Battery State Estimator Based on a Finite Impulse Response Filter

Abstract

We have developed a battery state estimator based on a finite impulse response filter. Simulation results indicate that the estimator gives accurate prediction and numerically-stable performance in the regression of filter coefficients and the open-circuit potential, which yields the battery state of charge. The estimator is also able to predict battery power capabilities. Comparison of the measured and predicted state of charge (SOC) and the charge and discharge power capabilities (state of power, SOP) of a Li-ion battery are provided. Predictions for the SOC and SOP agree well with experimental measurements, demonstrating the estimator's application in battery management systems. In particular, this new approach appears to be more flexible than previous models; we show that it can capture the behavior of batteries governed by various physicochemical phenomena.In many battery-powered systems, such as electric vehicles (EV) and hybrid electric vehicles (HEV), the performance of the electric traction system can be greatly enhanced by intelligent management of the electrochemical energy storage device. An adaptive battery state estimator (BSE) can provide accurate and timely estimation of battery states: the state of charge (SOC), the charge and the discharge power capabilities (SOP), and the state of health (SOH). 1 Battery modeling for BSE application is challenging because, particularly at high rates of operations, the battery system is nonlinear. In addition, the system is non-stationary in the sense that the battery is aging. Last, for vehicle applications where cost of controllers is important, the model for the BSE must be fast in terms of computation and storage requirements must be minimal. Hence, BSEs are usually based on a zero-dimensional equivalent circuit. Actual batteries have concentration, current, potential, and temperature distributions. Various battery models have been approximated within the framework of a BSE. 2-17 Coulomb counting and rule-based BSEs are the most rudimentary and are still used in many battery powered devices due to simplicity, low cost, and minimal computing demands. 3 The Coulomb counting technique uses the integration of current over time to estimate the battery SOC. The main drawback of this method is its susceptibility to large errors in long-term usage, as it is not adaptive. Rule-based battery controls are used to restrict the range of battery power and voltage in an attempt to ensure battery abuse tolerance and lifetime. However, these rules ignore the battery usage history and aging, and are therefore less reliable. 4 In contrast, advanced battery control and integration requires accurate battery SOC and SOP at any given moment."Black-box" type battery models (machine learning) 5-8 can be based on neural network and machine learning techniques that train the models with the test data of the battery. Despite some success in describing the capacities of lead-acid, nickel-metal hydride, 5 and recently lithium ion batteries, 6 these approaches are computationally intensiv...

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