Review of Electric Vehicle Technologies, Charging Methods, Standards and Optimization Techniques
- Charging standards as defined by the Society of the Automotive Engineers (SAE);
- EV charging systems such as on-board and off-board chargers, and
- Optimization techniques for sizing and placement of EV charging stations under different objectives and constraints.
2. Vehicle Technology
2.1. Hybrid Electric Vehicle
- Improved fuel efficiency and performance.
- Lower fuel consumption costs.
- Reduce CO2 emission.
- Recovery of some energy via regenerative braking.
- Use of an existing fuel station.
- The disadvantage is a higher initial cost due to the battery.
- Start and Low to Mid-range Speeds: During low to mid-range speeds or at the vehicle’s starting, the engine stops, and the vehicle is propelled by the motor alone.
- Driving Under Normal Conditions: The power split device sends some power to run the generator and the rest of the power to drive the wheels directly. If there is excessive power, then it’s used to charge the battery.
- Sudden Acceleration: Both the battery and engine provide power during sudden acceleration.
- Deceleration: The regenerative braking system converts the kinetic energy into electrical energy that is stored in the high-performance battery.
2.2. Electric Vehicle (EV)
- All times: Whenever the vehicle needs to move, the battery propels the vehicle.
- Deceleration or Braking: When the vehicle decelerates or brakes, the vehicle recaptures the kinetic energy into the battery using regenerative technology.
2.3. Fuel Cell Electric Vehicle (FCEV)
- Fuel Cell Electric Vehicle.
- Fuel Cell Hybrid Electric Vehicle (FCHEV).
3. EV Charging Methods
3.1. Battery Swap Station (BSS)
3.2. Wireless Power Transfer (WPT)
3.3. Conductive Charging (CC)
- Top-down Pantograph: The charging setup is mounted on the roof of the bus stop therefore it is commonly known as an off-board top-down pantograph. This method provides high power direct current which is already demonstrated in Singapore, Germany, and the U.S. .
- Bottom-up Pantograph: This type of charging method is suitable for those applications where the charging equipment is already installed in the bus. This is also known as an on-board bottom-up pantograph .
4. Review of EV Charging Configurations, and Standards
4.1. EV Charging Configurations
4.2. EV Charging Standards
5. Optimization Techniques
5.2. Total Loss
5.3. Maximize the Profit
6.1. Energy Source
- Diesel Generator.
- EV Fleet (for V2G purpose).
6.2. Optimization Method and Objectives
- Mixed-integer linear programming.
- Mixed-integer programming.
- Second-order conic programming (Convex Optimization).
- Markov chain Monte Carlo simulation.
- Particle swarm optimization and Voronoi diagram.
- Simulated annealing approach.
- Quadratic programming.
- Standard linear programming with the root-mean-square objective function.
6.3. Services and Test System
- Vehicle to Grid.
- Grid to Vehicle.
- Parking lot to grid.
- Parking lot to Vehicle.
6.4. Future Research Recommendations
- The research on the application of BESS and bi-directional power transfer capability of EVs in a distribution system can reduce the global warming issue more resourcefully by providing green electricity to homes and offices. Also, the intermittency of PV can be reduced by integrating optimally sized BESS . Also, the profit of the parking lot owner can be maximized by incorporating battery swap to provide added value to customers.
- The frequent charging/discharging can cause EV battery life degradation . Therefore, the use of BESS as an energy storage backup and subsequent sale of electricity to the building instead of discharging the EV battery repeatedly will ultimately increase the battery lifespan.
- The proposed PEB charge scheduling algorithms  can be applied to the charging scheduling of private EVs and Electric Ferries where the arrival and departure schedules are known. The battery capacity optimization for a given route can also be evaluated to minimize the vehicle cost.
- Research should be carried out on coordinated charging because uncoordinated charging of EVs can cause a peak load on a distribution system. EVs could be a great solution to settle these complications. In general, most vehicles are parked during peak load time. Therefore, using the stored electricity from vehicle (battery) to grid (V2G), electrical peak load would be reduced.
- Conventional PSO algorithm use for the optimal sizing has some problems such as searching the optimal value, the particles are trapped into local minima, and the number of iterations taken is increased [82,83]; therefore, the research could be carried out on the local trapping issue’s solution and computational time enhancement for example by hybridizing it with other heuristic technique can resolve these problems.
- The existing literature considered eco-charging systems (consisting of PV, ESS, and the electrical grid). However, mixing the other renewable DGs such as wind energy and Biomass energy can make the ecosystem more robust and sustainable and can conquer the intermittency issue caused by PV and wind.
- Research could be carried out on the charging and discharging model with the regenerative braking system of PEBs, which can lead to a more precise SOC estimation of PEBs.
- The existing literature considered the energy trading among the entities (PV, ESS, building, grid, and PEBs) in the ecosystem. However, generating power in multiple depots by using renewable energy resources and performing the energy trading between them can reduce the overloading of the grid.
7. Conclusions and Recommendation
Conflicts of Interest
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|BSS||Quick battery replaces (Fully charged)||More costly than ICE vehicle because of the monthly rent to BSS||||2014|
|BSS extend the battery life by slow charging||The huge investment required for both equipment and batteries||||2017|
|BSS help utilities in balancing the demand and load by using the V2G facilities||Need a large stock of expensive batteries||||2017|
|Easy to integrate with the locally generated RESs.||Many areas needed to accommodate the batteries||||2018|
|Different EVs have different battery standards.|
|WPT||EV recharge it safely and conveniently||Power transfer is generally weak||||2018|
|No need for any standard connector||The range of 20 to 100 cm for efficient power transmission|
|No need for any standard Socket||The transmitter and the EV should be real-time and communication latency.||||2018|
|Recharge when the vehicle is in motion.|
|CC||Provide multiple charging levels||Complex infrastructure||||2016|
|Provide high efficiency||Restriction to the electricity grid||||2017|
|Coordinated V2G facility||Fast charging cause voltage instability in the distribution system||||2014|
|Reduce the grid loss|
|maintain voltage level||Need a standard connector/charging level||||2018|
|prevent grid power overloading||Grid power overloading will cause due to uncoordinated charging||||2015|
|Active power support.||V2G operation reduces the lifetime of the battery.||||2013|
|Standards||Phase||Level/Mode||Voltage (V)||Current (A)||Source|
|Ref.||Energy Source||Optimization Method||Optimization Objective||Contribution||Service||Test System||Remarks|
|Mixed Integer Linear programming (MILP)||Enhance reliability through a probability model to quantify available energy in the PL||Outage management scheme||V2G||NHTS IEEE-34-node Test System||The NHTS data used is not practical EV data.|
Did not consider DRSs and EV constraints
CO2 emission generated due to the use of diesel generators
|Second-order conic programming (Convex Optimization)||Minimize total loss and maximize penetration|
|Solved mismatch issue between the production of DG unit and load consumption||G2V||Alibeykoy feeders|
Hamikoy feeders (Istanbul, Turkey)
|The investment and maintenance costs of ESS are ignored|
Power sold to the grid, instead of the contracted building
|Mixed Integer Linear programming (MILP)||Parking lot owner profit is maximized through a two-level problem model|
PLO profit maximization
DSO cost minimization
|Energy trading between upstream (Aggregator) and downstream (PL)||PL2G||IEEE-34-node Test System|
PL assumed 1000 parking spaces
|Used real PV and WT data, but the EV arrival and departure times are not based on real data|
Only focus on the operational framework
|Markov Chain Monte Carlo Simulation is used to generate A/D/S duration||Parking lot owner profit maximization||Annual profit maximization of PEV-PL by selling electricity to PEV||PL2EV||Implemented in an existing building in Toronto, Canada||EV arrival and departure times are not based on real data|
Only focused on the planning framework
|||Grid only||Particle Swarm Optimization (PSO) and Voronoi diagram||Minimize the annual cost of an entire PEV charging station for the PL owner||Sizing and sitting of Fast charging station||G2V||One Nissan Leaf is selected to represent the PEV population|
Implemented in an urban area in China
|EV arrival and departure times are not based on real data|
|Mixed Integer Linear Programming (MILP)||Minimize Station Energy Cost (SEC) and ESS storage cost||Sizing of ESS in a fast-charging station||G2V||UK daily traffic data||Only considered fixed electricity rate and not real-time electricity rate. The presence of ESS is thus not economical |
Potential constraints such as inverter, Grid, and vehicles are ignored
|Simulated Annealing Approach (SA)||Minimize aggregator operation cost through SA approach||The SA approach has a lower execution time than (GAMS) and GAMS_N||V2G||IEEE-33 bus Test system with 66 generators, 32 Loads & 1000 Grid-able vehicles||The total cost of network simulation is higher than another deterministic approach, e.g., GAMS (General Algebraic Modelling System) and GAMS_N|
|Mixed Integer Programming (MIP)||Minimize grid operation cost through proposed stochastic security constraints unit commitment model for PEV and Wind||Modeling of large-scale PEV integration as mobile distributed storage|
Modeling of load facilities and their impact on the power system.
|V2G||IEEE-6-Bus power system |
Modified IEEE-118-bus system
|Only used vehicle storage capability and not ESS as a storage device.|
Frequent charging and discharging degrade the battery lifespan
|||Grid only||Quadratic programming||Minimize the distribution power loss|
Maximize the main Grid load factor using the proposed coordinated charging
|Lower power loss|
Lower voltage deviation by leveling the peak power
|G2V||IEEE-34-node Test System||The proposed approach can prolong voltage control by PHEV reactive power control and Grid balancing|
|Mixed Integer Linear Programming (MILP)||Minimize economic cost related to energy exchange between grid and micro-grid||Allocation of the shiftable load during off-peak hours minimizes the overall cost||V2G||Household data from Spain||By introducing ESS, the critical, adjustable, and shiftable load can be managed efficiently and profit can be increased|
|Standard linear programming with the root-mean-square objective function||Minimize power imbalance in the grid through coordinated EVs charging and discharging||The optimization problem can compute quickly and efficiently |
Optimization will repeatedly calculate to revise V2G/G2V output of vehicle to deal with the error in prediction
|G2V/V2G||Wind data collected from Victoria state, Australia||The power imbalance issue can be resolved by using ESS more effectively |
A diesel generator is used to provide ancillary service, which contributes to CO2 emissions.
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Arif, S.M.; Lie, T.T.; Seet, B.C.; Ayyadi, S.; Jensen, K. Review of Electric Vehicle Technologies, Charging Methods, Standards and Optimization Techniques. Electronics 2021, 10, 1910. https://doi.org/10.3390/electronics10161910
Arif SM, Lie TT, Seet BC, Ayyadi S, Jensen K. Review of Electric Vehicle Technologies, Charging Methods, Standards and Optimization Techniques. Electronics. 2021; 10(16):1910. https://doi.org/10.3390/electronics10161910Chicago/Turabian Style
Arif, Syed Muhammad, Tek Tjing Lie, Boon Chong Seet, Soumia Ayyadi, and Kristian Jensen. 2021. "Review of Electric Vehicle Technologies, Charging Methods, Standards and Optimization Techniques" Electronics 10, no. 16: 1910. https://doi.org/10.3390/electronics10161910