cell as a function of global irradiance Gn(impinging vertically onto the module), cell temperature J and relative air mass (AM). When represented in a mathematical manner, it is:
hẳf Gf n;J;AMg (5.1)
Instantaneous power would be calculated by multiplying the cell efficiency to the solar resources available, which is the product of global irradiance per unit area and total cell area:
PPV ẳhGnAPV (5.2)
In subsequent research, Durischet al.(2002) demonstrated the same functional dependence ofhas in (5.1) could be applied to different types of PV technologies, and generalised the model as:
hẳp qGn Gn0
þ Gn Gn0
m
1þrJ
J0þSAM AM0
(5.3) The parameters p,q, r, mands are to be determined for each type of tech- nology individually. Whereas Gn0,J0andAM0are the reference values of global irradiance, cell temperature and relative air mass at STC, respectively. To illustrate the importance of this dependence, a two-dimensional graph of PV cell efficiency against cell temperature is shown in Figure 5.5. From the graph, it is clear that the ability of the PV cell to convert the sunlight into electricity decreases linearly with the rise in cell temperature. This decrease is rather significant, and is denoted by the term ‘Temperature Coefficient’. For example, for a first generation CIS module tested, it represents a relative decrease of 20% in conversion efficiency if we compare the PV cell operating at 70oC with its STC efficiency. In the design of PV system, the ventilation of the PV modules would therefore be very critical. To conclude, the choice of technology would have an impact on the real performance of the PV system.
From (5.3), we can see that besides the cell temperature, other important factors affecting the power output from the PV cells are the global solar irradianceGnand the relative air mass. Amongst which, the solar irradiance is the most important factor. First, the output is directly related to the amount of solar resources available – the solar irradiance. Second, Durisch et al. (2002) demonstrated that the con- version efficiency of a PV cell is a function of the solar irradiance as well.
Solar energy harvesting for electric vehicles 139
By substituting (5.3) into (5.2), the power output from the PV cells with areaAPV
would then be:
PPV ẳp qG2n
Gn0þ Gmþ1n Gmn0
!
" #
1þrJc
Jc0
þSAM AM0
APV (5.4)
For more detailed analysis on the responses of different PV technologies to changes in the environmental conditions, the readers may refer to Durisch et al.
(2007). The important point to note is that we cannot simply take the STC efficiency in estimating the power output from the PV cells mounted onto the EVs since the actual operating conditions would affect significantly the effective energy yield. Hence, a careful design process involves a better understanding on the specific responses.
One of the ways is to apply the above empirical models developed by Durisch.
5.3.2 Energy yield calculations
Once the parametersp,q,r,mandsin (5.4) of a particular type of PV cell were made available, then a detailed energy yield calculation under different weather conditions can be performed. This calculation would allow the designer to have a better understanding on the possibility of the EV under investigation could be powered entirely by PV or not. The designer could then also estimate when and by how much extra energy needed to charge the battery in case the energy from the PV alone could not support the prescribed mileage. The general formula in estimating
14 12 10 8 6 4 2 0
20 30
PV cell efficiency (%)
40 50 60 70 80 90
Cell temperature (°C)
Figure 5.5 A first generation CIS PV cell efficiency varies with PV cell temperature.
the total output from the PV cells during a specific period could be found by:
EẳXn
kẳ0
PPVDtk (5.5)
In the above equation, the parameter Dtk is the incremental duration of time during which the independent variables in (5.4) is constant enough for an accurate calculation of energy yield from PV during that period of time. Whereas nrepre- sents the total number of incremental time period for the duration of evaluation. For example, if environmental data for one whole year is input to the equation, and the Dtk is 1 h, then,nẳ24365ẳ8,760. Although it is widely accepted that hourly environmental data are used for PV systems energy yield calculation, Gansleret al.
(1995) asserted that hourly data are only suitable for systems responding slowly or linearly to changes in solar radiation. This is unquestionably not the case for PV systems which are responding almost instantaneously to changes in solar irra- diance. Also, Durischet al.(2002) successfully demonstrated that the change in PV conversion efficiency with respect to solar irradiance is non-linear. Gansler et al.
(1995) showed that the error in estimation of PV energy yield by using hourly solar radiation data as compared with that of 1-min data could be as high as 35%. Hence, the author would recommend adoptingDtkto be 1 min whenever possible for a more accurate energy yield estimation. Furthermore, if the analysis requires estimation of instantaneous power output, the use of high resolution data at 1-min interval would be more appropriate. The application of (5.4) and (5.5) in designing PV systems for EVs shall be illustrated with two case studies mentioned in section 5.4.
5.3.3 Power management for EVs
One particular characteristic about PV technology is the photo-electric effect of the semi-conductor. The photo-current is converted from the energy contained in the photons falling onto the surface of the semiconductor at specific physical condi- tions. Castan˜er and Silvestre (2002) summarised the electrical characteristics of a PV cell as follows:
ImẳISCI0eðVmỵImRSị=VT1
(5.6) VmẳVTln 1ỵISCIm
ISC eðVOC=VTị1
ImRS (5.7)
In the above equations,ImandVmare maximum current and maximum voltage values at maximum power point (MPP), respectively; whereasVOCandISCare the open circuit voltage and short circuit current, respectively. These are the important parameters for analysis on the electrical power output of the PV cells. It should be noted that the electrical characteristics of a PV cell is non-linear. The typical current–voltage characteristics when plotted together is called the IV curve and is shown in Figure 5.6.
Hence the maximum power could only occur atImandVmwhich are constantly shifting according to different environmental conditions. Continuous adjustment is Solar energy harvesting for electric vehicles 141
therefore needed to capture the maximum output from the PV Cell, and the process is called maximum power point tracking (MPPT). After regulating the output cur- rent and voltage of the PV cell, the power available would then be fixed according to the solar resources and other physical conditions. In the case of ordinary grid- connecting systems, the DC power will be converted to AC and then fed into the electricity grid. But for the case of EVs, since the system would be operating as DC, it will remain to be so for the rest of the conversion process. A few more steps are involved in the process of power management.
For application in EVs, the next step after MPPT would be voltage regulation.
Since the MPP voltageVmmay not be the optimal voltage for battery charging or driving the motor in case of the EVs, a second step of DC–DC voltage conversion is therefore needed. Besides the MPPT mode, there could exist other modes of operation during the time when the battery in the EV is not delivering power to the motor, dependent on the state of charge of the battery. The three basic modes of battery charging can be summarised as:
● Constant current mode: when the battery voltage falls below the cut-off vol- tage, the battery is charged using constant current which is the minimum of the inductor peak current and the PV peak current
● MPPT mode: between cut-off voltage and float voltage, the battery is charged in the MPPT mode. The MPPT controller will continuously track the peak power point of the PC cells. The MPPT calibration of the PV cell is done periodically as well as can be triggered by sudden changes in PV output vol- tage. The current through the battery at any time during the charging is given by power from the PV cells divided by the battery voltage.
● Trickle charge mode: when battery state of charge (SoC) indication is full with voltage (equals to the float voltage), the battery is trickle charged with minimal duty cycle applying to the synchronous buck converter.
Current (A) Power (W)
Voltage (V) Pm
Vm
Im
0
Figure 5.6 A typical IV curve of a PV cell
These three different modes can be related to the battery voltage as shown in Figure 5.7.
The above charging mechanism is only detailing the relationship between PV and the battery. The modes of operation would be much more complex when the other sources of power together with the regenerative load – the motor is taken into consideration (it could be even more so with hybrid EVs). One of the possible ways to handle this complexity is to separate the charge bus and discharge bus, although the discharge bus would accept charging current from the motor during regen- erative braking. To conclude, the mode of operation of the PV cells is determined by a number of factors, and their operation at MPP may not always be possible.
A simplified schematic diagram showing one of the possible PMSs for EVs incorporating PV is shown in Figure 5.8.
From the above simplified diagram, it can be observed that the energy loss in different stages could vary a lot in different operation mode. In case the EV is
Constant
current mode MPPT charge
mode Trickle
charge mode Battery draws power only to compensate loss
due to leakage The PV operates at the
maximum power point
Cut-off voltage Float voltage Battery voltage (V)
Battery charging is limited to a programmable maximum current
Figure 5.7 Three charging modes of the MPPT controller for PV cells
PV Charge controller
with MPPT
Overcurrent/
disconnect device
Discharge bus
To control and regenerative motor on EV AC from charging station
Battery charger
Battery
Charge bus O/D
O/D
O/D O/D
O/D O/D
Figure 5.8 The simplified schematic diagram of the possible PMSs for PV on EV Solar energy harvesting for electric vehicles 143
running on the road under the sunlight, the electricity generated from the PV cells can be utilised more directly without going through the processes of charging and discharging of battery. During regenerative braking, however, the power from PV would be used to charge up the battery for future use. Furthermore, the efficiency of charging of battery depends on the mode of charging as shown in Figure 5.7.
Hence, the overall efficiency of power conversion from PV to the final step of driving the EV may not be easily estimated. Despite these limitations, applying (5.5) is still useful in evaluating the maximum energy available from PV cells on the EV over a specific period of time. This valuable information can be used for further improving the overall efficiency of the EVs. The idea of incorporating the modelling of PV for aiding the PMS on-board will be discussed in the next section.
5.3.4 Incorporating solar energy into PMS
The power output from the PV cells is not a constant but depends on the environ- mental factors, the PV characteristics and the operating conditions. One of the most important nature of a PV cell is its passiveness in output power and the inter- mittence. This intermittence can be attributed to the environmental factors influ- encing the PV cell, and also to the environment that it situated (e.g. when it is mounted directly onto the body of EV, its operating temperature will be affected seriously). This intermittent property of PV requires some sort of energy storage and power management. An accurate forecast on the PV power production would also be a useful tool in ensuring the system stability and reliability when the supply and load can be predicted with higher resolution. Furthermore, the PMS is playing an important role to regulate the power flow for enhancing energy efficiency and system stability. If the precise knowledge of the efficiency characteristics of dif- ferent PV technologies is available, then the modelling of instantaneous power output from the PV cells can be realised as part of the PMS in providing necessary control strategy for the power flow. This can be achieved by incorporating a mathematical model of different PV technologies that can predict the instantaneous output given suitable input parameters. Also, the proper control of the power flow with the PMS dependent on accurate monitoring of both the environmental and electrical parameters. To fulfil this task when part of the power on-board the EV is provided by the PV, an accurate estimation of the PV power output is essential in providing information necessary for the control strategy. A possible schematic diagram showing the control mechanism is shown in Figure 5.9.
5.3.5 Harvesting solar energy for charging station
Besides harvesting solar energy on-board the EVs, there are also other beneficial techniques to harvest solar energy for EVs. One of them is by means of installing PV modules onto the roof-top of the charging station, and providing part of the electricity necessary for charging the EVs. The common way to accomplish this is to connect the power output from PV to the electricity grid on the AC side and charge the EVs via the parallel AC supplies. The advantages of adopting this approach include the availability of proprietary grid-connect inverters for PV, ease
in operation and maintenance of the systems, etc. The power loss, however, through this common scheme would be a bit higher than coupling the power output from PV on the DC side. A proper DC charging network coupled with renewable energy sources with DC in nature could further enhance the overall system efficiency. This is because one less step of conversion would be needed in such cases, where the DC output (e.g. from PV) can be utilised directly in the charging of EVs, without the need to convert to AC, then back to DC for charging. In either case of DC or AC coupling, the utilisation of the solar energy will no longer be limited to charging of the battery in one EV. There could be the possibility of charging multiple EVs within the same station and hence the energy loss in the forced mode of trickle charge would be minimised. Additionally, during the period when there is no EV within the charging station, the surplus electrical power can be delivered to the grid via the grid-connecting mechanism. The generation profile of PV usually peaks around noon time and therefore coincides with the peak loads of the local elec- tricity network if the charging station situated in cities. This characteristic enabling the charging station with PV installed poses a very good potential in supplying part of the peak electricity loads. If smart grid control is in place, then the flexibility in managing the electrical power flow will be higher since there could be moments when the charging station is taking energy from the battery in the EVs and supply back to the grid during some peak load situations. In such cases, the developed mathematical model of different PV technologies would be very useful in providing valuable information in forecasting the instantaneous output of the PV under different operating conditions. This accurate prediction of the performance of the PV system is crucial to the control strategy in a smart grid implementation.
One of the obvious applications in smart grid control that can be related to PV power generation would be the load-shifting control. It is the reduction of elec- tricity consumption during peak periods, by means of re-scheduling of some of the
Time
Solar radiation
Ambient temp.
Battery status
PV Mathematical
model of PV
Instantaneous power output
PMS Control
Motor Motor characteristics PMS
Load status
Control algorithm
Figure 5.9 Data-flow diagram of the PMSs incorporating PV on EV Solar energy harvesting for electric vehicles 145
controllable loads (Paracha and Doulai, 1998). The control process involves sophisticated load profile monitoring, prediction, and then finally decision-making by means of artificial intelligence. The contribution from PV systems would then be critical if certain part of the electrical load is going to be supplied by the sun. In performing the function of load-shifting, one of the major pre-requisites is the ability of the smart grid control to predict the shape of the load profile in the day, so that re-scheduling of loads would then be possible. When the smart grid has better knowledge on both the load profile (the demand) and the grid in co-generation with the PV system, its ability in deciding on the load shifting strategy would be more accurate. The resulting load profile will be taken as the total power requirement of the entire local power network obtained by subtracting the contribution of the PV system from the actual load before any smart grid control strategy. The battery in the EVs connected to the charging station then would be valuable in its capability and flexibility in providing buffer for getting surplus power from grid supple- mented with PV; and also possibly delivering power into the grid when needed. For further details about the PV modelling in smart grid applications, the reader may refer to Lamet al.(2011).