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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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors investigated the behavior and state-of-health monitoring of lithium-ion batteries and used recurrent neural networks (RNNs) to predict the degradation in battery performance.

330 citations

Proceedings ArticleDOI
04 Jun 1997
TL;DR: In this paper, a rule-based control and energy management strategy for a series hybrid vehicle is presented, which is based on splitting the power demand between the engine and the battery such that these power sources are operated at high efficiency.
Abstract: A rule-based control and energy management strategy for a series hybrid vehicle is presented. The strategy is based on splitting the power demand between the engine and the battery such that these power sources are operated at high efficiency. The power demand is estimated as the output of a high gain PI controller that controls the longitudinal acceleration of the vehicle. The focus was to improve the fuel economy of the vehicle by suitable power assignment to the power sources. This power split (assignment) is implemented under a rule-base frame. The rules depend on the values of selected variables: the power demand itself, the driver's acceleration command and the status of the SOC (state of charge) of the battery. The rules ensure that the engine and the battery operate at high efficiency whenever possible. At high power demand the engine will operate at its maximum rated power. Simulation results of the proposed strategy showed improvement in fuel economy over the "thermostat" strategy. An improvement of 11% in the urban cycle and of 6% in the highway cycle have been achieved for a series hybrid vehicle driven by a 40 KW diesel engine and a 60 kW lead acid battery.

329 citations

Journal ArticleDOI
TL;DR: This paper initially discusses battery and UC characteristics and then goes on to provide a detailed comparison of various proposed control strategies and proposes the use of precise power electronic converter topologies, which are summarized and suggested for on-board power management.
Abstract: Batteries, ultracapacitors (UCs), and fuel cells are widely being proposed for electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) as an electric power source or an energy storage unit. In general, the design of an intelligent control strategy for coordinated power distribution is a critical issue for UC-supported PHEV power systems. Implementation of several control methods has been presented in the past, with the goal of improving battery life and overall vehicle efficiency. It is clear that the control objectives vary with respect to vehicle velocity, power demand, and state of charge of both the batteries and UCs. Hence, an optimal control strategy design is the most critical aspect of an all-electric/plug-in hybrid electric vehicle operational characteristic. Although much effort has been made to improve the life of PHEV energy storage systems (ESSs), including research on energy storage device chemistries, this paper, on the contrary, highlights the fact that the fundamental problem lies within the design of power-electronics-based energy-management converters and the development of smarter control algorithms. This paper initially discusses battery and UC characteristics and then goes on to provide a detailed comparison of various proposed control strategies and proposes the use of precise power electronic converter topologies. Finally, this paper summarizes the benefits of the various techniques and suggests the most viable solutions for on-board power management, more specific to PHEVs with multiple/hybrid ESSs.

329 citations

Journal ArticleDOI
TL;DR: A linear Kalman filter based on a reduced order electrochemical model is designed to estimate internal battery potentials, concentration gradients, and state-of-charge (SOC) from external current and voltage measurements, providing performance in the 30%-70% SOC range except in the case of severe current pulses.
Abstract: High-power lithium ion batteries are often rated with multiple current and voltage limits depending on the duration of the pulse event. These variable control limits, however, are difficult to realize in practice. In this paper, a linear Kalman filter based on a reduced order electrochemical model is designed to estimate internal battery potentials, concentration gradients, and state-of-charge (SOC) from external current and voltage measurements. A reference current governor predicts the operating margin with respect to electrode side reactions and surface depletion/saturation conditions responsible for damage and sudden loss of power. The estimates are compared with results from an experimentally validated, 1-D, nonlinear finite volume model of a 6 Ah hybrid electric vehicle battery. The linear filter provides, to within ~ 2%, performance in the 30%-70% SOC range except in the case of severe current pulses that draw electrode surface concentrations to near saturation and depletion, although the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low-order comparable to empirical equivalent circuit models commonly employed and described in the literature. Accurate estimation of the battery's internal electrochemical state enables an expanded range of pulse operation.

328 citations

Proceedings ArticleDOI
15 Jun 2008
TL;DR: In this article, a battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead acid I-V characteristics with minimal model alterations is proposed, and a battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration.
Abstract: Simulation of electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles over driving schedules within a full dynamic hybrid and electric vehicle simulator requires battery models capable of predicting state-of-charge, I-V characteristics, and dynamic behavior of different battery types. A battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead- acid I-V characteristics (with minimal model alterations) is proposed. A battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration. A multiple time-constant battery model was used for modeling lithium- ion batteries; verification of time constants in the seconds to minutes and hour ranges has been shown in numerous research articles and a time constant in the millisecond range is verified here with experiments. Lack of significant time constants in the millisecond range is validated through direct testing. A modeled capacity-rate effect within the state-of-charge determination portion of the proposed model is verified experimentally to ensure accurate prediction of battery state of charge after lengthy driving schedules. The battery model was programmed into a Matlab/Simulink environment and used as a power source for plug-in hybrid electric vehicle simulations. Results from simulations of lithium-ion battery packs show that the proposed battery model behaves well with the other subcomponents of the vehicle simulator; accuracy of the model and prediction of battery internal losses depends on the extent of tests performed on the battery used for the simulation. Extraction of model parameters and their dependence on temperature and cycle number is ongoing, as well as validation of the Simulink model with hardware- in-the-loop "driving schedule" cycling of real batteries.

328 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023692
20221,326
2021926
20201,245
20191,285
20181,147