State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach

Tian, Jinpeng; Xiong, Rui; Shen, Weixiang; Lu, Jiahuan

doi:10.1016/j.apenergy.2021.116812

Cited by 214 publications

(67 citation statements)

References 41 publications

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“…Table 1 lists the components and specifications of the electric AWD tractor. The battery type was LiFePO 4 , which has a long cycle and can adequately cope with the high power output of the electric motor during operation [ 30 ]. The total battery capacity was 58.4 kWh, and the rated voltage and discharge rate were 70.4 V and 2 C (30 min.…”

Section: Methodsmentioning

confidence: 99%

Traction Performance Evaluation of the Electric All-Wheel-Drive Tractor

Baek

Jeon

et al. 2022

Sensors

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This study aims to design, develop, and evaluate the traction performance of an electric all-wheel-drive (AWD) tractor based on the power transmission and electric systems. The power transmission system includes the electric motor, helical gear reducer, planetary gear reducer, and tires. The electric system consists of a battery pack and charging system. An engine-generator and charger are installed to supply electric energy in emergency situations. The load measurement system consists of analog (current) and digital (battery voltage and rotational speed of the electric motor) components using a controller area network (CAN) bus. A traction test of the electric AWD tractor was performed towing a test vehicle. The output torques of the tractor motors during the traction test were calculated using the current and torque curves provided by the motor manufacturer. The agricultural work performance is verified by comparing the torque and rpm (T–N) curve of the motor with the reduction ratio applied. The traction is calculated using torque and specifications of the wheel, and traction performance is evaluated using tractive efficiency (TE) and dynamic ratio (DR). The results suggest a direction for the improvement of the electric drive system in agricultural research by comparison with the conventional tractor through the analysis of the agricultural performance and traction performance of the electric AWD tractor.

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Section: Methodsmentioning

confidence: 99%

Traction Performance Evaluation of the Electric All-Wheel-Drive Tractor

Baek

Jeon

et al. 2022

Sensors

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“…Because of the strong nonlinear processing ability of neural networks, many researchers have attempted to estimate the battery SOC using such networks. For example, Chen et al employed a recurrent neural network to estimate the battery SOC [14], and Tian et al utilized a deep neural network to identify the open-circuit voltage of the battery and estimate the SOC [15]. In addition, Zhang et al used a radial basis function (RBF) neural network for battery SOC estimation [16].…”

Section: Introductionmentioning

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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“…In addition, to obtain a reliable battery model, the SOC estimation also requires a high precision algorithm. Recently, effective estimation methods have been presented, such as the open circuit voltage (OCV) method [6], amperehour integration method [7,8], Kalman filter algorithm, neural network method [9,10], sliding mode observer [11,12], H ∞ filter [13,14], adaptive particle filter [15] and others. Each of these methods presents advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter

Chen

Lopes

et al. 2021

Fractal Fract

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The covariance matrix of measurement noise is fixed in the Kalman filter algorithm. However, in the process of battery operation, the measurement noise is affected by different charging and discharging conditions and the external environment. Consequently, obtaining the noise statistical characteristics is difficult, which affects the accuracy of the Kalman filter algorithm. In order to improve the estimation accuracy of the state of charge (SOC) of lithium-ion batteries under actual working conditions, a fuzzy fractional-order unscented Kalman filter (FFUKF) is proposed. The algorithm combines fuzzy inference with fractional-order unscented Kalman filter (FUKF) to infer the measurement noise in real time and take advantage of fractional calculus in describing the dynamic behavior of the lithium batteries. The accuracy of the SOC estimation under different working conditions at three different temperatures is verified. The results show that the accuracy of the proposed algorithm is superior to those of the FUKF and extended Kalman filter (EKF) algorithms.

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State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach

Cited by 214 publications

References 41 publications

Traction Performance Evaluation of the Electric All-Wheel-Drive Tractor

Traction Performance Evaluation of the Electric All-Wheel-Drive Tractor

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter

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