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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TL;DR: In this paper, a joint estimator based on particle filter is proposed for the estimation of both the state-of-energy (SOE) and the integral voltage of Li-ion batteries.
177 citations
TL;DR: In this paper, the authors investigated the overcharge-induced capacity fading behavior of large format lithium-ion batteries with Li y Ni 1/3 Co 1/1/3 Mn 1/2 O 2 O 2 + Li y Mn 2 O 4 composite cathode.
176 citations
TL;DR: Simulation results of the proposed predictive strategy show substantial improvements in fuel economy in hilly driving profiles, compared with nonpredictive strategies.
Abstract: A novel model-based and predictive energy supervisory controller for hybrid electric vehicles (HEVs) is presented. Its objective is to minimize the fuel consumption (FC) of HEVs using only the information on the current state of charge (SoC) of the battery and data available from a standard onboard navigation system. This objective is achieved using a predictive reference signal generator (pRSG) in combination with a nonpredictive reference tracking controller for the battery SoC. The pRSG computes the desired battery SoC trajectory as a function of vehicle position such that the recuperated energy is maximized despite the constraints on the battery SoC. To compute the SoC reference trajectory, only the topographic profile of the future road segments and the corresponding average traveling speeds must be known. Simulation results of the proposed predictive strategy show substantial improvements in fuel economy in hilly driving profiles, compared with nonpredictive strategies. A parallel HEV is analyzed in this paper. However, the proposed method is independent of the powertrain topology. Therefore, the method is applicable to all types of HEVs.
176 citations
TL;DR: In this article, a real-time energy management strategy (EMS) is proposed for a dual-mode power-split hybrid electric vehicle in order to improve the fuel economy and maintain proper battery state of charge (SOC) while satisfying all the constraints and the driving demands.
176 citations
TL;DR: Experimental results illustrate that proposed ETNN-UKF can rapidly eliminate initial errors and provide satisfactory co-estimation performance, and a neural network is incorporated to enhance the performance of sub-models.
176 citations