Renewable Energy Systems
Explainable AI-enabled adaptive fuzzy MPPT and energy management for bifacial PV and battery-powered electric vehicle charging system
Published on 07 January 2026 | DOI: 10.1038/s41598-025-34894-4
Executive Impact Summary
This research introduces an Explainable AI (XAI)-enabled adaptive fuzzy Maximum Power Point Tracking (MPPT) controller and a hierarchical rule-based Energy Management System (EMS) for a 10-kW bifacial solar-driven electric vehicle (EV) charging system. The system demonstrates enhanced tracking efficiency, improved power quality, and robust energy management under various environmental conditions, significantly contributing to sustainable EV infrastructure.
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MPPT Tracking Efficiency
0%
Grid Current THD
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Explainability Fidelity
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Convergence Time (PSC)
Deep Analysis & Enterprise Applications
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0.96
Explainability Fidelity
The system achieves a fidelity score of 0.96, indicating that the fuzzy rule base accurately reconstructs the internal decision-making process. This high explainability, combined with a consistency of 0.91 and sparsity of 0.38, ensures transparent and trustworthy AI control, crucial for safety-critical power system applications.
| Algorithm | Tracking Efficiency (%) | Convergence Time (s) |
|---|---|---|
| P&O | 76.4 | 0.45 |
| Incremental Conductance | 75.8 | 0.48 |
| Proposed XAI-Fuzzy MPPT | 80.9 | 0.18 |
The proposed XAI-Fuzzy MPPT controller significantly outperforms conventional methods under partial shading, achieving an 80.9% tracking efficiency (4.5% higher than P&O) and a 60% faster convergence time (0.18s).
Enterprise Process Flow
Measure Vpv, Ipv
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Calculate Ppv, ΔV, ΔP
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Calculate Error Terms E(k), ΔE(k)
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Fuzzification
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Inference Engine (Rule Base)
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Defuzzification (CoA)
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Calculate ΔD (Output)
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Update Duty Cycle D(k)
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Send to PWM Generator
24-Hour Energy Management System Operation
The EMS was simulated for a 24-hour clear-day profile at MMMUT, Gorakhpur. It effectively manages power flow through five operational modes, optimizing solar self-consumption, providing grid support, and ensuring continuous EV charging.
- Morning (07:00-10:00): PV output increases, BESS supplies initial EV charging deficit (SOC drops 50% to 20%).
- Midday (10:00-14:00): Peak solar generation. EV battery fully charged, BESS reaches 90% SOC by 11:30. Surplus power exported to grid.
- Evening (14:00-16:00): Solar declines, PV + BESS supply new EV demand. SOC gradually decreases.
- Night (16:00-20:00): Insufficient solar. BESS supplies EV until 20% SOC, then grid imports begin.
- System consistently maintains grid power quality with a THD of 2.38%.
Calculate Your Potential ROI
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Estimated Annual Savings
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Hours Reclaimed Annually
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Phased Implementation for Enterprise Adoption
A structured approach to integrating advanced AI energy management, ensuring seamless transition and maximized benefits.
Phase 1: Proof-of-Concept & Validation
Develop and simulate a detailed model of the XAI-enabled MPPT and EMS in a controlled environment (e.g., MATLAB/Simulink). Conduct extensive testing under various irradiance, temperature, and shading conditions. Validate explainability metrics and initial performance against traditional benchmarks. Engage with a pilot partner for initial data collection.
Phase 2: Hardware-in-the-Loop (HIL) & Prototype Development
Transition the validated control algorithms to a Hardware-in-the-Loop (HIL) setup to test real-time performance, converter nonlinearities, and disturbance rejection. Develop a scaled-down prototype of the bifacial PV-BESS-EV charging station. Refine adaptive calibration mechanisms for bifacial gains based on site-specific albedo conditions. Begin preliminary field testing at a controlled site.
Phase 3: Pilot Deployment & Optimization
Deploy a full-scale pilot system at a target enterprise location (e.g., corporate campus, public charging hub). Integrate predictive control with machine learning (e.g., LSTM models) for proactive energy management and V2G capabilities. Gather extensive real-world operational data to continuously optimize fuzzy rules, scaling factors, and EMS logic. Focus on scalability and economic viability.
Phase 4: Full-Scale Integration & Grid Services
Expand deployment across multiple enterprise sites. Implement advanced grid support services, including peak shaving and frequency regulation, leveraging the EV fleet as active grid resources. Further explore symbolic regression or attention-based models for enhanced efficiency and interpretation. Establish robust maintenance and monitoring protocols for long-term reliability and performance.
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