A sophisticated BMS often requires an accurate yet simple battery model. Simplified models such as the single particle model (SPM), however, provide limited insight because lithium concentration variations over the cell thickness dimension are often neglected. In this study, we propose a simplified electrochemical model that, by reviving the lithium concentration variation across the cell thickness, provides significantly improved prediction power over the SPM. The lithium concentration profiles are first derived according to the electrochemistry-based pseudo-two dimensions (P2D) model under the steady-state assumption, they are then relaxed into a dynamic model. By employing steady-state concentration profiles coupled with averaged dynamics, the proposed model predicts electrolyte and solid surface concentrations (c e and c ss , respectively) and potential variations across the cell during both transients and stead-state conditions. The proposed model is validated by comparing the cell voltage, c e , and c ss concentration RMS errors with respect to the P2D model under pulse and constant current inputs. The simulation shows that the proposed model achieves at least 74%, 77%, and 65% RMS error reductions for cell voltage, c ss and c e , respectively, when compared to SPM in constant current simulation. Finally, the presented model is used to predict and detect local lithium plating, which the SPM is not capable of doing; our method yields predictions similar to those of the original P2D model at computational loads comparable to SPM.