Adaptive power allocation strategy for hybrid energy storage system based on driving pattern recognition

The power allocation strategy of hybrid energy storage systems plays a decisive role in energy management for electric vehicles. However, existing online real-time power allocation strategies primarily rely on expert knowledge to make rules. Due to the real time changes in driving patterns, it is necessary for the power allocation strategy to possess adaptive capabilities to ensure optimal energy efficiency. Therefore, an adaptive power allocation strategy is proposed in this paper, which takes into account the driving patterns of electric vehicles. Firstly, driving pattern recognition is performed using the K-means clustering method to extract features of driving profiles. Then, the fuzzy logic control strategy is adopted to make the rules of filter frequency, and the rules is adaptive adjusted based on the results of driving pattern recognition. To achieve smoother power output from lithium-ion batteries and prolong their lifespan, the digital low pass filter is used to accomplish the fuzzy logic control strategy, and the filtering frequency is online optimized by the above fuzzy controller. According to the digital low pass filter, the load power demand is decomposed high-frequency component and low-frequency component. The decomposed high-frequency component of power demand is supplied by supercapacitors, while the low-frequency component is provided by lithium-ion batteries. To validate the effectiveness of the proposed method, comparative experiments are conducted between the proposed strategy and another four strategies. The experimental results demonstrate that the proposed strategy can effectively smooth the power output of lithium-ion batteries and reduce total energy losses.