xEV Li-Ion Battery Low-Temperature Effects—Review

Vidal, Carlos; Groß, Oliver; Gu, Ran; Kollmeyer, Phillip J.; Emadi, Ali

doi:10.1109/tvt.2019.2906487

Cited by 119 publications

(44 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4, where the inputs are the battery terminal voltage V (k), battery temperature T (k), averaged terminal voltage V avg , averaged current I avg , and the output is SOC at timestep k. The best result at 25 • C was obtained when a rolling window of 400 timesteps was used. As expected, the errors are much higher at −20 • C due to the low temperature effects on the li-ion batteries [16]. It was conjectured that it might be improved if a larger dataset was used and a more complex FNN was built to capture all the complexity presented at low temperatures.…”

Section: ) Fnn Directly Used For Soc Estimationmentioning

confidence: 67%

“…Because different sources influence the Li-ion batteries' characteristics, e.g., SOC, SOH, current, and temperature [16], the authors in [17], have considered the use of the battery polarization states as inputs for an FNN model trained to estimate battery SOC. The polarization states can have a significant influence on the battery terminal voltage [18] and can be calculated from the battery current using equation 3, where S k is the polarization state at sample point k, t i the sampling interval, N the number of parts within the interval t i , i L,k the battery current, and τ the time constant that can have any integer value from 0 to 1000.…”

Section: ) Fnn Directly Used For Soc Estimationmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: ) Fnn Directly Used For Soc Estimationmentioning

confidence: 67%

Section: ) Fnn Directly Used For Soc Estimationmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other advantages include high energy efficiency, no memory effects and a comparably long cycle life. The one major shortcoming of these batteries is the cost which is significantly more than the other two [ 21 ].…”

Section: Uav Power Sourcesmentioning

confidence: 99%

A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements

Townsend

Jiya

Martinson

et al. 2020

Heliyon

View full text Add to dashboard Cite

“…It is critical because the temperature below the freezing point is harmful to the Li‐ion battery. The battery capacity fade [7], the lithium plating form [8], the solid‐electrolyte interface (SEI) layer growth, the internal resistance increase [9] are listed as main challenges limiting the battery voltage in the cold weather. Fortunately, the cell core is self‐heated, thanks to the Joule effect on its internal resistance.…”

Section: Introductionmentioning

confidence: 99%

Effect of battery voltage variation on electric vehicle performance driven by induction machine with optimal flux‐weakening strategy

Nguyen

Nguyễn

Trovão

et al. 2020

IET Electrical Systems in Transportation

View full text Add to dashboard Cite

xEV Li-Ion Battery Low-Temperature Effects—Review

Cited by 119 publications

References 76 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

A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements

Effect of battery voltage variation on electric vehicle performance driven by induction machine with optimal flux‐weakening strategy

Contact Info

Product

Resources

About