A novel maximum power point tracking with hybrid control algorithm for automotive thermoelectric generator system

Area of Science:

• Automotive Engineering
• Thermoelectric Energy Conversion
• Control Systems

Background:

• Automotive thermoelectric generator (ATEG) systems offer potential for waste heat recovery.
• Efficient real-time maximum power point tracking (MPPT) is crucial for optimizing ATEG performance.
• Existing MPPT algorithms face challenges in dynamic conditions and tracking accuracy.

Purpose of the Study:

• To develop and validate a novel MPPT algorithm for ATEG systems.
• To enhance the real-time tracking speed and accuracy of ATEG power output.
• To improve the overall efficiency and stability of ATEG systems.

Main Methods:

• Integration of Kalman filtering and fuzzy control for a novel MPPT algorithm.
• Utilization of a two-phase interleaved parallel DC-DC boost converter.
• Comparison with the traditional incremental conductance algorithm.

Main Results:

• The novel MPPT algorithm significantly reduced tracking time compared to the incremental conductance method.
• Kalman filtering effectively eliminated high-frequency components, improving tracking stability.
• The new algorithm achieved a tracking accuracy of 94.9%, surpassing the traditional method by 5.2%.

Conclusions:

• The proposed Kalman filter and fuzzy control-based MPPT algorithm provides precise and rapid tracking of ATEG maximum power points.
• This novel approach enhances tracking stability and accuracy, outperforming conventional methods.
• The algorithm is suitable for real-time application across the entire vehicle driving cycle.