Prediction of Li-Ion Battery State of Charge Using Multilayer Perceptron and Long Short-Term Memory Models

Khalid, Asadullah; Sundararajan, Aditya; Acharya, Ipsita

doi:10.1109/itec.2019.8790533

Cited by 48 publications

(32 citation statements)

References 13 publications

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“…In [48], the authors have introduced a novel way to reduce training time and further improve SOC estimation by using an LSTM with Transfer learning, and in [49] the authors explored the accuracy impact of using different types of loss function optimizers during model training, e.g. Adam, NAdam, Adadelta, AdaGrad, RMSProp, and AdaMax.…”

Section: ) Gated Rnns Applied To Soc Estimationmentioning

confidence: 99%

“…The LSTM structure, shown in Fig. 10b, is similar to the one used in [49] and has 27 hidden units and an input vector composed by voltage, current and temperature, = {V, I, T}. The input vector for both the FNN and LSTM was rescaled to have values between 0 and 1 before being used to train the models.…”

Section: -Use the Same Training Validation And Testing Datasets 2-mentioning

confidence: 99%

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Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

et al. 2020

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The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the states of electrified vehicle batteries. Data-driven approaches, such as ML, are becoming more popular for estimating the state of charge (SOC) and state of health (SOH) due to greater availability of battery data and improved computing power capabilities. This paper provides a survey of battery state estimation methods based on ML approaches such as feedforward neural networks (FNNs), recurrent neural networks (RNNs), support vector machines (SVM), radial basis functions (RBF), and Hamming networks. Comparisons between methods are shown in terms of data quality, inputs and outputs, test conditions, battery types, and stated accuracy to give readers a bigger picture view of the ML landscape for SOC and SOH estimation. Additionally, to provide insight into how to best approach with the comparison of different neural network structures, an FNN and long short-term memory (LSTM) RNN are trained fifty times each for 3000 epochs. The error is somewhat different for each training repetition due to the random initial values of the trainable parameters, demonstrating that it is important to train networks multiple times to achieve the best result. Furthermore, it is recommended that when performing a comparison among estimation techniques such as those presented in this review paper, the compared networks should have a similar number of learnable parameters and be trained and tested with identical data. Otherwise, it is difficult to make a general conclusion regarding the quality of a given estimation technique. INDEX TERMS Machine learning, artificial intelligence, deep learning, battery management systems (BMS), electric vehicles, state of charge, state of health.

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Section: ) Gated Rnns Applied To Soc Estimationmentioning

confidence: 99%

Section: -Use the Same Training Validation And Testing Datasets 2-mentioning

confidence: 99%

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

et al. 2020

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“…Processing (PRC): Five levels of processing exist: descriptive (statistics, exploratory visualisation, and regression between dependent and independent variables to understand events [25]), diagnostic (root-cause and correlation studies to understand why the observed events have occurred) [26,27], predictive (supervised, semi-supervised, and unsupervised regression and classification models to determine, based on the understanding from past and current events, including seasonal trends, how they will likely progress in the short-term or long-term future [28][29][30]), prescriptive (optimisation and numerical analysis to come up with robust sets of feasible actions for optimised outcomes that maximise an objective [31]), and cognitive (deep learning and decision-making to refine feasible decisions that encode human thought process, experiences, and available context to form actionable decisions [32,33]).…”

Section: Big Data Lifecycle Stagesmentioning

confidence: 99%

Adapting big data standards, maturity models to smart grid distributed generation: critical review

2020

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Big data standards and capability maturity models (CMMs) help developers build applications with reduced coupling and increased breadth of deployment. In smart grids, stakeholders currently work with data management techniques that are unique and customised to their own goals, thereby posing challenges for grid-wide integration and deployment. Although big data standards and CMMs exist for other domains, no work in the literature considers adapting them to smart grids, which will benefit from both. Further, existing smart grid standards and CMMs do not fully account for big data challenges. This study bridges the gap by analysing the role of big data in smart grids, and explores if and how big data standards and CMMs can be adapted specifically to ten distributed generation (DG) use-cases that use big data. In doing so, this work provides a useful starting point for researchers and industry members developing standards and CMM assessments for smart grid DG. 3 Emerging and existing big data standards Key big data standards are described below, their relevance to attributes and lifecycle stages summarised in Table 2.

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“…Therefore, it is necessary, to search for the usable mathematical model describing its crucial properties and dynamics, allowing for the estimation of the decreasing efficiency of the components, and the operational parameters, 19,20 such as open circuit voltage (U OC ), internal resistance (R int ), series resistance (ESR), state of charge (SOC), state of health (SOH), state of energy (SOE), or state of life (SOL). Mathematical modeling can be used for diagnosing the electrochemical cell faults and predicting the time of the cell replacement 21 . For such characteristics, many different tests were developed, for example, pseudo‐random and hybrid pulse power characterization (HPPC) tests.…”

Section: Introductionmentioning

confidence: 99%

Comparison of hybrid recurrent neural networks and dual‐polarization models of valve regulated lead acid battery

et al. 2020

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Summary Distributed energy storage using low‐cost valve regulated lead acid (VRLA) batteries becomes a promising solution, especially in prosumer microinstallations. In herein work, the model‐based research of VRLA battery in pseudo‐random and hybrid power pulse cycles has been presented. The major goal of the article was to develop and compare parametric, analytical, and data‐driven models of the battery. There were chosen the dual polarization model and the hybrid recurrent neural network model based on long short‐term memory and nonlinear autoregressive with exogenous input model for estimating voltage values and determining nonmeasurable parameters of a VRLA battery for different values of the state of charge. Satisfactory results were obtained for both methods, with the favor of the dual polarization model. Finally, the advantages and disadvantages of both methods are discussed.

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Prediction of Li-Ion Battery State of Charge Using Multilayer Perceptron and Long Short-Term Memory Models

Cited by 48 publications

References 13 publications

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

Adapting big data standards, maturity models to smart grid distributed generation: critical review

Comparison of hybrid recurrent neural networks and dual‐polarization models of valve regulated lead acid battery

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