Electrochemical Model-Based State of Charge Estimation for Li-Ion Cells

Corno, Matteo; Bhatt, Nimitt; Savaresi, Sergio M.; Verhaegen, Michel

doi:10.1109/tcst.2014.2314333

Cited by 121 publications

(51 citation statements)

References 30 publications

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“…The approaches presented in [89][90][91][92] specifically develop EChMs for SoC estimation. Zou et al in [89] propose a model composed of four submodels that capture the electrochemical, thermal, electrical, and aging dynamics in a set of partial differential equations.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

“…Barlett et al in [90] present a reduced-order electrochemical model for different Li ion chemistries that are used in dual-nonlinear observers (KF, fixed interval Kalman smoother and particle filter), to estimate the cell SoC and loss of cyclable lithium over time. The research presented in [91] described an EChM where a set of discretized equations are used to estimate the SoC directly, and then SoC is compared with an approach where the EChM is written as a linearly spatially interconnected system. Here, by exploiting the resulting semi-separable structure, the method uses an EKF to optimize the calculations for the SoC estimation.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

“…In [100] a KF is introduced to extract an OCV value in a noisy environment, and then to estimate SoC based on the OCV-SoC mapping. As the KF has the observability of the system as a requirement, a virtual-measurement-based method can be implemented to account for a local loss of observability, like is presented in [91]. This approach uses the iterative process of the KF described in Figure 8 for estimating not only SoC but also other battery parameters, like the KF implemented in [96] estimates both SoC and SoH.…”

Section: Real Batterymentioning

confidence: 99%

“…Some good examples of how to apply and run an EKF algorithm (as depicted in Figure 9) for SoC estimation are presented in [7,21,91,102]. Due to the electrochemical and thermal models of the batteries being highly nonlinear, EKF is used for estimating its parameters, followed by SoC [45,80,90].…”

Section: Real Batterymentioning

confidence: 99%

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SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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Section: Model-based Estimation Methodsmentioning

confidence: 99%

Section: Model-based Estimation Methodsmentioning

confidence: 99%

Section: Real Batterymentioning

confidence: 99%

Section: Real Batterymentioning

confidence: 99%

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SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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“…Currently, four main equivalent circuit models, i.e., the Rint model, Thevenin model, PNGV model, and a multi-order RC circuit model, are widely used in electric vehicle simulation [9][10][11][12][13].…”

Section: Building the Battery Modelmentioning

confidence: 99%

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

et al. 2016

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An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.

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State‐of‐Power Estimation of Li‐Ion Batteries Considering the Battery Surface Temperature

Liu

Zeng

et al. 2018

Energy Tech

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The state‐of‐power (SOP) of a Li‐ion battery (LIB) is an important evaluation index for safety control and energy recovery in electric vehicles. Major state estimation methods are only applicable to fixed room temperature at 20 °C, although the battery capacity and resistance vary dramatically with a change in battery surface temperature in the working process of the battery. This causes significant errors in state estimation if only room temperature is assumed. The inaccurate state may cause further excessive current at high or low temperatures to affect safety and life cycle of the battery. Therefore, a novel state estimation method applicable to various battery surface temperatures is developed here. This method uses capacity–temperature and resistance–temperature relationships from experimental data to more accurately predict battery capacity and resistance over the full temperature range. It then applies the extended Kalman filter (EKF) technique to estimate the state‐of‐charge, and a method of constrained multi‐parameter algorithm (CMPA) to estimate the SOP of LIBs. Simulation and experimental results show that the proposed method can obtain accurate SOP results at different temperatures.

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Electrochemical Model-Based State of Charge Estimation for Li-Ion Cells

Cited by 121 publications

References 30 publications

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

State‐of‐Power Estimation of Li‐Ion Batteries Considering the Battery Surface Temperature

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