Online capacity estimation of lithium-ion batteries with deep long short-term memory networks

Li, Weihan; Sengupta, Neil; Dechent, Philipp; Howey, David A.; Annaswamy, Anuradha M.; Sauer, Dirk Uwe

doi:10.1016/j.jpowsour.2020.228863

Cited by 207 publications

(55 citation statements)

References 36 publications

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“…However, the hidden layer of LSTM adopts a special memory mechanism, and the repeat module of LSTM has different structures. References 36 – 41 explained the working mechanism of the LSTM unit. The structure of the LSTM cell is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Multi-step ahead thermal warning network for energy storage system based on the core temperature detection

Dong

et al. 2021

Sci Rep

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The energy storage system is an important part of the energy system. Lithium-ion batteries have been widely used in energy storage systems because of their high energy density and long life. However, the temperature is still the key factor hindering the further development of lithium-ion battery energy storage systems. Both low temperature and high temperature will reduce the life and safety of lithium-ion batteries. In actual operation, the core temperature and the surface temperature of the lithium-ion battery energy storage system may have a large temperature difference. However, only the surface temperature of the lithium-ion battery energy storage system can be easily measured. The estimation method of the core temperature, which can better reflect the operation condition of the lithium-ion battery energy storage system, has not been commercialized. To secure the thermal safety of the energy storage system, a multi-step ahead thermal warning network for the energy storage system based on the core temperature detection is developed in this paper. The thermal warning network utilizes the measurement difference and an integrated long and short-term memory network to process the input time series. This thermal early warning network takes the core temperature of the energy storage system as the judgment criterion of early warning and can provide a warning signal in multi-step in advance. This detection network can use real-time measurement to predict whether the core temperature of the lithium-ion battery energy storage system will reach a critical value in the following time window. And the output of the established warning network model directly determines whether or not an early emergency signal should be sent out. In the end, the accuracy and effectiveness of the model are verified by numerous testing.

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Section: Methodsmentioning

confidence: 99%

Multi-step ahead thermal warning network for energy storage system based on the core temperature detection

Dong

et al. 2021

Sci Rep

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“…Battery states can be derived quite precisely using deep networks. 6 , 7 In the focused work, 1 Adam’s algorithm is used to train the voltage sampling points to restore the entire charging curve by a developed DNN. The estimated curve is then used to predict the maximum capacity with around 4% maximum absolute estimation error and can also be used to construct an incremental capacity (IC) curve for battery-state estimation.…”

Section: Main Textmentioning

confidence: 99%

A quick battery charging curve prediction by artificial neural network

Hosen

2021

Patterns

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“…On the other hand, long short‐term memory (LSTM)‐RNN is another class of RNN that could solve some drawbacks of simple RNN, such as vanishing and exploding gradient 25 . Li et al implemented an architecture using LSTM‐based time series processing, which allows the input charging curves to be variable in time steps and prediction to be attained even with incomplete sensor data 25 . Although, LSTM's configuration is complex and is composed of three gates, including the output gate, input gate, and forget gate.…”

Section: Introductionmentioning

confidence: 99%

Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach

Ardeshiri

2021

Int J Energy Res

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Summary This paper proposes the gated recurrent unit (GRU)‐recurrent neural network (RNN), a deep learning approach to predict the remaining useful life (RUL) of lithium‐ion batteries (LIBs), accurately. The GRU‐RNN structure can self‐learn the network parameters utilizing adaptive gradient descent algorithms, leading to a reduced computational cost. Unlike the long short‐term memory (LSTM) model, GRU‐RNN allows time‐series dependencies to be tracked between degraded capacities without using any memory cell. This enables the method to predict non‐linear capacity degradations and build an explicitly capacity‐oriented RUL predictor. Additionally, feature selection based on the random forest technique was used to enhance the prediction precision. The analyses were conducted based on four separate cycling life testing datasets of a lithium‐ion battery. The experimental results indicate that the average percentage of root mean square error for the proposed method is about 2% which respectively is 1.34 times and 8.32 times superior to the LSTM and support vector machine methods. The outcome of this work can be used for managing the Li‐ion battery's improvement and optimization.

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Online capacity estimation of lithium-ion batteries with deep long short-term memory networks

Cited by 207 publications

References 36 publications

Multi-step ahead thermal warning network for energy storage system based on the core temperature detection

Multi-step ahead thermal warning network for energy storage system based on the core temperature detection

A quick battery charging curve prediction by artificial neural network

Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach

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