The
            <scp>Savitzky‐Golay</scp>
            filter based bidirectional long short‐term memory network for
            <scp>SOC</scp>
            estimation

Jiao, Mingli; Wang, Dongqing

doi:10.1002/er.7055

Cited by 46 publications

(17 citation statements)

References 42 publications

(42 reference statements)

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“…With the high-speed development of the artificial intelligence technology, the data-driven based neural networks are increasingly used for SOC estimation, such as the feedforward neural network (NN), 21 the recurrent NN, 22 the improved regularized extreme learning machines, 23,24 as well as the long short-term memory (LSTM) NN or gated recurrent unit (GRU) NN. [25][26][27] These NNs do not rely on a perfect battery model, but use a large amount of data for off-line training to obtain the mapping relationship between the battery's external characteristics (voltage, current, etc.) and its SOC.…”

Section: The Neural Network-based Soc Estimation Methodsmentioning

confidence: 99%

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

Wang

Jiao

2022

Intl J of Energy Research

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Summary This paper investigates a Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network (FRCG‐MESN) to identify battery state of charge (SOC). First, an echo state network with multiple reservoirs is established to estimate battery SOC by using its discharge terminal voltage and current as the inputs, and the FR conjugate gradient algorithm is explored to tune the output weights of the MESN to avoid calculating the inverse matrix. Second, an appropriate amount of Gaussian noise with zero mean is added to the training set to prevent overfitting. Finally, a battery test platform is adopted to sample the discharging data under two working conditions: the Los Angeles 92 and the urban dynamometer driving schedule. The simulation results show that the presented FRCG‐MESN method can accurately identify the battery SOC with different initial SOCs. Applying an appropriate amount of noise to the training set can prevent overfitting efficiently.

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Section: The Neural Network-based Soc Estimation Methodsmentioning

confidence: 99%

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

Wang

Jiao

2022

Intl J of Energy Research

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“…36 Recently, the type of recurrent neural networks (RNNs) has got much attention in identifying SOC. 37,38 A long short-term memory RNN based method was studied to estimate battery SOC 39 and a Savitzky-Golay filter bidirectional LSTM based Adam algorithm was explored to estimate SOC 40 ; A gated recurrent unit RNN-based moment algorithm was explored to identify SOC. 41 Nevertheless, similar to the traditional BPNN, the RNN based methods need to train weights/biases of multi-layers.…”

Section: The Intelligent Soc Estimation Schemesmentioning

confidence: 99%

A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

Wang

2022

Intl J of Energy Research

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Summary The extreme learning machine (ELM) is a single‐hidden layer feedforword neural network (FNN) without training hidden layer weights/biases. By constructing a recurrent extreme learning machine (Recurrent‐ELM) with time delay lines to model battery dynamic characteristics, a beetle antennae search based recursive least squares (BAS‐RLS) method is explored to online realize the state of charge (SOC) estimation. The contents include: (1) To decrease the computational burden, the ELM model with fixed hidden layer weights is adopted to model battery SOC, and a RLS algorithm is studied to online estimate SOC by using the sampled terminal voltages and currents; (2) To solve the modeling accuracy problem, a Recurrent‐ELM model with past/present voltages and currents, and past SOC as inputs is constructed by adding time delay lines to capture battery dynamic characteristics, so as to promote the battery modeling accuracy; (3) To determine suitable neuron numbers in the hidden layer, the BAS method is introduced to find the optimal neuron number in the hidden layer to promote intelligence of the Recurrent‐ELM based RLS algorithm. Simulation results indicate that the proposed model and method has high precision in SOC estimation compared with traditional method.

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“…The hidden layer is made up of two unidirectional LSTM layers that have identical architecture and the same input but propagate the data in the opposite direction. The following are the vector formulas defining the forward LSTM layer for the propagation process [69].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

Laouali

Ruano

et al. 2022

Energies

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The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.

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The Savitzky‐Golay filter based bidirectional long short‐term memory network for SOC estimation

Cited by 46 publications

References 42 publications

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

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