In this work, we have introduced equivariant features (P3 and P5) into the second-generation of PiNet. PiNet2-P3 shows a significant and cost-effective improvement on energy and force predictions across different types of datasets ranging from small molecules to liquid electrolytes, as compared to PiNet. The equivariant features turn out to also significantly improve the dipole and polarizability predictions, as demonstrated by the upgraded PiNet2-dipole and PiNet2-χ. Besides benchmarking standard datasets, we have applied the new PiNN code to two realistic electrochemical systems: protic ionic liquids and electrified graphene oxide/electrolyte interfaces by leveraging two plug-ins: PiNNAcLe for an adaptive learn-onthe-fly workflow and PiNNwall for molecular simulations of electrode/electrolyte interfaces respectively. Together, these build up a versatile and high-performing ML-accelerated simulation framework for future development and application in electrochemical systems.