Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation
Abstract
:1. Introduction
2. General Aspects of Electric Vehicle
- Improvement of local air quality due to less emitting tailpipe exhaust gases, zero in BEVs
- Improvement of energy efficiency in transport sector: the EM is more than twice as efficient as the Internal Combustion Engine (ICE) [45]
- Less maintenance related to engine or mechanics and associated costs, especially in BEVs
- Less noise pollution since EM is much quieter than ICE
- Reduction of the external energy dependence of some countries by reducing the oil needed for fuels
- Greater flexibility for the joint development of other technologies such as the integration of Renewable Energies Sources (RES) [11]
- Improvement of electrical network quality under correct coordination [12]
- Low autonomy in electric mode compared to ICEVs
- Few charging stations and long duration of the charge
- High cost and limited lifetime of batteries
- The almost zero noise produced during the operation of the EM may not prevent accidents, for example, by not warning the presence of vehicles to pedestrians
- BEV (Figure 2a): The BEV is the simplest in terms of technology, since it is a purely electric vehicle. It consists of batteries that are charged from the network through an on-board or off-board charger, and a DC/AC converter that feeds a reversible electric machine with the energy coming from the batteries. The bidirectionality of the energy flow allows the reversible machine to operate in generator mode, thus enabling the regenerative braking. The batteries operate in a single mode, “Charge Depleting Mode” [45], discharging during vehicle operation and recharging from grid or through regenerative braking. Constructively, two types of BEVs can be found, according to whether a single electric machine is connected to the wheels through a differential, or an electrical machine is installed in each wheel, known as an in-wheel machine.
- REEV/Series PHEV (Figure 2b): The REEV/Series PHEV comprises mechanical and electrical energy sources, while traction is always electrical. In addition, it includes an ICE that feeds the battery in moments of depth of discharge. Thus, the batteries work in two modes: Charge Depleting Mode, when the small ICE is disconnected; and Charge Sustaining Mode, when the ICE is working, keeping the battery charge at a specified minimum level [45]. The main difference between REEV and series PHEV is the size of the ICE, which is considerably higher in the PHEV. This allows an optimal operation of the REEV motor at the point of maximum efficiency, with less fuel consumption.
- Parallel PHEV (Figure 2c): A parallel PHEV vehicle also has two types of energy sources, but traction can be performed electrically or mechanically as required.
- About 40 km: UK is the leading country in this category.
- From 50 to 60 km: Most countries are in this category: Germany France, Italy, etc.
- More than 70–80 km: This range includes countries such as Poland and Spain.
3. Impact on the Grid and Associated Charging Methodologies
3.1. Unidirectional
3.1.1. Uncontrolled
3.1.2. Controlled (Smart Charging)
Decentralised
Centralised
3.2. Bidirectional
3.2.1. Vehicle-To-Grid (V2G)
3.2.2. Vehicle-To-Building (V2B)
3.2.3. Vehicle-To-Home (V2H)
4. Impact on Batteries and Associated Recharging Methodologies
4.1. In-Battery Phenomena Modelling
4.2. V2X Services Provision Consequences in Battery Degradation
- LFP and NCA cell degradation rates are similar for frequency regulation.
- NCA shows better performance in frequency regulation.
- LFP shows better performance in peak shaving, less calendar aging and higher round-trip Efficiency (discharge energy/charge energy ratio).
- Cell resistance evolution is lineal in NCA, but parabolic in LFP.
4.3. Battery Degradation Considering Strategies
5. Research Gap and Further Research Proposal
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
BEV | Battery Electric Vehicle |
CC-CV | Constant Current and Constant Voltage |
CO2e | Carbon Dioxide equivalent |
DER | Distributed Energy Resources |
DoD | Depth of Discharge |
DR | Demand Response |
DSM | Demand Side Management |
DSO | Distribution System Operator |
EM | Electric Motor |
EoL | End of Life |
EV | Electric Vehicle |
G2V | Grid-to-Vehicle |
GA | Genetic Algorithm |
GHG | Greenhouse Gases |
HEMS | Home Energy Management System |
HEV | Hybrid Electric Vehicle |
ICE | Internal Combustion Engine |
ICEV | Internal Combustion Engine Vehicle |
ISO | Independent System Operator |
MGAU | Micro Grid Aggregation Unit |
MPP | Maximum Power Point |
PHEV | Plug in Hybrid Electric Vehicle |
pKm | Passenger and kilometre |
PSO | Particle Swarm Optimisation |
REEV | Range Extended Electric Vehicle |
RES | Renewable Energies Sources |
SOC | State of Charge |
SOH | State of Health |
TSO | Transport System Operator |
V2B | Vehicle-to-Building |
V2G | Vehicle-to-Grid |
V2H | Vehicle-to-Home |
V2V | Vehicle-to-Vehicle |
VPP | Virtual Power Plant |
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Country | Emissions Per Energy Unit (g CO2/kW h) | Emissions Per km (g/km) | Price Per Energy Unit (€/kW h) | Price Per km (€/km) |
---|---|---|---|---|
Belgium | 199 | 23.88 | 0.2173 | 0.0299 |
Canada | 158 | 18.96 | 0.08 | 0.0110 |
China | 711 | 85.32 | 0.081 | 0.0111 |
Czech Republic | 516 | 61.92 | 0.1681 | 0.0231 |
Denmark | 300 | 36 | 0.3 | 0.0412 |
Estonia | 1016 | 121.92 | 0.1351 | 0.0185 |
Finland | 175 | 21 | 0.1578 | 0.0217 |
France | 64 | 7.68 | 0.1524 | 0.0209 |
Germany | 486 | 58.32 | 0.3 | 0.0412 |
Greece | 649 | 77.88 | 0.1563 | 0.0215 |
Hungary | 293 | 35.16 | 0.1397 | 0.0192 |
Ireland | 435 | 52.2 | 0.2295 | 0.0315 |
Italy | 343 | 41.16 | 0.2292 | 0.0315 |
Japan | 572 | 68.64 | 0.21 | 0.0288 |
Korea | 536 | 64.32 | 0.087 | 0.0119 |
Lithuania | 204 | 24.48 | 0.137 | 0.0188 |
Luxembourg | 306 | 36.72 | 0.1665 | 0.0229 |
Netherlands | 452 | 54.24 | 0.1898 | 0.0261 |
Norway | 8 | 0.96 | 0.1909 | 0.0262 |
Poland | 769 | 92.28 | 0.148 | 0.0203 |
Portugal | 281 | 33.72 | 0.2081 | 0.0286 |
Russia | 439 | 52.68 | 0.027 | 0.0037 |
Slovak Republic | 176 | 21.12 | 0.1698 | 0.0233 |
Slovenia | 319 | 38.28 | 0.161 | 0.0221 |
Spain | 247 | 29.64 | 0.2228 | 0.0306 |
Sweden | 13 | 1.56 | 0.2101 | 0.0288 |
Turkey | 442 | 53.04 | 0.1495 | 0.0205 |
UK | 459 | 55.08 | 0.1741 | 0.0239 |
USA | 489 | 58.68 | 0.1 | 0.0137 |
Reference | Technology | Impact | Batteries | Charging | Others | |||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Eco | Env | Grid | ||||||||||||||||||||||||
H | PT | B | C | LC | LP | Loss | V | PU | H | Eq | Sta | Typ | Mod | Eff | Std | Top | Met | V2G | RES | DG | S G | Com | Proj | |||
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OS | - | - | - | - | - | - | - | - | - | - | - | - | - |
Voltage [V] | Max Current [A] | Power [kW] | Time of Charging | Installation Cost [$] | Recommended Location | ||
---|---|---|---|---|---|---|---|
AC | SAE Standard | ||||||
Level 1 | 120/230 (US/UE) | 12/16 (US) | 1.4/1.9 1 ph | PHEV: 7 h (SOC 0–100%) BEV: 17 h (SOC 20–100%) | 500–800 | Domestic | |
Level 2 | 240 | Up to 80 | Up to 19.2, 1 ph | EV: 3 h (SOC 0–100%) (On-board charger, 3.3 kW) EV: 1.5 h (SOC 0–100%) (On-board charger, 7 kW) EV: 20 min (SOC 0–100%) (In case of 20 kW on-board charger) | 2000–8000 | Parking or public streets, Public places | |
Level 3 | From 20 (1 ph/3 ph) | 30,000–160,000 | |||||
IEC Standard | |||||||
Mode 1 | 230/450 | 16 | 3.7/11 (1 ph/3 ph) | Domestic | |||
Mode 2 | 230/690 | 32 | 3.7/22 (1 ph/3 ph) | Car rental Company, Fleet of Company cars, Service Stations | |||
DC | SAE standard | ||||||
Level 1 | 200–450 | 80 | Up to 20 | PHEV: 22 min (SOC 0–80%) BEV: 1.2 h (SOC 20–100%) (In case of 20 kW off-board charger) | 8500–50,000 | Parking or public streets, Public places | |
Level 2 | 200–450 | 200 | Up to 90 | PHEV: 10 min (SOC 0–80%) BEV: 10 min (SOC 0–80%) (Off-board charger, 45 kW) BEV: 22 min (SOC 20–80%) (Off-board charger, 45 kW) | Car rental Company, Fleet of Company cars, Service Stations | ||
Level 3 | 200–600 | 400 | Up to 240 | BEV (only): <10 min (SOC 0–80%) (45 kW off-board charger) | Service Stations | ||
IEC Standard | |||||||
Mode 3 | 63 | 43.5 | Service Stations | ||||
Mode 4 | 400 | From 50 |
Advantages | Drawbacks | |
---|---|---|
Decentralised | Scalable Greater fault tolerance Less communications needed, generally based on local measurements Greater data privacy Greater controllability by the user. Greater acceptance by the users | Unexpected results Limited provision of ancillary services User behaviour prediction necessity Possibility of avalanche reactions |
Centralised | Known architecture Better use of network capacity Better provision of ancillary services Hierarchical structure Possibility of business models | Communication layer needed Central controller (aggregator) needed Limitation on the number of vehicles per controller High processing capacity required Possible data privacy violations |
Interest | Objective | Considerations | |||||
Aggregator | [57,58,59] | OptPrice | [52,57,58,59,60,61,62,63,64,65] | Grid | Current and Voltage limits | [60,65,66,67] | |
DSO | [60,61,66,68,69,70] | Load Flattening | Valley filling | [57,58,59,61,66,68,70] | Transformer limits | [52,66] | |
Several stakeholders | [64,65] | Peak Saving | [64] | Battery | SOC | [52,57,59,60,61,65,67,68,71] | |
Load Shifting | [60,70] | Reaching desired SOC | [52,57,61,66] | ||||
OptQual | Frequency regulation | [70] | Market | Day ahead Prices | [57,58,59,68,70] | ||
Voltage regulation | [52,65,69] | Real Time prices | [67] | ||||
Load Factor | [61] | Population | Homogeneous | [57,59,61,65,67,68,69,70] | |||
Active power | [52] | Heterogeneous | [52,58,60,64,66] | ||||
Method | Software | Solver | Validation | ||||
Convex program | [52,61,68] | MATLAB | [60,68,69,70] | GAMS | [52] | Simulation | [52,57,58,59,61,66,70] |
Linear programming | [52,60,70] | PowerACE | [64] | CPLEX | [52] | Trusted/Applied Simulation | [60,64,65,66,68,69] |
Nash Certainly Equivalence (NCE) | [57,58,59] | Power Factory | [60] | CONOPT | [52] | ||
shrunken-primal dual subgradient (SPDS) | [66] | ||||||
Game Theory | [65] |
Interest | Objective | Considerations | |||||
Aggregator | [78,79,80,81,82] | OptPrice | [52,53,78,80,82,83,84,85,86,87,88,89,90,91,92] | Grid | Current and Voltage limits | [53,83,86,88,90,92,93] | |
DSO | [84,92,93,94,95,96] | OptLosses | [53,84,93] | Transformer limits | [52,87,90,93] | ||
Several stakeholders | [53,62,83,87,88,97] | Load Flattening | Valley filling | [83,84,95] | Battery | SOC | [52,80,81,82,84,88,91,94,96,97] |
Load Shifting | [78,88,89,93,96] | Reaching desired SOC | [78,79,86,91,92,95,97] | ||||
Peak Saving | [62] | Battery constraints | [83] | ||||
Battery degradation | [78] | ||||||
OptQual | Market | Day ahead Prices | [85,86,89,92] | ||||
Voltage regulation | [52] | Real Time prices | [81,89] | ||||
Load Factor | [96] | Capacity prices | [80,88,93,96,98] | ||||
Active power | [52,94] | Population | Homogeneous | [81,90,91,95,96] | |||
Heterogeneous | [52,53,83,84,86,87,88,92,93,97] | ||||||
Method | Software | Solver/Tools | Validation | ||||
Convex program | [52,86] | MATLAB | [53,78,80,86,88,89,92,93] | GAMS | [52] | Simulation | [52,62,78,79,80,81,82,85,87,89,90,91,93,95,96,97,98] |
Nash Certainly Equivalence (NCE) | [62] | CPLEX | [52,78,80,83] | Trusted/Applied Simulation | [53,84,88,92,94] | ||
Linear programming | [52,85] | CVX | [86,89] | ||||
Iterative Quadratic programming | [83,92,95] | NSGA-II | [94] | ||||
Backward-forward method | [84] | CONOPT | [52] | ||||
Flexibility Envelope | [91] | ||||||
Maximum Sensitivities Optimisation | [53] | ||||||
Genetic Algorithm (GA) | [84,87,94] | ||||||
Particle Swarm Optimisation (PSO) | [84,87,90] | ||||||
Differential Evolution (DE) | [87] | ||||||
Ageist Spider Monkey Optimisation (ASMO) | [87] | ||||||
Dynamic Programming | [82] | ||||||
Game theory | [62] |
Interest | Objective | Considerations | |||||
OptPrice | [100,101,102] | Grid | Current and Voltage limits | [72,100,103] | |||
DSO | [51,72,99,102,103,104,105] | OptLosses | [72,104] | Transformer limits | [100,101] | ||
Several stakeholders | [100,101,106] | Load Flattening | Valley filling | [99,102] | Battery | SOC | [99,100,101,103,106] |
Load Shifting | [99,103,104] | Reaching desired SOC | [99,100,104,106] | ||||
OptQual | Frequency Regulation | [105] | Market | Day ahead Prices | [102] | ||
Voltage regulation | [103,105] | Real Time prices | [101,102] | ||||
Load Factor | [99,104] | Capacity prices | [101,106] | ||||
Active power | [105] | Population | Homogeneous | [100,103] | |||
Reactive Power | [105] | Heterogeneous | [101,104,106] | ||||
Method | Software | Solver/Tools | Validation | ||||
Convex program | [104,105] | MATLAB | [99,100,102,104,106] | GAMS | [106] | Simulation | [99,102,104] |
Linear programming | [100] | Power Factory | [101] | CPLEX | [106] | Trusted/Applied Simulation | [100,101,103,104,105] |
Iterative Quadratic programming | [99,104] | CVX | [103,104] | ||||
Dynamic Programming | [51] | fmincon | [100] | ||||
Artificial Immune System | [72] | MATPOWER | [102] |
Interest | Objective | Considerations | |||||
OptPrice | [63,107,108] | Grid | Current and Voltage limits | [63,107] | |||
DSO | [109] | OptLosses | [63] | Transformer limits | [63] | ||
Several stakeholders | [63,107,108] | Load Flattening | Valley filling | Battery | SOC | [107] | |
Load Shifting | [63] | Market | Day ahead Prices | [63,108] | |||
OptQual | Active power | [107] | Real Time prices | [107] | |||
Reactive Power | [107] | Population | Homogeneous | [63,107,108,109] | |||
Method | Software | Solver/Tools | Validation | ||||
MATLAB | [107] | GAMS | [107] | Simulation | [63,108,109] | ||
Linear programming | [108,109] | PSS/E | [108] | CPLEX | [63] | Trusted/Applied Simulation | [107] |
JAVA | [58,66] | KNITRO | [107] | ||||
Python | [108] | ||||||
LINGO | [108] |
Approach | Advantages | Drawbacks |
---|---|---|
V2G | Operation at large scale Ancillary services supply Electricity market participation Large scale RES Integration New business model opportunity | Complex operation Complex prediction of EV demand Large communication infrastructure required User preferences to be considered Lack of regulatory framework Battery degradation Standards needed to be developed |
V2B | DER improvement DS improvement Electricity bill lowering capability Backup power Easy EV demand prediction Low investment | Medium difficulty operation Poor market integration User preferences to be considered Battery degradation |
V2H | DS improvement Backup power Electricity bill lowering capability Easy implementation Very low investment Low communication infrastructure required Local RES integration Isolated houses energy provision | Only compatible with single family home Large scale opportunity loss Battery degradation |
Simulation Concept | Degradation Driver | Degradation Mechanism | Result | ||
---|---|---|---|---|---|
Cathode | Anode | Power Fade | Capacity Fade | ||
Calendar aging (~t2) | Surface Phase Change | Temperature | X | ||
Precipitation of Phases | Temperature, SOC | X | |||
Loss of Active Material | Temperature, SOC | X | |||
Dissolution of Species | Temperature, SOC | X | |||
Binder Decomposition | SOC | Temperature, SOC | X (Cathode) | X (Anode) | |
SEI Growth | Temperature, SOC | X | X | ||
SEI Dissolution | Temperature | X | |||
Crystal Disorder | SOC | X | |||
Current Collector Corrosion | SOC | X | |||
Cycling aging (Ah) | Lithium Plating | Temperature,C-Rate | X | X | |
Intercalation Gradients | Temperature,C-Rate | X | |||
Mechanical failure | DOD | C-Rate, SOC, DOD | X | X |
Approach | L1 | L2 | |
---|---|---|---|
G2V | Uncontrolled charging | 31.41% | |
V2G | Peak shaving | 36% | 45% |
Frequency Regulation | 35.03% | 42.56% | |
Net load shaping | 32.59% | 34.01% |
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Saldaña, G.; San Martin, J.I.; Zamora, I.; Asensio, F.J.; Oñederra, O. Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation. Energies 2019, 12, 2443. https://doi.org/10.3390/en12122443
Saldaña G, San Martin JI, Zamora I, Asensio FJ, Oñederra O. Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation. Energies. 2019; 12(12):2443. https://doi.org/10.3390/en12122443
Chicago/Turabian StyleSaldaña, Gaizka, Jose Ignacio San Martin, Inmaculada Zamora, Francisco Javier Asensio, and Oier Oñederra. 2019. "Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation" Energies 12, no. 12: 2443. https://doi.org/10.3390/en12122443