Modeling and Simulation of a Hybrid PV-Wind Energy System for Electric Vehicle Battery Swapping Stations
- lms editor
- 2 hours ago
- 8 min read
Abstract
The escalating adoption of electric vehicles (EVs) necessitates the development of reliable and accessible charging infrastructure, particularly in off-grid or remote locations. Standalone renewable energy sources are often limited by their inherent intermittency. This paper presents a comprehensive MATLAB/Simulink model of a hybrid renewable energy system (HRES) designed to power an EV battery swapping station. The proposed system integrates a photovoltaic (PV) array and a wind energy conversion system (WECS), coupling them to a common DC bus to charge EV batteries. To maximize energy harvesting, the system employs a dual Maximum Power Point Tracking (MPPT) control strategy, utilizing a Fuzzy Logic controller for the PV subsystem and a Perturb and Observe (P&O) algorithm for the WECS. Simulation results demonstrate the system's effectiveness, showing consistent battery charging performance under prescribed transient solar irradiance and wind speed profiles. The findings validate the viability of the hybrid system as a robust and stable solution for off-grid EV charging applications.
Keywords
Hybrid Renewable Energy System (HRES), Electric Vehicle (EV) Charging, Battery Swapping, Maximum Power Point Tracking (MPPT), Perturb and Observe (P&O), Fuzzy Logic Control.
I. Introduction
The global shift towards sustainable transportation has led to a rapid increase in the adoption of electric vehicles (EVs). This transition imposes significant demands on the development of a robust and widely available charging infrastructure. However, reliance on conventional grid-based charging can strain existing power networks and may not be feasible in remote or off-grid areas. While renewable energy sources offer a clean alternative, single-source systems, such as standalone solar or wind, are inherently unreliable due to their dependence on intermittent environmental conditions. This limitation poses a significant challenge for applications requiring a consistent power supply, as it diminishes the system's capacity factor and power availability—critical metrics for reliable charging infrastructure.
To overcome the intermittency of individual renewable sources, Hybrid Renewable Energy Systems (HRES) that combine multiple generation technologies have emerged as a promising solution. By integrating photovoltaic (PV) and wind energy, an HRES can provide a more stable and continuous power output, as solar and wind resources often complement each other. This paper proposes and analyzes a standalone HRES specifically designed to power an EV battery swapping station. The system model, developed in the MATLAB/Simulink environment, features a PV array and a wind turbine, each governed by a distinct Maximum Power Point Tracking (MPPT) algorithm to optimize energy capture. This study evaluates the system's performance under dynamic conditions to validate its potential as a reliable off-grid charging solution.
II. Proposed System Configuration
The architecture of the proposed hybrid energy system is designed to integrate two distinct renewable energy sources—solar and wind—with an energy storage component to create a self-sufficient, standalone charging station for EVs. This integrated approach is strategic, ensuring greater power availability and reliability by leveraging the complementary nature of solar and wind resources. The system is structured around a common DC bus, which serves as the central point for power aggregation and distribution to the EV battery (Fig. 1).
Fig. 1. Block diagram of the proposed hybrid PV-Wind energy system for the EV battery swapping station.
The system is composed of three primary subsystems:
• A. Wind Energy Conversion System (WECS): This subsystem is responsible for converting wind's kinetic energy into electrical energy. It consists of a wind turbine mechanically coupled with a permanent magnet generator. The AC power produced by the generator is converted to DC power by a rectifier. This DC output is then stepped up and regulated by a DC-DC boost converter before being supplied to the common DC bus, thereby ensuring the wind's variable energy is conditioned for stable delivery to the battery.
• B. Photovoltaic (PV) Power System: This subsystem captures solar energy. It comprises a PV panel array that generates DC electricity when exposed to sunlight. Similar to the WECS, the PV array is connected to the common DC bus via a dedicated DC-DC boost converter, which manages the power transfer to maintain a stable charging voltage.
• C. Energy Storage and Charging System: The core of the charging application is the EV battery, which is connected directly to the common DC bus. Its primary function is to absorb and store the combined power generated by both the WECS and the PV system. The design is intended for a battery swapping station, allowing for the connection of multiple EV batteries for sequential charging and enabling a depleted battery from an EV to be swapped with a fully charged one from the station, thus providing a consistent energy buffer for the charging service.
The effective operation of this integrated system relies on sophisticated control strategies to manage the power flow from each variable source, which are detailed in the following section.
III. Control Strategy and Modeling
The efficiency of a renewable energy system is critically dependent on its ability to extract the maximum available power from the source, which fluctuates with environmental conditions. This is achieved through Maximum Power Point Tracking (MPPT) controllers. In the proposed hybrid system, distinct MPPT control algorithms are implemented for the wind and PV subsystems to optimize their individual energy yields and ensure the highest possible power transfer to the EV battery.
The primary control objective for the Wind Energy Conversion System (WECS) is to extract maximum power from the wind turbine under varying wind speeds. This is accomplished using a Perturb and Observe (P&O) MPPT algorithm. The algorithm operates by introducing a small perturbation to the boost converter's duty cycle. It then measures the resulting change in output power (ΔP). If ΔP is positive, the perturbation continues in the same direction to approach the MPP; if ΔP is negative, the direction of perturbation is reversed. By continuously monitoring the rectifier's voltage and current, the controller iteratively adjusts the duty cycle to converge upon and track the peak of the turbine's power curve.
B. MPPT Control for the PV System
For the PV system, the control objective is to extract maximum power from the solar panel array as solar irradiance levels change. A Fuzzy Logic-based MPPT controller is implemented for this purpose. This choice is advantageous due to its robustness in handling the non-linear V-I characteristics of PV panels and its faster response time compared to conventional algorithms under rapidly changing irradiance. The controller utilizes two inputs derived from the PV array's voltage and current measurements, defined with standard notation as:
1. Error (E): E = ΔP/ΔV
2. Change in Error (ΔE): ΔE = E(k) - E(k-1)
These inputs are processed through a set of fuzzy logic rules that emulate expert reasoning. The output of the fuzzy controller is a precise adjustment to the duty cycle, which is fed to a Pulse-Width Modulation (PWM) generator to create the switching signal for the PV system's boost converter. This process ensures the PV array consistently operates at its maximum power point. The implementation and validation of these control strategies were carried out within the comprehensive simulation environment described next.
IV. Simulation Model and Parameters
To validate the design and performance of the proposed hybrid charging system, a detailed model was developed and simulated using the MATLAB/Simulink environment. Simulation is a crucial step that allows for the verification of system dynamics and control strategy effectiveness under a wide range of controlled operating conditions before any physical hardware is implemented.
The key power specifications for the renewable energy components modeled in the simulation are detailed in the table below.
Table 1: Key System Specifications
Component | Specification |
PV Panel Array Power | 2000 W |
Wind Turbine System Power | 2500 W (at 12 m/s wind speed) |
A specific dynamic scenario was designed to test the system's response to realistic and challenging environmental variations. The simulation was configured with the following input conditions:
1. Solar Irradiance: The solar irradiance was varied in discrete steps to simulate changing cloud cover or time of day. The sequence begins at 1000 W/m² (full sun), steps down to 500 W/m², then to 10 W/m² (near darkness), and subsequently steps back up to 500 W/m² and 1000 W/m². Each irradiance level is maintained for 0.3 seconds.
2. Wind Speed: The wind speed is held constant at 12 m/s for the first second of the simulation. At t = 1 second, the wind speed is changed to 10.8 m/s to test the system's response to a drop in wind resource.
The outcomes of this simulation provide critical insights into the system's operational performance and charging capabilities.
V. Results and Discussion
This section presents a detailed analysis of the simulation results, evaluating the performance of the hybrid PV-wind charging system under the dynamic environmental conditions previously defined. The analysis focuses on the power output from each renewable subsystem (Fig. 2), the combined charging power and current delivered to the EV battery (Fig. 3), and the battery's State of Charge (SoC) (Fig. 4).
System Performance under Varying Solar Irradiance (Wind Speed = 12 m/s):
• During the initial phase with high solar irradiance (1000 W/m²), the PV system generates its rated power of approximately 2000 W. Simultaneously, the WECS, operating at a constant wind speed of 12 m/s, produces its nominal output of 2500 W. The combined generation results in total battery charging power that peaked at approximately 4000 W, with a corresponding charging current of approximately 10 A (Note: Charging current is represented as a positive value).
• When the solar irradiance drops to 500 W/m², the PV power output reduces accordingly. The wind power remains constant at 2500 W, and the total charging power decreases, causing the battery charging current to fall to the range of 7-8 A.
Fig. 2. Power output from PV and Wind sources. Fig. 3. EV battery charging power and current.
• Under near-zero solar input (10 W/m²), the PV power output drops to almost zero. In this scenario, the system demonstrates its hybrid advantage, as the battery charging is sustained solely by the WECS, which continues to supply 2500 W. This results in a stable charging current of approximately 6 A, ensuring the charging process is not interrupted by the lack of solar resource.
System Performance under Varying Wind Speed:
• At the t = 1 second mark, the simulation introduces a drop in wind speed from 12 m/s to 10.8 m/s. This change causes a corresponding decrease in the power generated by the WECS. The simulation results show that the P&O MPPT controller for the wind system effectively tracks the new maximum power point at the lower wind speed, and the total charging power delivered to the battery reflects this reduction.
EV Battery Charging Performance:
• Crucially, the State of Charge (SoC) exhibits a monotonically increasing trajectory throughout the simulation. This demonstrates that even during periods of minimal solar input (at t = 0.6s) or reduced wind speed (after t = 1s), the system's remaining generation capacity was sufficient to maintain a positive charging power balance. This result validates the core design principle of the hybrid architecture: to provide reliable and uninterrupted charging despite significant fluctuations in individual energy sources.
Fig. 4. EV battery State of Charge (SoC).
VI. Conclusion and Future Scope
This paper developed and simulated a MATLAB/Simulink model of a hybrid PV-wind energy system designed for an Electric Vehicle battery swapping station. The model integrated a 2000 W PV array and a 2500 W wind turbine system, each governed by an independent Maximum Power Point Tracking algorithm—Fuzzy Logic for PV and Perturb and Observe for the wind system. The simulation results conclusively demonstrate that the proposed dual-MPPT control scheme effectively maximizes energy harvesting from both renewable sources under highly variable solar irradiance and wind speed conditions. The system successfully provided continuous and stable charging to the EV battery, confirming the viability of the proposed architecture for reliable, off-grid EV charging applications. These findings underscore the potential of intelligently controlled hybrid renewable systems to form the backbone of a decentralized, resilient, and sustainable transportation energy infrastructure.
Looking forward, several avenues exist for expanding upon this work. The current model represents a small-scale system; future efforts could focus on scaling up the power rating of the PV and wind components to support the simultaneous charging of a larger number of EV batteries. Furthermore, this simulation-based study lays the groundwork for the real-time implementation of the battery swapping concept, which would involve developing physical prototypes and control hardware to validate the system's performance in a practical setting.
VII. YouTube Video
VIII. Purchase link of the Model
SKU: 0315
Comments