Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

Bhushan, Neha; Mekhilef, Saad; Soon, Tey Kok; Shaaban, Mohamed; Seyedmahmoudian, Mehdi; Stojcevski, Alex

doi:10.3390/su142315912

Cited by 8 publications

(2 citation statements)

References 137 publications

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“…It can only be estimated by measurable factors such as voltage and current. [ 5–7 ] For this reason, domestic and international researchers have proposed several methods for estimating SOC accurately. [ 8,9 ] Traditional discharge test methods, ampere–time integration methods, open‐circuit voltage methods, and internal resistance methods cannot be estimated in real‐time, resulting in large estimation errors.…”

Section: Introductionmentioning

confidence: 99%

“…The model‐based Kalman filter (KF) algorithm approximates system linearization and then optimally estimates system state based on input and output observations. [ 5,6,12–14 ] Ge et al [ 15 ] used Taylor's formula to extend the state space equations of the nonlinear system by omitting the higher‐order terms and proposed an extended KF (EKF) to solve the SOC estimation problem of the nonlinear battery system with an estimation error of no more than 2.5% based on the linear KF algorithm. However, the use of Taylor's formula to extend the nonlinear expressions in higher‐order nonlinear battery models can lead to a large number of inaccuracies.…”

Section: Introductionmentioning

confidence: 99%

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Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order—Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium‐Ion Batteries in Electric Vehicles

Zhou,

Wang,

Cao

et al. 2023

Energy Tech

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The accuracy of lithium‐ion battery state of charge (SOC) estimation affects the driving distance, battery life, and safety performance of electric vehicles. Herein, the polarization reaction inside the battery is modeled using a second‐order fractional‐order equivalent circuit model and uses an adaptive genetic algorithm for model parameter identification. Then, an improved adaptive fractional‐order backward smoothing square root cubature Kalman filtering algorithm (AFOBS‐SRCKF) is proposed by integrating Sage Husa adaptive filtering and backward smoothing processes to optimize the square root cubature Kalman filter for improving the accuracy and adaptability of real‐time estimation of SOC in a complex environment. Finally, the algorithm is compared with the integer‐order SRCKF, fractional‐order SRCKF through simulation, and fractional‐order backward smoothing SRCKF through simulation. Under complex operating conditions, the error sum of SOC estimation of the AFOBS‐SRCKF algorithm is controlled within 1.0% and the convergence speed is improved by at least 30%. The results show that the AFOBS‐SRCKF algorithm effectively improves the accuracy, stability, and convergence of SOC estimation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order—Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium‐Ion Batteries in Electric Vehicles

Zhou,

Wang,

Cao

et al. 2023

Energy Tech

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Construction of a machine-learning model to predict the optimal gene expression level for efficient production of d-lactic acid in yeast

Yamamoto¹,

Yamada²,

Matsumoto³

et al. 2023

World J Microbiol Biotechnol

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The modi cation of gene expression is being researched in the production of useful chemicals by metabolic engineering of the yeast Saccharomyces cerevisiae. When the expression levels of many metabolic enzyme genes are modi ed simultaneously, the expression ratio of these genes becomes diverse; the relationship between the gene expression ratio and chemical productivity remains unclear. In other words, it is challenging to predict phenotypes from genotypes. However, the productivity of useful chemicals can be improved if this relationship is clari ed. In this study, we aimed to construct a machinelearning model that can be used to clarify the relationship between gene expression levels and D-lactic acid productivity and predict the optimal gene expression level for e cient D-lactic acid production in yeast. A machine-learning model was constructed using data on D-lactate dehydrogenase and glycolytic genes expression (13 dimensions) and D-lactic acid productivity. The coe cient of determination of the completed machine-learning model was 0.6932 when using the training data and 0.6628 when using the test data. Using the constructed machine-learning model, we predicted the optimal gene expression level for high D-lactic acid production. We successfully constructed a machine-learning model to predict both D-lactic acid productivity and the suitable gene expression ratio for the production of D-lactic acid. The technique established in this study could be key for predicting phenotypes from genotypes, a problem faced by recent metabolic engineering strategies.

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Application of multi-core Gaussian process and embedded speech recognition in political teaching network course

Zhang,

Li,

et al. 2023

Int J Syst Assur Eng Manag

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Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

Cited by 8 publications

References 137 publications

Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order—Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium‐Ion Batteries in Electric Vehicles

Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order—Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium‐Ion Batteries in Electric Vehicles

Construction of a machine-learning model to predict the optimal gene expression level for efficient production of d-lactic acid in yeast

Application of multi-core Gaussian process and embedded speech recognition in political teaching network course

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