Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy

Messing, Marvin; Rahimifard, Sara; Shoa, Tina; Habibi, Saeid

doi:10.1109/access.2021.3095938

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…Under the experimental condition of BBDST, the maximum error is À0.91%, and the absolute accuracy of estimation is improved by 2.80%. The estimation accuracy has been greatly improved compared with the method in these papers, 28,38 which verifies that F-EKF-Ah has the advantages of high accuracy and strong robustness in estimating SOC.…”

Section: Discussionmentioning

confidence: 68%

“…In this paper, a fuzzy control strategy is used to form a fusion algorithm based on the EKF and Ah integration algorithm to solve the problem of low estimation accuracy of lithium-ion batteries in the strong nonlinear interval. 38,39 Considering the complexity of physical equivalent modeling and accurate identification of the nonlinear system of lithiumion batteries, 40 based on the PNGV model and second-order RC equivalent model, an improved PNGV model of the lithium-ion batteries was established, and the forgetting factor recursive least square algorithm was used to identify model parameters online. Under experimental verification, the proposed F-Ah-EKF algorithm can achieve higher estimation accuracy in the estimation of lithium-ion batteries SOC.…”

Section: Introductionmentioning

confidence: 99%

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A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

Liu

Wang

Fan

et al. 2022

Circuit Theory & Apps

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Aiming at the problem that it is difficult to accurately estimate the state of charge (SOC) of lithium‐ion batteries in the strongly nonlinear interval, a novel algorithm based on a fuzzy control strategy is proposed. It integrates extended Kalman filter (EKF) and ampere‐hour (Ah) integration accurately estimate the SOC of lithium‐ion batteries. First, the algorithm uses the advantage that the EKF algorithm has high estimation accuracy in the nonlinear interval and can solve the problem of the large error caused by the inaccurate initial value of the Ah integral algorithm. Then the fuzzy‐EKF‐Ah (F‐EKF‐Ah) is used to fuse the two algorithms of EKF and Ah integral. The fused algorithm can effectively solve the problems of the cumulative error caused by the sampling accuracy of the Ah integral algorithm and the large estimation error of the EKF algorithm in the strong nonlinear interval. Finally, the equivalent circuit model is used for analysis. The experimental results show that the improved algorithm can achieve high estimation accuracy under three experimental conditions.

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Section: Discussionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

Liu

Wang

Fan

et al. 2022

Circuit Theory & Apps

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show abstract

“…Additionally, in this study, Coulomb counting is considered as an additional measurement to improve the estimation accuracy. The same approach has been applied to 3rd order Equivalent Circuit Model (ECM)to estimate the SOC at low temperatures [28]. EKF-SVSF is a well-known approach that uses a switching strategy between the EKF and SVSF based on a variable boundary layer.…”

Section: Introductionmentioning

confidence: 99%

State of Temperature Estimation of Li-Ion Batteries Using 3rd Order Smooth Variable Structure Filter

Ebrahimi,

Ahmed,

Habibi

2023

IEEE Access

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The Battery Management System (BMS) plays a critical role in ensuring the longevity, safety, and optimal performance of batteries by performing state of charge and health estimation, thermal management, cell balancing, and charge control. Thermal management is a crucial component that is responsible for temperature monitoring and control, managing heat generation and dissipation, preventing thermal runaway, and optimizing battery performance. This paper includes several original contributions. (1) A four-state lumped thermal model is introduced to model the core and surface temperatures of the battery.(2) Accordingly, various characterization tests were conducted on a lithium-ion Prismatic battery to log the thermal behavior of the battery. The third-order Equivalent Circuit Model (ECM) is used to calculate the generated heat inside the cell using the measured physical parameters such as voltage, and current. (3) Machine learning methods like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used and compared to determine the parameters of the thermal model. (4) A novel, reliable 3rd order Smooth Variable Structure Filter (SVSF) filter is suggested in this work and evaluated against Extended Kalman Filter (EKF), SVSF, and 2nd order SVSF. The proposed strategy demonstrated higher accuracy compared to the abovementioned filters.

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“…Online parameter estimation is commonly considered to improve the performance by adapting the model for different conditions [14]. Multiple model strategies can also increase the adaptation of a battery by considering a range of scenarios [15,16]. Once a model is chosen to determine the dynamics of the battery, a robust filter is needed to estimate the states of the battery [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…MM methods consider switching between a finite number of models to provide adaptability against changes and uncertainties. Different forms of multiple model methods have been proposed for state and parameter estimation of batteries [15,16,35]. Filter tuning methods, on the other hands, are used to adjust filter and model parameters as the system changes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

et al. 2021

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Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

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Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy

Cited by 8 publications

References 33 publications

A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

State of Temperature Estimation of Li-Ion Batteries Using 3rd Order Smooth Variable Structure Filter

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

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