It sounds like you already know the batch effect? If that is the case you should not use RUVg (or SVA) instead:
For DE: You simply add the batch effect as a factor in the DE model - then it will be ignored when you test for the difference caused by your experimental conditions.
For EDA: you need to remove the batch effect and RUV, SVA::combat or limma::removeBatchEffects can all help you with this. All methods work on Kallisto data (although each need different input) and both on gene and transcript level.
Btw if you are interested in transcript analysis you can, with the data you already have, directly analyse fx isoform switches - something my R package IsoformSwitchAnalyzeR can help you with (and it also handles batch effects). You can find examples of what type of analysis you can do in this section of the vignette.