Hybrid VARMA and LSTM Method for Lithium-ion Battery State-of-Charge and Output Voltage Forecasting in Electric Motorcycle Applications

Caliwag, Angela C.; Lim, Wansu

doi:10.1109/access.2019.2914188

Cited by 46 publications

(19 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A combination of vector autoregressive moving average (VARMA) and an LSTM was introduced in [47] to forecast li-ion battery voltage and SOC of an electric motorcycle where a different combination of inputs, including motor speed, input power and torque, and battery voltage, current, and temperatur were evaluated. The authors have tested the model at 0 • C and 25 • C using only CVS-40, a South Korean driving cycle, and the data used to train the model were obtained directly from driving the motorcycle.…”

Section: ) Gated Rnns Applied To 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

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: ) Gated Rnns Applied To 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

View full text Add to dashboard Cite

show abstract

“…As a new machine learning technology, the LSTM neural network uses the traditional recurrent neural network to address the problem of the exploding gradient and vanishing gradient [37]. The LSTM cell nucleus is used to replace the cell nucleus of the traditional dynamic neural network; therefore, it has long-term memory capabilities.…”

Section: B Lstm Neural Networkmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Wei

Min

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Highly accurate state of charge (SOC) estimation of lithium-ion batteries is one of the key technologies of battery management systems in electric vehicles. The performance of SOC estimation directly influences the driving range and safety of these vehicles. Due to external disturbances, temperature variation and electromagnetic interference, accurate SOC estimation becomes difficult. To accurately estimate the SOC of lithium-ion batteries, this paper presents a novel machine-learning method to address the risk of gradient explosion and gradient decent using the dynamic nonlinear auto-regressive models with exogenous input neural network (NARX) with long short-term memories (LSTM).The proposed hybrid NARX model embeds LSTM memory, which provides jump-ahead connections in the time-unfolded model. These jump-ahead connections provide a shorter path for the propagation of gradient information, therefore reducing long-term dependence on the recurrent neural network. Experimental results show that the estimation performance root mean square error (RMSE) of the proposed model is less than 1%, and this model has better multitime prediction performance. Finally, the hybrid NARX and LSTM model is compared with the standard back propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) and LSTM existing models under urban dynamometer driving schedule (UDDS) and dynamic stress test (DST) conditions. The proposed hybrid NARX-LSTM model yield relative to other methods and can estimate the battery SOC with high accuracy. The RMSE of proposed model is improved by approximately 60% compared with the standard LSTM INDEX TERMS electric vehicles; state of charge; lithium-ion batteries; NARX; LSTM

show abstract

“…In References 10–14, LSTM (long short‐term memory) algorithms are used to predict the RUL and SOH of the battery. Hybrid versions of LSTM are also presented in References 9 and 15. LSTM fusion with algorithms such as VARMA or Elman neural networks leads to an increase of LSTM prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

2020

View full text Add to dashboard Cite

Summary Online state of health (SOH) prediction of lithium‐ion batteries remains a very important problem in assessing the safety and reliability of battery‐powered systems. Deep learning techniques based on recurrent neural networks with memory, such as the long short‐term memory (LSTM) and gated recurrent unit (GRU), have very promising advantages, when compared to other SOH estimation algorithms. This work addresses the battery SOH prediction based on GRU. A complete BMS is presented along with the internal structure and configuration parameters. The neural network was highly optimized by adaptive moment estimation (Adam) algorithm. Experimental data show very good estimation results for different temperature values, not only at room value. Comparisons performed against other relevant estimation methods highlight the performance of the recursive neural network algorithms such as GRU and LSTM, with the exception of the battery regeneration points. Compared to LSTM, the GRU algorithm gives slightly higher estimation errors, but within similar prediction error range, while needing significantly fewer parameters (about 25% fewer), thus making it a very suitable candidate for embedded implementations.

show abstract

Hybrid VARMA and LSTM Method for Lithium-ion Battery State-of-Charge and Output Voltage Forecasting in Electric Motorcycle Applications

Cited by 46 publications

References 22 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

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

Contact Info

Product

Resources

About