Design of Sepic Converter as Battery Charger with Artificial Neural Network - PID Method for Load Variation
DOI:
https://doi.org/10.33019/7g1ceb77Keywords:
Artificial Neural Network, Battery Adaptive Charge, PID, Sepic ConverterAbstract
More$ adaptable$ charging me$thods are$ re$quire$d as a re$sult of the$ growing varie$ty of e$le$ctronic gadge$ts that use$ re$charge$able$ batte$rie$s brought about by te$chnological advance$me$nts. Whe$n trying to charge$ diffe$re$nt batte$ry type$s with a same$ charge$r, diffe$re$nce$s in batte$ry parame$te$rs, like$ voltage$ and capacity, pre$se$nt difficultie$s. This study sugge$sts an adaptive$ charging syste$m that can charge$ se$ve$ral batte$ry type$s using a single$ port conne$ction in orde$r to solve$ this proble$m. To transform a 24 V DC input into an output voltage$ appropriate$ for the$ particular batte$ry re$quire$me$nts, the$ syste$m use$s a SE$PIC conve$rte$r whose$ duty cycle$ is controlle$d by a microproce$ssor. The$ duty cycle$ rise$s until a spe$cific curre$nt le$ve$l is me$asure$d whe$n a batte$ry is attache$d and the$ limit switch is activate$d. The$ me$asure$d voltage$ and curre$nt data are$ the$n proce$sse$d by an Artificial Ne$ural Ne$twork (ANN) algorithm to de$te$rmine$ the$ batte$ry type$ and se$t the$ appropriate$ charging voltage$ se$tpoint. Afte$r that, a PID controlle$r is use$d to ke$e$p the$ charging conditions ste$ady. LiFe$PO4 (12.8 V, se$tpoint 14.6 V), Li-ion (14.8 V, se$tpoint 16.8 V), and Le$ad Acid (12 V, se$tpoint 14.4 V) batte$rie$s we$re$ use$d to te$st the$ syste$m. The$ syste$m succe$ssfully charge$d all thre$e$ batte$ry type$s with an ave$rage$ e$rror of 0.174%, according to e$xpe$rime$ntal findings, de$monstrating pre$cise$ and re$liable$ control pe$rformance$.
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