<p>Accurate atomistic modeling of ion solvation structures is crucial for next-generation battery design but remains challenging due to complex many-body polarization. Conventional fixed-charge force fields often fail to capture environment-dependent electronic responses. Here, we introduce a transferable parameterization workflow based on the Drude oscillator methodology to develop OPLS&amp;Pol, an open-source polarizable force field for battery electrolytes. Validated across diverse neat and multicomponent systems, OPLS&amp;Pol achieves predictive accuracy comparable to state-of-the-art machine learning potentials and a commercial polarizable force field. Specifically, it yields a global coefficient of determination (<i>R</i>²_global) of 0.94 for complex solvation motifs while maintaining a computational throughput up to 1.65 million atom steps per second, nearly an order of magnitude faster than typical machine learning models. This systematic workflow successfully identifies a coherent set of mechanisms across chemically distinct electrolytes: it reveals polarization-driven reorganization of ion solvation structures across concentration regimes, resolves competitive coordination in mixed ethers, elucidates energetic drivers of solvation preferences in cyclic and linear carbonates, and captures polarization-stabilized ion aggregation in fluorosulfonyl-based electrolytes while recovering physically realistic transport dynamics. These findings establish the Drude-based parameterization workflow and the resulting OPLS&amp;Pol model as a physically rigorous, computationally efficient foundation for predictive electrolyte modeling across a vast chemical space.</p>