Firefly algorithm-based optimization of a high-efficiency DC–DC converter for hybrid energy-powered electric vehicles

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Due to the rising trend of electric vehicle use, there is an increasing need for high gain DC-DC converters that perform well in terms of energy management and robust control under varying renewable energy conditions. In this work, a Firefly Algorithm-optimized PID-controlled Self-Lift Luo (SL Luo) converter integrated with a hybrid energy storage system (HESS) comprising a lithium-ion battery and a supercapacitor for photovoltaic (PV)-fed EV applications. Traditional converters are inefficient, and their size makes them difficult to handle. Hence, a novel system with Self-Lift Luo (SL Luo) converter is designed without any additional booster components such as voltage multiplier cell or interleaved inductor-based circuits. To investigate system stability and attain better results, a reduced-order state-space modeling is used, which offers reduced computational complexity and high accuracy. Initially, the Zeigler Nicholas approach is used to construct a PID controller, and the performance is compared to the Optimization technique. Then, The Firefly-PID methodology was incorporated, which outperforms the ZN-PID method, according to performance results, with a rise time of 0.18 s, a settling time of 0.1 s, zero overshoot, and no steady-state error or ripple voltage. The proposed HESS ensures uninterrupted power delivery during renewable energy deficits through seamless battery-assisted power sharing. The regulated DC-link voltage drives a BLDC motor through a voltage source inverter, achieving 0% speed regulation, 11.92% current THD, and a 5.56% torque ripple factor, thereby improving drive smoothness and power quality. To manage the drive speed, no additional speed control devices or dedicated speed sensors are required beyond the built-in Hall sensors used for commutation. Hardware implementation validates the simulation results, demonstrating a maximum efficiency of 96% and confirming the practical feasibility of the proposed converter and control strategy for high-performance renewable energy-powered EV applications.

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