# Compact Interoperable DC Charging Station Enabling Smart Charging Features for e-Bike Charging Park

Shahid Jaman, Iftadul Islam Sakib, Thomas Geury, Omar Hegazy

► To cite this version:

Shahid Jaman, Iftadul Islam Sakib, Thomas Geury, Omar Hegazy. Compact Interoperable DC Charging Station Enabling Smart Charging Features for e-Bike Charging Park. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0111. hal-05095943

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# Compact Interoperable DC Charging Station Enabling Smart Charging Features for e-Bike Charging Park

Shahid Jaman<sup>1,2</sup>, Md Iftadul Islam Sakib<sup>1,2</sup>, Thomas Geury<sup>1,2</sup>, Omar Hegazy<sup>1,2,*</sup>

<sup>1</sup> MOBI-EPOWERS RESEARCH GROUP, ETEC DEPARTMENT, VRIJE UNIVERSETEIT BRUSSEL (VUB)

Pleinlaan 2, 1050 Elsene, Brussels, Belgium

<sup>2</sup> FLANDERS MAKE

Gaston Geenslaan 8, 3001 Heverlee, Belgium

E-Mail: omar.hegazy@vub.be

## Acknowledgements

<figure><img src="image_2.png" /></figure>

This work is a part of the eCharge4Drivers project. This project has received funding from the European Union Horizon 2020 research and innovation program under a grant agreement (No 875131). The authors also acknowledge Flanders Make for the support to our research group.

## Keywords

«Battery Charger», «E-mobility», «Charging Scheduling», «Interoperability»

## Abstract

This paper proposes a 1.5 kW rated compact, interoperable DC charger system with smart charging strategies for e-Bike charging parks. The necessary data monitoring unit, open charge point protocol (OCPP 1.6j) hardware unit, EnergyBus protocol compatible charging ports, and power protection devices are incorporated into the proposed system.

## Introduction

The global transportation landscape is undergoing a transformative shift with the increasing adoption of electric vehicles (EVs), prominently including electric scooters. The global electric bike (e-Bike) market was valued at USD 17.73 billion in 2023 and is forecasted to grow to USD 19.43 billion in 2024, reaching USD 50.15 billion by 2032, with a 12.6% CAGR. Asia Pacific held an 83.08% market share in 2023 [1]. The light electric vehicles, favored for their compactness, ease of use, and environmental advantages, are increasingly popular in urban areas. Unlike traditional gasoline light electric vehicles (E-BIKEs), which emit over 350 g of

CO2 per km, new E-BIKEs emit only about 105 g per km [2]. However, their widespread adoption depends on efficient charging infrastructure and strategies. The smart charging strategies are beneficial in terms of efficiency and reliability for long-term usage [3]. It is investigated for low voltage batteries that the fast charging (≥ 2C charging rate) enhanced efficiency by more than 8% via utilizing smart charging technique [4]. Generally, the e-Bike is fitted with a 2 kWh (58 V, 33.5 Ah) Li-ion battery and the recommended charging power is 0.5 C. The original equipment manufacturers (OEMs) suggested charging rates based on a continuous charging strategy. The smart charging strategy not only reduces the charging time but also improves battery health by reducing degradation. Thus, there is a scope to improve the charging strategy for e-Bike applications. Moreover, the current state of e-Bike chargers presents several challenges. Most existing chargers are either bulky or lack of interoperability, the dc-charging park scarcity, lack of data collection and monitoring unit, and the communication protocol for smart charging is not incorporated in the charging systems for different e-Bike applications. The e-Bike OEMs do not prefer onboard chargers (OBCs) due to thermal heat dissipation, bulky packaging, uncontrolled slower charging, lack of electrical isolation, and leakage detection [5]. Moreover, 2-wheeler (2W) and 3-wheeler (3W) e-Bike OEMs have proprietary charging hardware, connectors, and related communication protocols which is not standardized. Thus, a public DC charging station for one OEM may not be compatible with other OEM e-Bikes due to standard connector and communication protocols. This is the major disadvantage of the charging infrastructure for the 2W or 3W e-Bikes. For an interoperable DC charging infrastructure for 2/3W e-Bikes is required 3 key elements: (a) standard charging
plug and socket; (b) standard communication protocol between EV and electric vehicle supply equipment (EVSE); (c) standard communication protocol between EVSE and charge point operator (CPO). To address these challenges, future research must focus on developing compact interoperable DC charger systems equipped with smart charging strategies. Such systems should prioritize compatibility across different e-Bike models, ensuring that users can access charging facilities with ease. The scope of innovations in design, incorporating lightweight materials and modular components, can further improve the portability and scalability of charging infrastructure.

In this paper, a novel compact interoperable DC charger system is designed specifically for e-Bike charging parks. The system incorporates IoT technologies for smart charging and data collection to optimize energy usage and enhance the user experience. The system also utilized the open charge point protocol (OCPP) 1.6j communication protocol to communicate with the CPO. The EnergyBus protocol-compatible universal charging plug and socket are also integrated into the proposed DC charging container.

for electric scooter is shown in Fig. 1. The DC charger EVSE is the significant component of the proposed charging station. The EVSE enabled smart charging features for e-Bike users. The EVSE utilized the single-phase AC supply (230 Vrms, 50 Hz) as an input and generate DC output (1.5 kW max, 60 V) which fed directly to the e-Bike battery. The e-Bike used energybus compatible connector (IEC 62196-4) to connect with the EVSE and communicate using ISO 15118 protocol. The E-BIKE users are allowed to select the smart charging option through the mobile app service provided by CPO. To enable the smart charging feature, e-Bike users provide some information such as arrival time, departure time, initial SoC, E-BIKE battery data etc. using HTTP protocol. This information is used as input to the smart charging scheduling computation. Smart charging scheduling is an optimization-based strategy through which the CPO can provide the optimized charging profile (kW) to ensure not only the optimized cost, but also to charge faster than the equal energy distribution strategy. The CPO performs the offline optimization after receiving the necessary input from E-BIKE user, local power distribution operator (local demand and dynamic tariff profile) within 2-5 sec and generates the

<figure><img src="image_3.png" /><figcaption>Fig. 1: Block diagram of electric scooter park concept including interoperable DC charging system with smart charging facility.</figcaption></figure>

## Overall Concept of Proposed DC Charging Station

The overall concept diagram of proposed DC charging station including smart charging feature
<figure><img src="image_4.png" /><figcaption>Fig. 2: Internal block diagram of internal DC charger unit with PE converter, and necessary embedded hardware components.</figcaption></figure>

optimized charging profile (hh:mm, kW). The EVSE requests the charging profile for CPO server using OCPP 1.6j communication protocol to and send to the low-e-Bikeel controller to follow the power profile as reference. Thus, the smart charging scheduler generates a charging profile for each E-BIKE who booked the DC charger for charging the battery.

## Smart Charging Scheduling Model

The detailed explanation of mathematical formulation for optimization problem is out of scope for this paper. Mathematically, the optimization problem is formulated from Eq. (1) – Eq. (5) as follows:

$$ \text{Minimize} \left( \sum_{i \in N} \left( \beta_0 y_i + \frac{\beta_1}{2} y_i^2 \right) - \left( \beta_0 P_i^b + \frac{\beta_1}{2} (P_i^b)^2 \right) \right) \quad (1) $$

$$ y_i = P_i^b + \sum_{m \in M} p_{mi} S_{mi}, \forall i \in N \quad (2) $$

$$ 0 \le SoC_m^{ini} + \sum_{i \in N} dp_{mi}S_{mi} \le SoC_m^{max}, \forall i \in N, \forall m \in M \quad (3) $$

$$ SoC_m^{ini} + \sum_{i \in N} ip_{mi}S_{mi} \ge \alpha_m SoC_m^{max}, \forall i \in N, \forall m \in M \quad (4) $$

$$ 0 \le p_{mi} \le P_{max}^{ch}, \forall i \in N, \forall m \in M \quad (5) $$

In the optimization problem which is adapted from [6], the objective function Eq. (1) is convex,

and all the constraint functions are linear. Therefore, the optimization problem (5) is a convex optimization problem, which can be solved efficiently with the interior point methods [7]. Here, the number of intervals is denoted as “N” which is divided into 24 h and each interval duration is i = 1h. Moreover, “M” represents the total number of E-BIKE which is booked for charging. Each E-BIKE iterated as “m” during calculation. The total power demand of the site including E-BIKE charging load is represented as “y” which represents the base load (Pbi). The charging-interval matrix (Smi) is generated from E-BIKE user input to track the expected charging duration. The initial and maximum SoC of each E-BIKE is denoted as SoCini and SoCmax which considered and kWh unit. Similarly, the charging rate is denoted as pmi which can go up to maximum charging rate (Pchmax) during optimized charging profile generation.

## Hardware System Layout

The proposed DC charger system with smart charging feature and standard connector ports are shown in Fig. 2, which comprises with different significant components such as PE converter, power protection unit, embedded hardware etc. The components details are briefly discussed in the following subsections:

### PE Converter

The detail design and performance assessment of PE converter is out of scope for this paper. **Error! Reference source not found.** on the next page shows the bridgeless totem-pole bi-directional PFC unit with Infineon 650 V CoolSiC™ silicon carbide MOSFET.
<figure><img src="image_5.png" /><figcaption>Fig. 3: Totem-pole based AC/DC converter unit.</figcaption></figure>

The board is 280 mm long, with a width of 127 mm and a height of 64 mm, for a power density of 11 W/in3. The AC/DC converter unit specification is listed in Table I below:

<table><caption>**Table I : Totem-pole-based PFC specification**</caption><thead><tr><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Rated Power, P<sub>rat</sub></td><td>1500 W</td></tr><tr><td>DC Output Voltage</td><td>60 V</td></tr><tr><td>DC Current Range</td><td>0-25 A</td></tr><tr><td>Rated AC Input Voltage, frequency</td><td>230 V, 50 Hz</td></tr><tr><td>Max. AC Input Current</td><td>9 A</td></tr><tr><td>Operating Switching frequency Range</td><td>10 – 60 kHz</td></tr></tbody></table>

## Embedded System Components

The embedded system unit comprises a processor unit, RFID sensor, LCD display and communication interface circuit. There are different processor units that can be used for this application such Raspberry Pi, Arduino, ESP 32 etc. In this setup, ESP32-DEVKITC VIE module is used to utilize integrated WiFi module and faster response (2.4 GHz). The RC522 kit is used as RFID sensor and I2C TWI serial 2004 20X4 LCD display is also used to communicate with processor unit and to display the necessary instruction sets for charging.

The PFC converter communication is compatible for 5V TTL logic through JST SPHD-002T-P connector. Thus, a JST SPHD to RS232 DB9 port converter circuit as shown in Fig. 4 is used to interface with Rx/Tx port of ESP 32 processor. The interface communication converter is required 5 Vdc input and 4800 bit/s baud rate to communicate with the PFC unit.

<figure><img src="image_6.png" /><figcaption>Fig. 4: JST-SPHD to RS232 port Converter unit to communicate with the PE converter.</figcaption></figure>

## OCPP 1.6j Protocol for Smart Charging

The open charge point protocol (OCPP1.6j) version of the OCPP standard is widely used for EVSE and CPO communication [8]. OCPP1.6j provides basic functionalities such as starting and stopping a charge, retrieving charging station information and updating firmware. This version supports smart charging for load balancing and use of charging profile [9]. Several steps take place when a charging session occurs with smart charging enabled. Once a charging session is initiated, either through a *StartTransaction* or *remoteStart*, the Central System sends charging profiles using a *SetChargingProfile req* [10]. This message request is one of the more complex OCPP message types and has many configuration options. This message makes energy management possible since it contains the flexibility to build complex logic. On the other hand, this can lead to complex problems and errors if used wrong. The *SetChargingProfile req* includes an object *csChargingProfile*. This object specifies core parameters such as the power or current limits, timeframe, and more as shown in Fig. 5. or *current* (maximum current allowed per phase in Amperes).

<figure><img src="image_7.png" /><figcaption>Fig. 5: OCPP 1.6j message structure (*SetChargingProfile req*) to enable the smart charging for E-BIKE</figcaption></figure>
For example, the units of the limit can be expressed in power (maximum power allowed in Watts). There are certain parameters, such as *numberPhases*, *Duration*, can also heavily influence how a charge point will react to a command. The charge point will respond to these commands with a confirmation, *setChargingProfile.conf*, which includes a response defined for *ChargeProfileStatus* as accepted, rejected, not supported. If the charger responded with rejected or not supported, there might be needed to verify the format of the *setChargingProfile* or verify the format with the hardware manufacturer. Most chargers have limitations and expectations which is the reason for those response. The OCPP message request and response regarding smart charging are depicted in Fig. 6 below:

<figure><img src="image_8.png" /><figcaption>Fig. 6: The message request and respond between E-BIKE and EVSE using OCPP 1.6j protocol during smart charging.</figcaption></figure>

## Charging Connector Port

The proposed DC charging system adopted the charging socket based on EnergyBus standard. EnergyBus has condensed the variety of existing charging systems into one functional, safety-certifiable, charging standards for the E-BIKE sector [11]. The used charging plug and socket is shown in Fig. 7.

<figure><img src="image_9.png" /><figcaption>Fig. 7: EnergyBus protocol compatible charging ports for E-BIKEs (a) universal charging socket, (b) charging plug.</figcaption></figure>

The charging standard for E-BIKEs is already defined in the EU standard EN 50604-1 and in the international standard IEC 61851-3. The charging interface is, rather like the main plug and socket, always identical.

## Simulation Results and Description

The proposed DC charging station along with smart charging scheduling is virtually implemented using MATLAB/Simulink platform to validate the smart charging facility. A single e-Bike charger is considered with 1.5 kW rated with a queue of 4 e-Bikes. The e-Bike drivers send charging request to charging station via mobile app. The CPO takes the EV user information as an input to estimate the optimal current demand. To simulate the smart charging scheduling, the user information is depicted in Table II is considered.

<table><caption>Table II: E-Bike user data to enable smart charging</caption><thead><tr><th></th><th>Arrival Time</th><th>Departure Time</th><th>Initial Energy (kWh)</th><th>Charging Period (h)</th></tr></thead><tbody><tr><td>Bike1</td><td>2.00</td><td>14.00</td><td>0.470</td><td>13</td></tr><tr><td>Bike2</td><td>2.00</td><td>14.00</td><td>0.212</td><td>13</td></tr><tr><td>Bike3</td><td>11.00</td><td>23.00</td><td>0.404</td><td>13</td></tr><tr><td>Bike4</td><td>13.00</td><td>24.00</td><td>0.368</td><td>12</td></tr></tbody></table>

The e-Bike battery capacity nominal voltage and capacity are 58V and 33.5 Ah is considered. The SoC variation and charging rate profile during charging Bike1 with optimal setpoints are illustrated in Fig. 8, where the optimal charging profile is compared with an equal energy distribution-based profile.

<figure><img src="image_10.png" /><figcaption>Fig. 8: The SoC and charging power profile during smart charging of Bike1, which is compared to normal charging strategy.</figcaption></figure>
It is observed that the smart charging allows the Bike1 fully charged within 3 hr using variable charging profile up to 0.9 kW (0.5 C-rate) whereas equal energy distribution strategy allows to transfer around 0.15 kW to charge within 13hr. Similarly, Bike4 booked the time slot 13 pm to 12 am to get a fully charge. Thus, Bike4 charged with 0.35 kW flat charging rate for 11 hr, whereas the smart charging allows a fully charge within 5 hr with variable charging rate up to 0.7 kW (0.3 C-rate). The SoC variation and charging rate profile is shown in Fig. 9.

<figure><img src="image_11.png" /><figcaption>Fig. 9: The SoC and charging power profile during smart charging of Bike4 which is compared to the normal charging strategy.</figcaption></figure>

A linear cost model is implemented. The charging scheduler tries to minimize the charging service cost. Smart EV charging scheduler allows the charging from the grid at lower energy tariff than conventional scheduler which is shown in Fig. 10. below. Thus, the charging cost is reduced by 50% compared to the conventional charging method.

<figure><img src="image_12.png" /><figcaption>Fig. 10: The electricity price and overall charging profile during smart charging of electric scooter charging park which is compared to the normal charging.</figcaption></figure>

To investigate the performance of the power electronic stage, a single-phase totem pole-based Bike charger with closed loop control is implemented in MATLAB/Simulink. The voltage and current profile from the AC input is shown in Fig. 11. during Bike1 charging. The AC current of 2.7 A<sub>pk</sub> is drawn during 0.4 kW charging between t = 1 sec and t = 2 sec. The amount of THD is almost 6.5%. The maximum AC current of 3 A<sub>pk</sub> is drawn between t = 3 sec and t = 4 sec when the charging demand is 0.93 kW. The THD is almost 5 % during 0.93 kW charging. The battery performance is also shown in Fig. 12. The initial SoC is 70 % is considered for the simulation. The battery draws around 7 A during 0.4 kW charging between t = 1 sec and t = 2 sec. The battery voltage was 54.3 on average during this time interval. Likewise, the battery voltage was 54.6 V during the maximum charging power (0.93 kW) drawing between t = 3 sec and t = 4 sec. The battery current was 18 A on average. The output voltage ripple was 0.6 V which 1.2% of the battery nominal voltage. Similarly, the current ripple was around 6.6% of the battery 1C rated current.

<figure><img src="image_13.png" /><figcaption>Fig. 11: Grid voltage and current waveforms during charging with the smart charging profile.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 12: Battery SoC, current and voltage profile during smart charging.</figcaption></figure>
Experimental Testing and Result
Discussions

The 1.5 kW rated DC charging container is
implemented as shown in Fig. 13. The significant
components detail is discussed in Section 4. The
performance testing is performed in both ETEC
laboratory and EnergyPark, Zellik, Brussels. The
4-month testing data of a single unit is collected
and analyzed. The API link provided by
Route220 is utilized as CPO to operate the
charging session using OCPP communication.

<figure><img src="image_15.png" /><figcaption>Fig. 13: Proposed DC charger hardware unit including components.</figcaption></figure>

The experimental setup with measurement
devices, electric scooter is shown in Fig. 14. The
charging profile with variable charging rate is
applied to the charger.

<figure><img src="image_16.png" /><figcaption>Fig. 14: The experimental setup of electric scooter charging park in EnergyPark, Zellik, Brussel- an e-Scooter connected with proposed DC charger unit.</figcaption></figure>

The oscilloscope view which is shown in Fig. 15.
contains the measurements of grid voltage
(yellow), grid current (blue), battery voltage
(magenta), battery current (green), and battery
power (red). During 0.8 kW charging rate, 14.8
Adc drawn by battery at 60.6 V. The AC voltage

and current was 233 Vrms and 4.04 Arms.
Similarly, during 0.518 kW charging rate as
shown in Fig, 8.64 Adc drawn by battery at 60.7
V. The AC voltage and current was 232 Vrms and
2.43 Arms. The current waveform is not pure
sinusoidal. The reason may be the non-linear load
characteristics. As the battery charges, its voltage
and impedance change, leading to variations in
the charging current.

<figure><img src="image_17.png" /><figcaption>Fig. 15: Voltage and current measurements from AC and DC side during charging; (a) 0.9 kW charging rate (b) 0.5 kW charging rate</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 16: Frequency response measurement (using FFT) of AC current during charging</figcaption></figure>
This non-linearity can introduce harmonics into the current waveform. Additionally, the filters may not be perfectly effective in eliminating all high-frequency components. The residual high-frequency harmonics can still be present in the output current. The FFT of the input current waveform is shown in Fig. 16. The grid current THD is 7.65% calculated from the FFT profile. The impact of smart charging on the charging cost and required charging time is investigated. In this testing, 4 e-Bikes are being used for charging. The charging cost is estimated using the sum of energy consumption (kWh) and corresponding charging cost (€/kWh) for individual BIKE. The “trapz” command in MATLAB is used to calculate the total cost.

<figure><img src="image_19.png" /><figcaption>Fig. 17: Performance assessment of proposed DC charging system with smart charging feature compared to normal DC charging; (a) charging cost comparison (b) charging time comparison.</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Fig. 18: The measured charging efficiency up to 1500 W output power for e-Bikes.</figcaption></figure>

The charging cost of individual e-Bike with smart charging is shown in Fig. 17(a) compared to the without smart charging. It is observed that the smart charging profile allows up to 50% reduction in charging cost compared to the normal charging. The charging time is reduced due to the increase in charging rate compared to the uncontrolled charger as shown in Fig. 17(b). It was found that the normal charger allows to charge max 150 W rate whereas the proposed DC charger solution allows to charge up to 1.5 kW which leads to fast charging. The charging efficiency based on the measured data is depicted in Fig. 18. The maximum efficiency is 95.76% recorded during the 1 kW output power. During 0.9 kW output power, the measured AC side power is 0.937 kW which indicates the efficiency around 95.6%. Additionally, 91.7% efficiency was found at 0.52 kW output power. The AC power is measured almost 0.563 kW. The charging efficiency is lower than 90% for the charging power less than 0.3 kW.

## Conclusion

In this paper, the development of an interoperable DC charging system with smart charging strategy for an electric scooter is performed and analyzed. The components detail and the objective function of smart charging is also discussed. The proposed DC charger system hardware is fabricated and tested in the laboratory. Firstly, the proposed concept is virtually validated using MATLAB/Simulink software. The virtual model with specification is implemented and the electrical performance is assessed. According to the simulation results, the output voltage and current ripples are 0.6% and 6.6% of the corresponding rated values. The THD of the
charging current is less than 5%. The smart charging algorithm is incorporated with multiple scooters charging events in the simulation model. The result of smart charging is assessed and compared with normal charging strategy. It is found that the smart charging strategy can reduce the charging cost and the charging time up to 50%. The proposed DC charging system is installed in EnergyPark in Zellik to validate the electrical performance with smart charging profile. OCPP 1.6j standard is utilized to communicate with the CPO server. According to the testing data, 95.76% maximum charging efficiency is found during 1 kW charging rate. The charging current THD was more than 7%. The efficiency is poor for the charging rate less than 0.3 kW.

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