Topic
State of charge
About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.
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23 Jan 2000
TL;DR: In this paper, a review of the battery models used for electric vehicles and battery energy storage system applications is presented, which takes into account the nonlinear characteristics of battery with respect to the battery's state of charge.
Abstract: This paper initially presents a review of the several battery models used for electric vehicles and battery energy storage system applications. A model is discussed which takes into account the nonlinear characteristics of the battery with respect to the battery's state of charge. Comparisons between simulation and laboratory measurements are presented. The effects of high frequency switching on the battery performance are also discussed. A strategy to reduce the high frequency charging and discharging current is proposed.
191 citations
TL;DR: In this paper, a particle filter for state estimation of A123 lithium-iron phosphate batteries is presented. But the state of health estimation of the battery is not considered in this paper.
190 citations
TL;DR: A novel SOH estimation method by using a prior knowledge-based neural network (PKNN) and the Markov chain for a single LIB and the maximum estimation error of the SOH is reduced to less than 1.7% by adopting the proposed method.
Abstract: The state of health (SOH) of lithium-ion batteries (LIBs) is a critical parameter of the battery management system. Because of the complex internal electrochemical properties of LIBs and uncertain external working environment, it is difficult to achieve an accurate SOH determination. In this paper, we have proposed a novel SOH estimation method by using a prior knowledge-based neural network (PKNN) and the Markov chain for a single LIB. First, we extract multiple features to capture the battery aging process. Due to its effective fitting ability for complex nonlinear problems, the neural network with a prior knowledge-based optimization strategy is adopted for the battery SOH prediction. The Markov chain, with the advantageous prediction performance for the long-term system, is established to modify the PKNN estimation results based on the prediction error. Experimental results show that the maximum estimation error of the SOH is reduced to less than 1.7% by adopting the proposed method. By comparing with the group method of data handling and the back-propagation neural network in conjunction with the Levenberg–Marquardt algorithm, the proposed estimation method obtains the highest SOH accuracy.
189 citations
TL;DR: In this paper, an adaptive switching gain sliding mode observer (ASGSMO) was proposed for battery state of charge estimation in electric vehicles (EVs) based on a battery equivalent circuit model (BECM).
186 citations
TL;DR: In this paper, a rapid calibration procedure for identifying the parameters of a dynamic model of batteries for use in automotive applications is described, which is a phenomenological model based on an equivalent circuit model with varying parameters that are linear spline functions of the SoC.
185 citations