A Method of Energy Management Based on Working Condition Recognition

Abstract

The invention discloses an energy management method based on working condition identification, which includes, using the K-means clustering algorithm to classify the working conditions of four typical working conditions, and calculating the cluster centers of the four typical working conditions; The minimum control strategy for effective fuel consumption; combining the multi-island genetic algorithm and the sequence quadratic programming algorithm, and using the Task component in the Isight software to construct a combined optimization model; using the combined optimization model to analyze the variables and four typical equivalent fuel consumption minimum control strategies The cluster centers of the working conditions are integrated and optimized to obtain the final cluster centers and the corresponding optimal equivalent fuel coefficients under four typical working conditions; extract a section of random driving condition characteristic parameters, and classify the working conditions. The typical working condition type and the optimal equivalent fuel coefficient are used for optimal distribution of power; the invention can realize the identification of working condition characteristic parameters, and at the same time solve the shortcomings of a single local or global optimization algorithm.

Description

technical field [0001] The invention relates to the technical field of hybrid electric vehicle energy management, in particular to an energy management method based on working condition identification. Background technique [0002] Compared with instantaneous optimization, global optimization, and adaptive energy management strategies based on working condition identification, the rule-based energy management strategy has relatively low fuel economy. Because the instantaneous optimization is optimized for a certain driving condition, it has poor adaptability to other working conditions. At the same time, the potential of instantaneous optimization to improve fuel economy is insufficient; the global optimization algorithm is complex and computationally intensive, and it needs to know the future road conditions. The practicability is poor; the adaptive energy management strategy based on working condition identification can better improve the fuel economy of the whole vehicle....

AI Technical Summary

Problems solved by technology

[0002] Compared with instantaneous optimization, global optimization, and adaptive energy management strategies based on working condition identification, the rule-based energy management strategy has relatively low fuel economy.

Because the instantaneous optimization is optimized for a certain driving condition, it has poor adaptability to other working conditions. At the same time, the potential of instantaneous optimization to improve fuel economy is insufficient; the global optimization algorithm is complex and computationally intensive, and it needs to know the future road conditions. The practicability is poor; the adaptive energy management strategy based on working condition identification can better improve the fuel economy of the whole vehicle. The commonly used working condition identification algorithms are neural network, support vector machine, etc. Poor computing power makes it difficult to directly apply these operating condition recognition algorithms to actual vehicle energy management strategies

In addition, optimizing the design of the hybrid system by manually adjusting parameters or writing an optimization algorithm requires a large and cumbersome workload, and the accuracy is difficult to guarantee.

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