Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends

Lipu, Molla Shahadat Hossain; Hannan, M. A.; Hussain, Aini; Ayob, Afida; Saad, Mohamad Hanif Md; Karim, Tahia Fahrin; How, D. N. T.

doi:10.1016/j.jclepro.2020.124110

Cited by 193 publications

(61 citation statements)

References 92 publications

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“…Therefore, it can be concluded that this method, as a data pre-processing method, can be adapted to various neural networks. In addition, in order to validate the performance of the proposed method, three other pre-processing methods based on the average values, moving average filter, and finite impulse response were utilized to perform a comparative study [30]. The current and voltage data were processed using the wavelet transform (scale = 5) and the three mentioned pre-processing methods.…”

Section: Methods Validation and Comparison Studymentioning

confidence: 99%

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

et al. 2022

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The traditional battery state of charge (SOC) estimation method, which is based on neural networks, directly uses terminal voltage and terminal current as the input data. Although it is convenient to implement, it produces a large estimation error when the current and voltage change drastically. To solve this problem, a new method, which uses a composite multiscale wavelet transform, is proposed to estimate the battery SOC. In the proposed method, a wavelet transform is applied to the input data, and this process obtains the approximate coefficients and detail coefficients of the input data at different scales. A neural network then uses these coefficients as inputs to estimate the SOC. The experimental results show that the proposed method can improve the accuracy of the battery SOC estimation without changing the neural network structure or algorithm.

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Section: Methods Validation and Comparison Studymentioning

confidence: 99%

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

et al. 2022

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“…For the two inputs' case, the Sigmoid function is shown in Equation (7). For more than two inputs, the output equation is shown Equation (8). The input to the sigmoid neuron would be of any input value and output will be a continuous value between 0 and 1, for example, 0.4, 0.6, 0.8, and so on.…”

Section: Accuracy = Number Of Correct Predictionmentioning

confidence: 99%

“…It estimates SOC from the historical data (current, voltage, temperature, impedance), which are fed to the network, which adapts to them accordingly. Traditionally, various neural network architectures contain a different number of neurons, hidden layers, and activation function to determine the dynamic properties of a battery, which are usually composed of an input layer, hidden layers, and an output layer [8][9][10]. Charkhgard et al [11] used the combination of EKF and a neural network comprising 30 neurons in the hidden layer and Gaussian as the activation function to estimate the SOC with Root Mean Square error of <2% for Li-ion battery.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Darbar

Bhattacharya

2022

Electrochem

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Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na0.67Fe0.5Mn0.5O2 (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na+, reaching the R2 value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.

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“…Ghosh [6] presented a comprehensive analysis on the impacts of EVs towards decarbonization in terms of efficient battery management system (BMS); fast charging facility; and numerous sociotechnical challenges, incentives, benefits and government policies. Nevertheless, EV batteries have several limitations with regard to a short lifespan, battery-health degradation, aging, long charging duration, overcharging and over-discharging issues, charge and voltage unbalancing, temperature influences, thermal runaway, overheating and fire threats [12,13]. Thus, a BMS is required to improve the battery performance including efficient charging-discharging operation, temperature control, health assessment, fault diagnosis and protection.…”

Section: Introductionmentioning

confidence: 99%

Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment

et al. 2021

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Electric vehicles (EVs) have received massive consideration in the automotive industries due to their improved performance, efficiency and capability to minimize global warming and carbon emission impacts. The utilization of EVs has several potential benefits, such as increased use of renewable energy, less dependency on fossil-fuel-based power generations and energy-storage capability. Although EVs can significantly mitigate global carbon emissions, it is challenging to maintain power balance during charging on-peak hours. Thus, it mandates a comprehensive impact analysis of high-level electric vehicle penetration in utility grids. This paper investigates the impacts of large-scale EV penetration on low voltage distribution, considering the charging time, charging method and characteristics. Several charging scenarios are considered for EVs’ integration into the utility grid regarding power demand, voltage profile, power quality and system adequacy. A lookup-table-based charging approach for EVs is proposed for impact analysis, while considering a large-scale integration. It is observed that the bus voltage and line current are affected during high-level charging and discharging of the EVs. The residential grid voltage sag increases by about 1.96% to 1.77%, 2.21%, 1.96 to 1.521% and 1.93% in four EV-charging profiles, respectively. The finding of this work can be adopted in designing optimal charging/discharging of EVs to minimize the impacts on bus voltage and line current.

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Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends

Cited by 193 publications

References 92 publications

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment

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