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<p>The strong randomness and pulsatility of electric vehicle (EV) charging loads at urban stations often create quasi-static blind zones, making precise power scheduling and automatic balancing difficult. To address this, an automatic charging-discharging balancing method based on variable-universe fuzzy PID control is proposed. A global state perception system using narrowband Internet of Things (NB-IoT) enables high-frequency acquisition of key parameters, including battery capacity and grid power supply capacity. Monte Carlo simulations integrating Markov decision processes for travel chains and improved S-shaped probability functions are introduced to model clustered charging demands under uncertain user behaviors. A variable-universe fuzzy PID controller with function-based scaling factors is innovatively designed to dynamically adjust input/output variable universes, thereby eliminating control blind zones. On the charging side, a feedforward-feedback hybrid control structure allocates power in real time according to demand deviations and their rates of change, mitigating grid impacts. On the discharging side, grid demand and onboard energy storage state deviations serve as inputs to precisely schedule vehicle-to-grid (V2G) reverse power transmission, enabling bidirectional energy interaction. Experimental results show that the proposed method limits the charging/discharging power control error of a single charging pile to within ± 0.1&#xa0;kW in heterogeneous hardware environments, with no overshoot throughout. Under the extreme condition of 80 EVs charging simultaneously, the regulation time is 82&#xa0;s, demonstrating superior robustness. This method successfully realizes automatic balancing control for both charging and discharging at EV charging stations.</p>

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Automatic Charging-Discharging Balancing for Urban EV Charging Stations Using Variable-Universe Fuzzy PID Control

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Xiaoyin Ding,
  • Yingxuan Zheng,
  • Qi Chen

摘要

The strong randomness and pulsatility of electric vehicle (EV) charging loads at urban stations often create quasi-static blind zones, making precise power scheduling and automatic balancing difficult. To address this, an automatic charging-discharging balancing method based on variable-universe fuzzy PID control is proposed. A global state perception system using narrowband Internet of Things (NB-IoT) enables high-frequency acquisition of key parameters, including battery capacity and grid power supply capacity. Monte Carlo simulations integrating Markov decision processes for travel chains and improved S-shaped probability functions are introduced to model clustered charging demands under uncertain user behaviors. A variable-universe fuzzy PID controller with function-based scaling factors is innovatively designed to dynamically adjust input/output variable universes, thereby eliminating control blind zones. On the charging side, a feedforward-feedback hybrid control structure allocates power in real time according to demand deviations and their rates of change, mitigating grid impacts. On the discharging side, grid demand and onboard energy storage state deviations serve as inputs to precisely schedule vehicle-to-grid (V2G) reverse power transmission, enabling bidirectional energy interaction. Experimental results show that the proposed method limits the charging/discharging power control error of a single charging pile to within ± 0.1 kW in heterogeneous hardware environments, with no overshoot throughout. Under the extreme condition of 80 EVs charging simultaneously, the regulation time is 82 s, demonstrating superior robustness. This method successfully realizes automatic balancing control for both charging and discharging at EV charging stations.