An Extension of the Kinetic Battery Model for Optimal Control Applications

Abstract

Optimal control of electric vehicle (EV) batteries for maximal energy efficiency, safety and lifespan requires that the Battery Management System (BMS) has accurate realtime information on both the battery State-of-Charge (SoC) and its dynamics, i.e. energy supply capacity, at all times. However, these quantities cannot be measured directly from the battery, and, in practice, only SoC estimation is typically carried out. Moreover, the so-called Equivalent Circuit Models (ECM) commonly utilized in BMS solutions only display a memoryless algebraic dependence of voltage and current on SoC, without an ability to predict battery energy supply capacity based on its recent charge/discharge history. In this article, we propose a novel parametric algebraic voltage model coupled to the well-known Manwell-McGowan dynamic Kinetic Battery Model (KiBaM), which is able to predict both battery SoC dynamics and its electrical response. We present an offline model parameter identification procedure that yields SoC-dependent model parameters from standard dynamic battery tests, and we introduce an algorithm based on the Extended Kalman Filter (EKF) for standard SoC estimation on the proposed model. Numerical simulations, based on laboratory measurements, are presented for prismatic Lithium-Titanate Oxide (LTO) battery cells. Such cells are prime candidates for modern heavy offroad EV applications.