EVs and Batteries Unite in Smart Grid Control Breakthrough
A groundbreaking new power coordination strategy is redefining how electric vehicles (EVs) and battery energy storage systems (BESS) interact within modern hybrid AC/DC microgrids. Researchers from Henan Polytechnic University have developed an advanced control framework that integrates EVs not just as transportation tools, but as dynamic, responsive components of a smarter, more resilient energy ecosystem. This innovation addresses critical challenges in grid stability, energy efficiency, and the sustainable integration of renewable sources, offering a practical pathway toward next-generation smart grids.
The research, led by Wang Hao, Wu Zhe, Kang Boyang, Li Bin, and Li Shaoling from the School of Electrical Engineering and Automation at Henan Polytechnic University, along with the Henan Key Laboratory of Intelligent Detection and Control for Coal Mine Equipment, introduces a sophisticated control strategy that treats EVs and stationary batteries as a unified, joint energy storage system (CESS). This approach moves beyond the traditional model where energy storage is fragmented across AC and DC subnetworks, often leading to inefficiencies and increased stress on key power conversion components. The team’s work, published in a leading energy journal, demonstrates how a coordinated, hierarchical control system can optimize power flow, extend the lifespan of storage assets, and enhance overall grid reliability.
The core of the challenge lies in the inherent variability of renewable energy sources like solar and wind, coupled with the unpredictable nature of electricity demand. In a hybrid AC/DC microgrid, which seamlessly connects both alternating current and direct current networks, maintaining a perfect balance between power generation and consumption is a constant, complex task. When power is generated in excess, it must be stored; when demand spikes, stored energy must be released. Historically, this balancing act has relied heavily on bidirectional interlinking converters (BILCs) to shuttle power between the AC and DC sides of the grid. However, this centralized reliance creates a bottleneck. When one side of the grid is producing surplus power and the other is experiencing a deficit, the BILC becomes the sole conduit for power transfer, leading to potential overloads and energy losses.
Furthermore, the conventional approach of using separate, decentralized battery systems on each side of the grid to manage local imbalances introduces another problem: energy interaction losses. To balance the state of charge (SOC) between batteries on the AC side and those on the DC side, power must be constantly cycled through the BILC, wasting energy in the conversion process and accelerating the wear on the converter. This inefficiency undermines the economic and environmental benefits of the microgrid. Additionally, relying solely on BILCs for coordination, especially in systems without robust communication networks, limits operational flexibility and can lead to unstable conditions during rapid power fluctuations.
The research team identified these limitations and proposed a paradigm shift. Instead of treating EVs and batteries as isolated assets, they conceptualized a joint energy storage system. This CESS acts as a central, shared reservoir that can interact directly with both the AC and DC subnetworks through dedicated bidirectional converters—a DC-AC converter (BDAC) for the AC side and a DC-DC converter (BDDC) for the DC side. This architecture fundamentally changes the power dynamics. The CESS can now absorb surplus power from either subnetwork or inject power to support a deficit, without necessarily forcing all inter-subnetwork power flow through the BILC. This design significantly reduces the transmission burden on the BILC, enhancing its longevity and the overall system’s efficiency.
A critical innovation in this strategy is the implementation of a sophisticated, multi-layered control system. The researchers adopted a hierarchical control structure, which is essential for managing the complexity of a modern microgrid. At the base level is the device control layer, responsible for the real-time operation of individual components like wind turbines, solar panels, micro-turbines, and the various power converters. This layer ensures that distributed generators (DGs) operate at peak efficiency—for instance, using maximum power point tracking (MPPT) for solar and wind units—and that the primary power sources use droop control to maintain stable voltage and frequency on the AC and DC buses.
Sitting above this is the coordination control layer, the brain of the operation, managed by a Microgrid Central Controller (MGCC). This layer is where the intelligence of the new strategy truly shines. The MGCC continuously collects data from the entire system, including the net power output of the AC and DC subnetworks, the voltage and frequency of the main buses, and, most importantly, the SOC of every battery and connected EV. With this comprehensive real-time picture, the MGCC can make informed decisions about the overall system state.
The researchers devised a novel method for classifying the microgrid’s operational state. They divided system behavior into four distinct modes based on the total net power imbalance and the capacity of the CESS to correct it. The first mode is autonomous operation, where the AC and DC subnetworks are perfectly balanced internally, requiring no action from the CESS or the BILC. The second is surplus mutual support, where there is an overall power surplus. In this mode, the CESS is tasked with charging, absorbing the excess energy. The third is deficit mutual support, where there is a system-wide power shortage, and the CESS must discharge to provide support. The fourth, and most critical, is the power limit-exceeded mode, which occurs when the imbalance is so severe that the CESS cannot compensate on its own, necessitating actions like curtailing renewable generation or shedding non-essential loads to maintain stability.
Within these primary modes, the researchers defined multiple specific operating conditions, or “work conditions,” which dictate exactly which power converters should be active. For example, in the surplus mutual support mode, if only the AC side has excess power, the BDAC will activate to charge the CESS. If only the DC side has excess, the BDDC activates. If both sides have a surplus, both converters can work simultaneously. If one side has a surplus and the other a deficit, the BILC will first attempt to balance them directly. Only if there is still a surplus after this inter-subnetwork transfer will the CESS engage to absorb the remaining excess. This layered, conditional logic ensures that the most efficient and least stressful path for power flow is always chosen.
The true brilliance of the strategy lies in how it manages the CESS itself, particularly the complex interaction between the stationary batteries and the fleet of EVs. The researchers recognized that these two storage types have vastly different characteristics and constraints. Stationary batteries are designed for high-cycle, rapid-response applications but are expensive and degrade with frequent, deep cycling. EVs, on the other hand, represent a vast, distributed, and often underutilized storage resource. However, their participation in grid services is constrained by the needs of their owners—no driver wants to find their car with a depleted battery when they need to leave.
To address this, the team developed a sophisticated priority-based response system for the CESS. For the stationary batteries, they implemented a dynamic priority scheme based on their SOC. Batteries that are nearing a fully charged state are given the highest priority to discharge, preventing overcharging. Conversely, batteries that are nearly empty are given the highest priority to charge, preventing deep discharge, which is harmful to battery health. This intelligent load-balancing ensures that the battery bank operates within a safe and optimal SOC range, maximizing its lifespan and performance.
For the EVs, the priority system is even more nuanced and user-centric. The control strategy first identifies a subset of EVs that are in a “charge-only” zone. These are vehicles whose owners have set a minimum SOC (a “constraint SOC”) that must be met before the vehicle can be used for any grid-supporting discharge. For these vehicles, the system treats them as a fixed load that must be satisfied, prioritizing their charging above all else. This guarantees that the user’s primary transportation need is never compromised.
For the remaining EVs that are capable of vehicle-to-grid (V2G) operations, the system assigns charging and discharging priorities based on how close their current SOC is to their personal constraints. An EV with a SOC just above its minimum constraint has a very low margin for discharge and is therefore given a low discharging priority. An EV that is nearly full has a high discharging priority, as it can safely release a large amount of energy back to the grid without impacting the owner’s needs. Similarly, for charging, an EV with a very low SOC is given a high charging priority to bring it back to a usable level quickly. This granular, SOC-based priority system ensures that the most flexible and available storage resources are used first, making the CESS highly responsive and efficient.
The coordination between the batteries and the EVs is carefully managed. In the surplus mutual support mode, when the CESS is charging, the system first ensures that all “charge-only” EVs receive their required power. The remaining surplus is then allocated to the stationary batteries and the V2G-capable EVs, with charging priorities determined by their SOC. In the deficit mutual support mode, when the CESS is discharging, the stationary batteries are given priority as the primary, stable source of power. The V2G-capable EVs are then called upon as “emergency support units,” providing additional power only when needed, thus minimizing the impact on EV owners and preserving their battery life.
To prevent unnecessary wear and tear on all storage components, the researchers introduced a critical safeguard: a power threshold. The CESS does not engage for every minor fluctuation in power. Only when the net power imbalance exceeds a predefined threshold is the CESS activated. This hysteresis prevents the system from constantly cycling the storage units on and off for small, transient imbalances, which would be inefficient and damaging. Furthermore, to prevent a single battery or EV from being repeatedly switched in and out of service when its SOC is hovering near a priority boundary, the system incorporates a time delay. A unit is only disconnected from the control loop if its SOC crosses a critical threshold and remains beyond it for a set period, such as 0.02 seconds. This “debounce” mechanism adds stability and prevents chattering in the control system.
The effectiveness of this comprehensive strategy was rigorously tested using a detailed simulation model built on the Matlab/Simulink platform. The researchers created a realistic scenario with a diverse mix of power sources, including wind, solar, and micro-turbines, and a variety of loads. They simulated a fleet of EVs with different charging needs and a battery bank with units at various SOCs, assigning them to different priority levels as per their control logic.
The simulation results were compelling. The system demonstrated a seamless ability to transition between the four operational modes in response to changing power conditions. The BILC’s power flow was dramatically reduced, as the CESS effectively managed most of the surplus and deficit power directly with the subnetworks. The SOC of the stationary batteries was kept remarkably balanced, with units smoothly transitioning between priority levels as they charged and discharged. The “charge-only” EVs were always guaranteed their required power, while the V2G-capable EVs participated in grid support only when it was safe and appropriate, based on their assigned priorities.
In scenarios of extreme power imbalance, the system correctly entered the power limit-exceeded mode. For severe surpluses, it automatically curtailed the output of renewable generators in a controlled manner. For severe deficits, it shed non-critical loads to maintain system stability, all while the CESS provided its maximum possible support. These actions prevented system collapse and demonstrated the strategy’s robustness in handling real-world emergencies.
The implications of this research are far-reaching. As the world moves toward a future dominated by renewable energy and electric transportation, the integration of these two systems is paramount. This control strategy provides a blueprint for creating microgrids that are not only more stable and efficient but also more economical. By reducing stress on power converters, extending the life of expensive battery banks, and intelligently harnessing the latent storage capacity of EVs, the total cost of ownership for a microgrid can be significantly lowered.
Moreover, this approach empowers consumers. EV owners can participate in the energy market, potentially earning revenue from their vehicle’s battery, without sacrificing the reliability of their transportation. The system’s user-centric design, with its strict respect for owner-defined SOC constraints, builds trust and encourages wider adoption of V2G technology.
The work of Wang Hao, Wu Zhe, Kang Boyang, Li Bin, and Li Shaoling represents a significant leap forward in microgrid control. It transforms the relationship between transportation and energy from a simple consumer-producer dynamic into a sophisticated, symbiotic partnership. Their strategy is not just a theoretical exercise; it is a practical, scalable solution that can be deployed in communities, campuses, industrial parks, and remote locations around the world. By unifying EVs and batteries into a single, intelligent storage system, they have paved the way for a more resilient, sustainable, and user-friendly energy future.
Wang Hao, Wu Zhe, Kang Boyang, Li Bin, Li Shaoling, School of Electrical Engineering and Automation, Henan Polytechnic University; Henan Key Laboratory of Intelligent Detection and Control for Coal Mine Equipment. Published in a leading energy journal. DOI: 10.19595/j.cnki.1000-6753.tees.240618