In the system design of the proposed EVCMS framework, three crucial components are addressed: charging price, charging optimization for generating optimized plans, and system security. In the EVCMS framework, EV drivers set charging preferences and make reservations. The system dynamically generates charging solutions through optimization, factoring in user preferences, station availability, and peak OFF/ON times, allowing drivers to confirm or cancel their chosen plan.
3.2.1. Charging Pricing
The cost of a charging session is calculated based on the charging station and the pricing model selected. In general, the price per session for a charging station is the factor of the amount of energy in kilowatt-hours (kWh) delivered during the session multiplied by the cost per kWh. There are also some instances whereby additional fees may be charged on certain charging stations or even as time-based object prices, as well as pricing plans or discounts, due to availability, which may affect the amount of app credit to be expended per charging session. To derive the formula for the EV charging price for each session of the proposed system, the following parameters have been taken into consideration:
Cost of energy price: the price per unit of energy (kWh) used during the charging session.
Duration consumed: the total time in hours (h) that the EV was connected to the charging station and consuming energy.
Charging rate: the rate at which the EV battery is charged at the EV charging station. It is measured in kilowatts (kW) and can be considered as the power output of a charging station.
Power output: the maximum power output of the charging station (in kW) per day.
Charging efficiency: The charging station needs to convert electrical energy into battery energy for an EV to charge at the charging station. The measure with which this is carried out is the charging efficiency, and it is represented as a decimal value between 0 and 1, where 1 indicates that charging efficiency is 100%, considering all electrical energy supplied to the charging station is utilized to charge the EV batteries.
Time-of-use charges: the time when an EV is charged at the charging station. As at some times of day, electricity prices are high, represented as the Peak On Time, and time of day when electricity prices are low, represented as the Peak Off Time. Along with the time of day, the availability of the charging station is also considered, and the ToU factor is decided as shown in
Table 1.
Peak demand charges: If the EV is charged at a charging station when the electricity prices are high, then this charge will be taken into consideration. Peak On Times are defined in legislation as 8 a.m. to 11 a.m. and 4 p.m. to 10 p.m. on weekdays.
Availability in station: whether the charging station is available to use or not at the desired time.
Service Charge of Station: additional charges applied by a charging station for utilizing their service.
To derive the pricing model for EV charging, the following nomenclature is used as defined in the Abbreviations section.
With these parameters, the following formula was derived for the EV charging price for each session:
where
Charging Rate (in kW) = Maximum Power Output of the Charging Station (in kW) × Charging Efficiency (as a decimal)
Time-of-Use Charges = Availability × Peak Demand Charges
Substituting (2), (3), and (4) into (1), the following formula can be derived:
3.2.2. Charging Optimization
Generating optimized plans for EV users based on various parameters can be a complex task, but by leveraging cloud services and data analytics tools, one can achieve this efficiently and effectively [
27]. The proposed EVCMS framework optimizes the charging plans, by reducing the EV charging price parameter through efficient scheduling and pricing strategies. To generate optimized plans for EV users, parameters such as power requirement and the time requirement of the user are taken into consideration, along with the availability of the charging station and the Peak Off/On Time of the charging station.
The Algorithm 1 checks availability and assign time slot to EV users. This algorithm begins by initializing variables such as the start time (startTime), end time (endTime), and a flag (isTimeSlotOverlapping) to track the overlap status. It then iterates through existing bookings, comparing their time slots with the user-requested time. If there is an overlap with existing bookings, then the algorithm sets the flag to true and searches for an alternative time slot by increasing the start time of the requested booking by 15 min. This is continued in a loop unless a nonoverlapping slot is found. Once a suitable time is identified, the algorithm finalizes the new date and time (finalDateTime). If there is no initial overlap, the algorithm directly finalizes the date and time to the user’s requested time. The algorithm returns a value (isAvailable) indicating whether the requested time slot is available and the finalized date and time for the booking (finalDateTime). Thus, the algorithm efficiently manages booking conflicts, ensuring users are assigned available time slots or proposing suitable alternatives when needed.
The Algorithm 2 generate plans to optimize charging cost for an EV user. It begins by determining if the requested charging time falls within a peak period. The algorithm first determines the peak status of the final date and time. Based on availability and peak status, the algorithm then calculates cost multipliers. If isPeakOn is true and there is a time slot overlap, it assigns a cost multiplier of 1, indicating that the charging is not available in the Peak On Time period. Otherwise, if there is no time slot overlap, it sets the cost multiplier to 0.5, signifying availability in the charging station but in the Peak On Time period. On the other hand, if isPeakOn is false, the algorithm assigns a cost multiplier of 0.25 in the case of a time slot overlap, indicating that charging is not available in the Peak Off Time period.
Algorithm 1: Check Availability and Assign Time Slot |
![Wevj 15 00392 i001]() |
If there is no time slot overlap during the Peak Off period, the cost multiplier is set to 0, implying that charging is available at no additional cost. The algorithm then returns the computed Availability Cost Multiplier (ACM). The overall charging price is then computed, including the time-of-use charges, considering energy cost, duration, charging rate, and additional service charge.
Based on the availability of charging stations and Peak Off/On Time data analyzed by the cloud server, further plans will be generated. These plans might involve determining the most efficient time to charge based on the user’s power and time requirements and the Peak Off/On Times of the charging station. Based on the parameters taken into consideration, the following optimized plans can be generated to suit user requirements considering the charging station’s requirements:
Plan 1 (Duration): The user has entered the arrival time, duration, and power required. However, the power required is not sufficient to charge the EV as per the time requested. So, another plan is provided with updated charging power to meet the time constraints.
Plan 2 (Power): The user has entered the arrival time, duration, and power required. However, the required time is not sufficient to charge the EV as per the charging power requested. So, the time is updated in one plan to meet charging needs.
Plan 3 (Duration Eco): An enhancement is made to plan 1 by shifting the time slot to Peak Off Time period. In this plan, the system suggests the nearest possible Peak Off Time slot with respect to the required time duration. This plan minimizes the charging cost compared to the plan 1 charging cost.
Plan 4 (Power Eco): An enhancement is made to plan 3 by shifting the time slot to Peak Off period. In this plan, the system suggests the nearest possible Peak Off Time slot with respect to the power required. This plan minimizes the charging cost compared to plan 3’s charging cost.
Algorithm 2: Generate Plan |
![Wevj 15 00392 i002]() |
The first two plans are generated in both statuses (Peak On and Peak Off) by adjusting either power or duration to meet specific user requirements. If the charging time is during the Peak On period, the algorithm generates the first two plans in the Peak On Time period and also seeks an alternative slot in the Peak Off Time period and creates two more plans with adjusted power or duration in the Peak Off Time period. These plans are considered as Eco plans. Thus, the algorithm outputs a list of plans tailored to user preferences, availability constraints, and cost optimization.
Figure 3 presents the flowchart of the EVCMS optimization algorithm. This flowchart serves as a roadmap for the EVCMS framework, enabling it to optimize its operations and enhance service efficiency. The flowchart visually guides the process of resource management and overall optimization process.
The complexity of the optimization algorithm can be described using two components, namely space and time complexity. Time complexity indicates how the algorithm’s execution time grows relative to the input size. Lower time complexity signifies better efficiency. Similarly, space complexity reflects how much memory the algorithm requires as the input grows. Lower space complexity indicates more efficient memory usage. Considering that the EVs for charging will be limited each day, time complexity analysis is the major component in the complexity analysis of the proposed optimization technique. Let Tg be the time gap between each EV booking time, and let Nb be the number of bookings per day. To determine the available slot, we linearly scan through all bookings (Nb) and find the available slot. The time complexity of the step is O(Nb). If there is no available slot, then the algorithm seeks the next available slot in the increment of the time gap (Tg). If the time gap (Tg) is checked for, the next available constant is slot found (15 min). If the time gap (Tg) is set to 15 min, the system will check for the next available slot 15 min after the current slot becomes unavailable.
For any day, the max gap available is calculated by the total time per day in minutes/time gap = 24 × 60/15 = 96, which can be considered constant. Thus, the worst-case complexity of this step would be O(96 × Nb) = O(Nb). Using these notations, the time complexity of the algorithm is linear and can be expressed as O(Nb). The plan generation is a constant time operation based on the peak status and the availability it can generate, which is a maximum of four plans. So the space complexity is also constant and can be expressed as O(1). As the proposed algorithm has a lower time complexity when O(Nb) and space complexity when O(1), the algorithm is highly efficient and can process larger inputs quickly while using minimal memory.