Solar PV Battery Charging Using ANN and P&O MPPT
- lms editor
- Nov 19, 2025
- 2 min read
Efficient Solar Charging for EV Systems | ANN MPPT vs P&O MPPT | Intelligent Solar Controllers
Solar energy continues to be a leading choice for powering electric vehicles (EVs) and off-grid systems. However, the efficiency of a solar charging setup heavily depends on how well the controller extracts maximum power from the PV array. In this blog, we explore the working principles of an MBBT Solar Charger Controller integrated with ANN-based MPPT and P&O MPPT algorithms—two of the most widely used techniques for optimizing solar energy harvesting.
🔧 Understanding the MBBT Solar Charger Controller
The MBBT (Model-Based Battery Tracking) solar charger controller combines two intelligent tracking blocks:
MPPT – Maximum Power Point Tracking
MBPT – Maximum/Peak Power Tracking
These blocks work together to maximize the energy extracted from the PV array while regulating battery charging under diverse environmental conditions.
How It Works
Inside the MPPT subsystem, the controller processes:
Irradiation level
Cell temperature
Using these inputs, the controller generates:
Reference voltage (Vref)
Peak power voltage (Vpp)
These reference signals are compared with the actual PV voltage. A PI controller then adjusts the converter duty cycle accordingly. This ensures accurate power tracking and stabilizes battery charging even when sunlight intensity fluctuates.
⚡ Key Components of the System
📌 Solar PV Array
Rated power: 250 W per panel
Rated voltage: 39.9 V
Rated current: 8.1 A
Configuration: 4 panels in series, 2 strings in parallel
Total system capacity: ~2000 W
🔋 Battery System
40 V, 200 Ah battery bank
Stores excess energy for EV charging or standalone loads
🔀 Power Converter
Operates in both buck and boost modes
Duty cycle controlled by MPPT/MBPT logic & battery SOC
Ensures proper charging even during low irradiation
☀️ Simulation Overview and Performance
The simulation evaluates how the MBBT controller performs under changing sunlight conditions:
🌤 Test Scenario
Initial irradiance: 1000 W/m²
Reduced to: 500 W/m² after 5 seconds
📈 Key Observations
PV voltage and current adjust smoothly without large oscillations
Battery charging remains stable and continuous
PV output power transitions steadily during irradiance drop
Duty cycle modifies intelligently based on battery SOC and voltage levels
These results demonstrate the controller’s ability to maintain efficient charging even under rapidly fluctuating environmental inputs.
🤖 ANN MPPT vs P&O MPPT: Which Performs Better?
P&O MPPT
Simple and widely used
Works by perturbing voltage and observing power change
Can exhibit oscillations near the MPP
Slower during rapid irradiance variations
ANN MPPT
Learns PV behavior using training data
Predicts accurate voltage/current reference for maximum power
Faster response with less oscillation
More stable during transient and partial shading conditions
MBBT Controller Advantage
The MBBT controller provides smoother voltage-current transitions, faster adaptation, and minimal overshoot compared to conventional MPPT alone. This makes it particularly effective for battery charging and EV energy management.
📝 Final Thoughts
The MBBT solar charger controller, enhanced with ANN and P&O MPPT, offers a powerful and reliable solution for efficient solar battery charging. Its ability to intelligently track maximum power, regulate charge cycles, and handle changing irradiation makes it a valuable asset in:
EV charging stations
Off-grid solar installations
Hybrid renewable energy systems
Energy storage–based microgrids
If you’re working on solar-based EV charging or advanced MPPT optimization, this controller model provides strong stability, high tracking efficiency, and enhanced battery protection.
Comments