Power control method and device for industrial forklift charger

By using LLC resonant topology and stochastic neural control barrier function algorithm, combined with data-driven hybrid predictive control, the parameters of magnetic components and adaptive charging strategies are optimized, solving the problems of high-frequency loss of magnetic components and battery aging in industrial forklift charging systems, and achieving efficient and stable charging control and extended battery life.

CN121157700APending Publication Date: 2025-12-19SHENZHEN TRANSFORMER ELECTRONICS
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Patent Information

Application Number
CN202511280628.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-09
Publication Date
2025-12-19

AI Technical Summary

Technical Problem

Existing industrial forklift charging technologies suffer from high high-frequency losses in magnetic components, a lack of adaptive strategies for battery aging, and insufficient robustness in power control algorithms, resulting in difficulty in improving overall efficiency and shortening battery life.

Method used

By employing an LLC resonant topology combined with a stochastic neural control barrier function algorithm and a data-driven hybrid predictive control strategy, and optimizing the magnetic component parameters through a probabilistic learning algorithm, dynamic power control and adaptive charging curve optimization algorithms are constructed to achieve real-time adjustment of the battery state.

Benefits of technology

It effectively reduces the high-frequency loss of magnetic components, improves the control accuracy and stability of the system in complex environments, extends battery life, and achieves zero-voltage switching and zero-current switching operation across the entire load range, thereby improving charging efficiency.

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Abstract

The invention discloses a power control method and device for an industrial forklift charger, and the method comprises the steps: obtaining industrial forklift charging demand data and LLC resonant topology characteristic parameters, and constructing an LLC resonant converter; on the basis of the working frequency range and the power density requirement, through a probability learning algorithm and a random agent model, a random agent model for multi-objective optimization of magnetic element parameters is formed, and a high-frequency low-loss magnetic element is obtained; a high-frequency low-loss magnetic element is integrated into an LLC resonant converter, a random neural control barrier function algorithm is realized, and dynamic power control is performed on a charging process; based on the real-time data, a data-driven hybrid prediction control strategy is realized, and a charging parameter optimization instruction is generated; and in combination with the battery charge and discharge characteristic data and the battery aging state evaluation model, battery state evaluation and charging parameter adaptive adjustment are realized. The problems that a traditional charger is insufficient in control precision, low in efficiency, short in battery life and the like in a complex industrial environment are solved.
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