Reliable Residential Backup Power Control System Through Home to Plug-In Electric Vehicle (H2V)

  • Mahdi Shafaati Shemami
  • Mohammad Saad Alam
  • M. S. Jamil Asghar
Original Paper


Electric vehicles (EV) are being commercialized to overcome engine emissions and boost performance of vehicle. Peak power management of grid is also accomplished through EVs with vehicle-to-grid (V2G) and vehicle-to-home (V2H) capabilities. In a smart grid environment, these strategies assist in managing peak load management of the grid and home respectively. This paper proposes the use of EV as a regular standby power supply for home in addition to its use as an EV. EVs availability and usage developed by different drive patterns and operating modes of the vehicle. Unscheduled charging of the EV through grid case too much stress on the grid, so the ability of the vehicle to take power from the roof mounted solar panel and feed to the domestic load in different seasons of the year is discussed. The hardware in the loop model of the proposed home-to-vehicle (H2V) system is developed and results are obtained for different operating modes. The obtained result has shown significant promise to the suggested H2V strategy.


Electric vehicle (EV) Energy storage units (ESU) Photovoltaic arrays (PVAs) Home to vehicle (H2V) 


There is a global concern of reducing greenhouse gas emissions and to find the approaches of providing cleaner energy. These challenges have directed the automotive industry toward introducing electric vehicles (EV) in the market [1, 2]. The need of improving energy efficiency and fuel efficiency further catalyzed the automotive sector to develop EVs; this will increase the number of EVs in the near future. For instance, the U.S. Department of Energy estimates that there will be more than 1.5 million by the end of 2016 [3, 4]. The EV, either in the form of the plug-in hybrid electric vehicle (PHEV) or battery electric vehicle, allows the energy storage units (ESU) to be charged either through the electrical grid during vehicle downtimes, or by renewable power sources [5, 6, 7]. The profile of the load imposed on a power system by grid-charging of the onboard battery pack of electric and plug-in hybrid vehicles was studied [8, 9] it shows that unschueled charging has bad effect on the grid. So prediction of driver charging habit is necessary, the study [10, 11] utilizes a large database of field-recorded driving cycles which are stamped with parking times and locations to predict realistic driving habits of drivers in an urban setting.

The impact of charging of EV batteries on distribution grid is analyzed in terms of power losses and voltage deviations [12, 13]. EVs are charge immediately when they are plugged in or after a fixed start delay, without coordination of the charging [9, 14]. This type of power consumption on a local scale can lead to grid pressure [15]. The coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor [16, 17].

EV can be into aggregators to provide auxiliary services such as frequency regulation. When the grid requires frequency regulation service to adjust the grid frequency, the EV contributing in to provide the ancillary services by either take energy (as it is usually done to charge the vehicle) or give power to the grid by help of the vehicle-to-grid (V2G) interface [18]. These EV performing V2G ancillary services can cause significant economic benefits without degrading vehicle performance [19]. However, using an EV means that the main part of the vehicle energy comes from the grid to charge the EVs battery. Increasing the number of nuclear and thermal power plants to supply these new needs will not solve the problem of pollution or fossil fuel depletion [20]. It is not easy to change thermal power plant to renewable energy based power plant so with such issues especially in developing and underdeveloped countries, it is necessary to find the solution to overcome this problem. One solution is the V2H concept that EV can take power from home or give it back to home but still, the power consumption direction is from the grid to the house to charge the vehicle [21, 22]. A fuzzy logic inference method is applied for power controlling and utilization of PEVs for home loads. It emulates the decision-making method of charging/discharging of the PEV battery through priority decision making of power management and power supply of an ordinary home in India [23].

In [24, 25] the idea of sharing the energy between the home and the vehicle and use the energy from vehicle to home (V2H) to supply power to the home appliances during the grid peak demand (i.e., when the prices are the highest), is presented. Thus, making the utilization of EV for peak power management of utility grid is also one of EV utilization that explained [26, 27]. Besides, still charging the vehicle from the power grid is remaining and it case problem [28], especially in developing countries like India where there always is a shortage of electricity, currently more than 250 million people in rural areas do not access to power [29]. Frequent scheduled and unscheduled load shedding for hours are common in urban and suburban areas. Repeated interruption of power supply from the utility grid, several times within few minutes [30]. People use conventional dc-to-ac inverters with a lead acid battery for a standby power supply to meet the basic/essential home-loads (emergency loads) demand during load shedding hours. Therefore, it was suggested that solar PV power supply is a good choice [30]. The cost and payback period of solar PV is decreasing day by day with the rapid growth in PV technology [29]. Therefore, PV modules are becoming more economical and being used in many applications.

This research paper proposes the concept of using an electric vehicle as a load of home that is called H2V strategy. In this work, the EV as a load of home can be charged by the grid but the main contribution is utilization of the PV panel to charge the vehicle to reduce the effect of EV charging on the grid. Afterall EV supply power to a house during blackout/power failure, making EV suitable for developing and underdeveloped countries, where the reliability of electric power is poor. To optimize the performance of the vehicle only emergency load (lightening etc.) and normal load (cooler, fans, etc.) is connected to it. Solar charging of the electric vehicle is proposed due to advantages of being green energy (without burning of fossil fuel) and following up the India National Solar Mission of India. Typical drive patterns, according to the vehicle usage, are also developed to access its availability. This assists in annual control strategy of the vehicle because the grid power is scanty in such countries and thus, the vehicle is dependent on solar power.

The remainder of the paper is arranged as follows. In “Basic Structure of H2V and V2H Strategies” basic concept of H2V system has been described. Detailed proposed work and modes of H2V and V2H system described in “Proposed Home to Vehicle (H2V) and Vehicle to Home (V2H)”. The modeling and simulation of the work in MATLAB plus case study results has been discussed in “Modeling and Simulation of H2V System”. The hardware in the loop experimental setup for case case study discussed in “Hardware in the Loop Experimental Setup”, and finally conclusion and future work has been discussed in “Conclusion”.

Basic Structure of H2V and V2H Strategies

The block diagram view of the concept that performing H2V and V2H power interaction is shown in Fig. 1. It consists grid as primary source and PVA as a secondary power source. EV considered as a load and source that depends on the control strategy it will charge or discharge. So at the time of charging it effect on the grid. The other load consist of four types of loads; 1) normal AC power consuming loads (fans, lights, laptops, water coolers), 2) high AC power consuming loads (air conditioner, heater, microwave oven, Washing Machin), 3) emergency loads (essential loads like fans and lights), 4) DC loads (DC fans, DC lights, mobile chargers).
Fig. 1

Basic concept of Home to Vehicle structure

Proposed Home to Vehicle (H2V) and Vehicle to Home (V2H)

Architecture of the System

Figure 2 shows the detailed block diagram of the system. It consists of two controllers that controller 1 is related to the options of reliable power mode and economical power mode. In case of reliable power supply, battery charging through grid is preferred for a reliable backup. For an economical mode, the total power supplied to load is from PV or the battery which store the PV power. Thus complete utilization of solar energy is possible and in this mode, charging of the battery is never done from the mains. It interacts the normal load and emergency load with the power supply either from PV, mains or EV. The controller 2 allows the load to be connected in the economy mode which depends upon the state-of-charge (SOC) of the battery. Paper [31] shows the details of innovative State-of-Charge (SOC) estimator that is developed through machine learning approach. The load gets connected to the mains when SOC is low and PV fails to supply sufficient power required by the load circuit. Battery operate in either of the two conditions, (i) battery gets charge and discharge simultaneously when solar power is available and (ii) battery will be getting discharge only. Other components of the system are ESU, ac-dc converter and EV boost converter are the components of EV relevant to this research. The PVA and power grid charges ESU through PCU and ac-dc converter respectively. The home takes its power requirements from the utility grid, but during the periods of power outage, ESU delivers power to the house through the dc-ac converter.
Fig. 2

Block diagram showing the details of the proposed H2V system

Vehicle Operation

Figure 3, shows the details block diagram of the EV and general view of H2V system. ESU, dc-ac converter with filter and EV boost converter are the components of EV. The PV panel is situated on the roof-rack of EV. The PVA and power grid charges ESU through PVA boost converter and ac-dc on-board unidirectional converter respectively according to the control algorithm as shown in Fig. 4. The CAN communication has been used to deliver and receive the status of EV and the controllers connected to EV. The home satisfies its power requirements from rooftop solar PV module and utility grid as discussed in hierarchical control. However when both the power sources could not be able to feed the power to the home load then ESU delivers power to the home load through the dc-ac converter.
Fig. 3

The details block diagram of the EV and general view of H2V

Fig. 4

The control algorithm of EV

The typical drive patterns of the EV on the basis of its usage are developed and shown in Fig. 5. The use of the vehicle on every hour is classified in any of the following three states: Weekdays, Weekends: Traveling, Weekends Holiday.
Fig. 5

Drive patterns of the PEV

Operating Modes

The operation of the system in the proposed strategy is categorized into four modes: Mode–I: (Direct grid transfer), Mode–II: (Direct grid transfer PVA charging–Grid connected rectification), Mode–III: (PVA charging–EV connected inversion), Mode–IV: (EV connected inversion). The modes of operation are shown in Figs. 678 and 9 and described as following subsections.
Fig. 6

Model I, direct grid transfer

Fig. 7

Mode II, PVA charging and grid connected rectification

Fig. 8

Mode III, PVA charging and EV connected inversion

Fig. 9

Mode IV, emergency back-up power

Mode–I: Direct Grid Transfer

In this mode, power is supplied by the grid. The EV is not charged previously so it is connected to the grid through controller 1. HPCL of the home also connected to the grid directly by controller 1. Other home loads are connected to the grid through controller 2 and 1.

Mode–II: PVA Charging and Grid-Connected Rectification

In this case, NPCL of home receives power from the grid, PVA charges EV besides grid. PVA boost converter is the interface between vehicle and PVA. If PVA does not produce power, the operation shifts to Mode-I or III.
Table 1

Details of different components of H2V system



PEV boost converter

DC-AC converter

AC-DC converter

PVA boost converter

Battery pack


Battery units


Rated capacity



of each unit (Ah)



Nominal voltage (V)


Inductance (mH)




Output capacitance (μ F)




Operating frequency (kHz)




Duty ratio (pu)




Power electronic device



Switching technique

Bipolar PWM

Modulation index (pu)


Carrier frequency (Hz)


Output frequency (Hz)



Snubber resistance (kΩ)





Delay angle (degrees)


Input frequency (Hz)


LCL filter (mH, μ F)

200, 900

Mode–III: PVA Charging and EV Connected Inversion

This is the most pleasing operating mode of the system. In this mode, the vehicle is charged with PVA. The grid power is unavailable due to an outage (or load shedding). The vehicle supplies the household load. Thus, EV works as a backup power source. This mode continues till the grid power is restored or the user wants to use the vehicle for traveling.

MODE IV: Emergency Backup Power

In this mode, the other sources are not available so emergency backup power, supply power to emergency loads (emergency lights, emergency fans, and laptop charger).

Modeling and Simulation of H2V System

The simulation model of the system is developed in MATLAB/Simulink. All of the components shown in Fig. 10. Detailed description of each subsystem is reported in the following subsection. Figure 11, shows the mode III of proposed system.
Fig. 10

Simulation modeling of proposed system

Fig. 11

Simulink model of mode III

The model of PV Array consists of four temperature and solar insolation dependent PV panels of 250 Wp at 25 C and 1000 W/m2, which are installed on the rooftop and one more is at roof-rack of vehicle. A single-phase AC voltage source with 230 V of nominal voltage, 50 Hz of nominal frequency utilized to model the grid supply. The load of 1 kW is considered in the simulation. The details of different components of the H2V system for the Simulink model which is developed is reported in Table 1. On the basis of different operating modes and vehicle operation, three case studies are developed and c described and then, simulation results are reported.

Case Studies

  1. 1.


On weekdays there are 14 h to use the vehicle (Fig. 5). During winters, the system operates in mode I and mode IV since PVA does not produce any power or output power is to low so it can charge vehicle if it is available. During summer the system operates in mode III.
  1. 2.

    Weekends: Traveling

In this case, there are about 18 h to use the vehicle, even if it goes for a trip on weekends. The system operates in mode II, mode III and mode IV more effectively than case study 1.
  1. 3.

    Weekends: Holiday


In this case, EV is parked at home for complete 24 h. During a power outage, the system operates in mode III. In summer, there is appreciable solar charging of the vehicle than in winters.

Simulation Results

As long as there is no power outage, grid fulfills the power requirement of load and EV. Therefore, simulation results are obtained only for cases corresponding to power outages. The simulation results of mode III are obtained from the modified Simulink model of the system (Fig. 11). Solar insolation and temperature data of Aligarh is used to obtain the power output of PV Array. This data is for 13 h from summer, autumn, winter, and spring (February, June, September, and December). Figure 12 show the variation of solar insolation and temperature for four different months respectively.
Fig. 12

Variation of solar insolation of different months

In case-study I, there is no solar charging of EV and hence it does not deliver energy to the load. No PVA power plots are available. For case study II, the PVA power output for summer and winter is given in Fig. 13. To fulfill the load requirement of maximum 900 W, the remaining power is supplied by EV whenever PV Array power is less than 1 kW. For case study III, the output power of PVA for a period of 12 h is given in Fig. 13. So based on the four different modes of operation simulation result of each mode has been shown in Figs. 14 and 15. Figure 14a shows the mode I, grid provide power for home loads as well as for EV and emergency battery and (b) shows mode II, grid supply home loads and PV supply power for EV. Figure 15a shows mode III.
Fig. 13

Output power of PV Array for different month

Fig. 14

a mode I b mode II

Fig. 15

a Mode III b Mode IV

Hardware in the Loop Experimental Setup

Design of the Experiment

The experimental setup is implemented for the normal load of 325 W during daylight, 0.98 lagging power factor at Aligarh, India that it can reach up to 800 W during the night due to more lighting at night. Four 250 Wp solar PV panels, constitute a PVA and four 12 V lead-acid batteries have been used for ESU. Both PVA and ESU are connected to dc side of a dc-ac converter (commercial inverter). The output of AC terminal of the inverter is connected to the load. The inverter AC input terminal is connected to the utility supply. The complete experimental setup and distribution board of the vehicle is shown in Fig. 16.
Fig. 16

Experimental setup and distribution board

Experimental Setup Results

Figure 17 shows the 24 h collected data for the proposed system, based on the recorded data from 1 am to 9 am it works in the Mode I. After while at daylight load demand reduced and PV power became sufficient to feed home loads it shifted to the Mode III. At this time solar power is more than home demand so EV charged through PV source (H2V). After 4 pm EV supply power to the home (V2H) and it is still in Mode III. At 8 pm it shifted to Mode I again and finally at 23 pm it operated on Mode IV. The results show that proposed system is working properly.
Fig. 17

Experimental setup results that shows different modes of operation


This paper proposed the use of EV as a home load as well as power source for the home during power outages in addition to its use as a vehicle which makes it suitable for developing and underdeveloped countries. In addition to the utility grid, the vehicle was charged from solar PV panels because reliability of grid power is poor in such countries. The PV charging applied to reduce the home billing and prevent more stress on the grid. Three drive patterns of EV was developed based on EV presence at home on weekdays and weekends, and it was found that the drive pattern ‘weekend: holiday’ suits the most to the proposed H2V system. Depending on the availability of grid and solar power, the operation of the system was categorized in four modes, and it was seen that mode III fits ideally for developing and underdeveloped countries.

Hardware in the loop setup was experimentally validated by means of a prototype of the system. The obtained results are in accordance with the proposed strategy and the findings are successful for H2V based back up power supply for developing and underdeveloped countries. The controlling circuit is very cheap and easy to install on the pre-installed solar system and no need more change on the structure of home wiring.

Future work should further walk around climate prediction to optimize the EV solar charging, prediction of driver charging pattern by Artificial Neural Network to achieve optimized charging and discharging time, and more focus on effectiveness of DC charging to reduce the power losses.


  1. 1.
    Liu YJ, Chang TP, Chen HW, Chang TK, Lan PH (2014) Power quality measurements of low-voltage distribution system with smart electric vehicle charging infrastructures. In: Proceedings of international conference on harmonics and quality of power, ICHQP, pp 631–635Google Scholar
  2. 2.
    Martinez CM, Hu X, Cao D, Velenis E, Gao B, Wellers M (2017) Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective. IEEE Trans Veh Technol 66 (6):4534–4549CrossRefGoogle Scholar
  3. 3.
    Chen C, Duan S (2015) Microgrid economic operation considering plug-in hybrid electric vehicles integration. J Mod Power Syst Clean Energy 3(2):221–231CrossRefGoogle Scholar
  4. 4.
    International Energy Agency (2016) Global EV outlook 2016 electric vehicles initiative. IEA, p 51Google Scholar
  5. 5.
    Ahmad F, Alam MS, Shahidehpour M (2017) Optimal placement of electric, hybrid and plug-in hybrid electric vehicles (xEVs) in Indian power market. In: 2017 Saudi Arabia smart grid (SASG), pp 1–7Google Scholar
  6. 6.
    Shahidinejad S, Filizadeh S, Bibeau E (2012) Profile of charging load on the grid due to plug-in vehicles. IEEE Trans Smart Grid 3(1):135–141CrossRefGoogle Scholar
  7. 7.
    Gupta M, Rafat Y, Alam MS (2017) Well to wheel cum tailpipe emission analysis: ICE vs xEV. In: SAE international symposium on international automotive technology 2017Google Scholar
  8. 8.
    Liu C, Chau KT, Wu D, Gao S (2013) Opportunities and challenges of vehicle-to-home, vehicle-to-vehicle, and vehicle-to-grid technologies. Proc IEEE 101(11):2409–2427CrossRefGoogle Scholar
  9. 9.
    Ahmad F, Alam MS, Asaad M (2017) Developments in xEVs charging infrastructure and energy management system for smart microgrids including xEVs. Sustain Cities Soc 35:552– 564CrossRefGoogle Scholar
  10. 10.
    Ashtari A, Bibeau E, Shahidinejad S, Molinski T (2012) PEV Charging profile prediction and analysis based on vehicle usage data. IEEE Trans Smart Grid 3(1):341–350CrossRefGoogle Scholar
  11. 11.
    Rezvanizaniani SM, Liu Z, Chen Y, Lee J (2014) Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. J Power Source 256:110–124CrossRefGoogle Scholar
  12. 12.
    Chen L, Chung CY, Nie Y, Yu R (2013) Modeling and optimization of electric vehicle charging load in a parking lot. In: 2013 IEEE PES Asia-Pacific power and energy engineering conference (APPEEC), pp 1–5Google Scholar
  13. 13.
    Ahmad F, Alam MS (2017) Feasibility study, design and implementation of smart polygeneration microgrid at AMU. Sustain Cities Soc 35:309–322CrossRefGoogle Scholar
  14. 14.
    Khan S, Ahmad A, Ahmad F, Shafaati Shemami M, Saad Alam M, Khateeb S (2017) A comprehensive review on solar powered electric vehicle charging system. Smart Sci 6(1):1–26Google Scholar
  15. 15.
    Asaad M, Ahmad F, Saad Alam M, Rafat Y (2017) IoT enabled electric vehicle’s battery monitoring system. In: Proceedings of the 1st EAI international conference on smart grid assisted internet of things, no 8Google Scholar
  16. 16.
    Wu X, Hu X, Moura S, Yin X, Pickert V (2016) Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array. J Power Source 333:203–212CrossRefGoogle Scholar
  17. 17.
    Kong P-Y, Karagiannidis GK (2016) Charging schemes for plug-in hybrid electric vehicles in smart grid: a survey. IEEE Access 4(99):6846–6875CrossRefGoogle Scholar
  18. 18.
    Zhang T, Wang X, Chu CC, Gadh R (2016) User demand prediction and cloud-based smart mobile interface for electric vehicle charging. In: Asia-Pacific power and energy engineering conference APPEEC, pp 348–352Google Scholar
  19. 19.
    Nworgu OA, Chukwu UC, Okezie CG, Chukwu NB (2016) Economic prospects and market operations of V2G in electric distribution network. In: Proceedings of the IEEE power engineering society transmission and distribution conference, 2016, pp 1–5Google Scholar
  20. 20.
    Gong C et al. (2015) Study on the impacts and analysis of EV and PV integration into power systems. In: 2015 5th international conference on electric utility deregulation and restructuring and power technologies (DRPT), pp 2454–2458Google Scholar
  21. 21.
    Wu X, Hu X, Yin X, Moura SJ (2018) Stochastic optimal energy management of smart home with PEV energy storage. IEEE Trans Smart Grid 9(3):2065–2075CrossRefGoogle Scholar
  22. 22.
    Shemami MS, Alam MS, Asghar MSJ (2017) Load shedding mitigation through plug-in electric vehicle-to-home (V2H) system. In: 2017 IEEE transportation and electrification conference and expo, ITEC 2017, pp 799–804Google Scholar
  23. 23.
    Shafaati Shemami M, Saad Alam M, Asghar MSJ (2017) Fuzzy control assisted vehicle-to-home (V2H) energy management system. Smart Sci 477:1–15Google Scholar
  24. 24.
    Shin H, Baldick R (2017) Plug-in electric vehicle to home (V2H) operation under a grid outage. IEEE Trans Smart Grid 8(4):2032–2041CrossRefGoogle Scholar
  25. 25.
    Wu X, Hu X, Moura S, Yin X, Pickert V (2016) Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array. J Power Source 333:203–212CrossRefGoogle Scholar
  26. 26.
    Sun C, Sun F, Moura SJ (2015) Data enabled predictive energy management of a PV-battery smart home nanogrid. In: 2015 American control conference (ACC), pp 1023–1028Google Scholar
  27. 27.
    Goli P, Shireen W (2015) Plug in electric vehicles in smart grids. Springer, BerlinGoogle Scholar
  28. 28.
    Said D, Cherkaoui S, Khoukhi L (2015) Scheduling protocol with load managementfor EV charging. In: 2014 IEEE global communications conference GLOBECOM 2014, pp 362–367Google Scholar
  29. 29.
    The Cost of Solar Will Drop Another 25% by 2022 [Online]. Available: Accessed 22 Nov 2017
  30. 30.
    Jhunjhunwala A, Lolla A, Kaur P (2016) Solar-dc microgrid for indian homes: a transforming power scenario. IEEE Electrif Mag 4(2):10–19CrossRefGoogle Scholar
  31. 31.
    Hu X, Li SE, Yang Y (2016) Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles. IEEE Trans Transp Electrif 2(2):140–149CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringAligarh Muslim UniversityAligarhIndia

Personalised recommendations