Abstract
An accurate state of charge (SOC) estimation depends on an accurate battery model. The influence of nonlinear and unstable interference factors makes the accurate SOC estimation difficult. To obtain an accurate battery model, a method based on the NARX (nonlinear autoregressive network with exogenous inputs) recurrent neural network and moving window method is proposed. This paper improves the accuracy, modelling speed and robustness of SOC estimation from the following three aspects. First, to overcome the excessive reliance on the amount of data in the model training process, the NARX recurrent neural network is used to establish the battery model. NARX (nonlinear autoregressive with external input) recurrent neural network with the delay and feedback functions can keep the input and output of a previous moment and add it to the calculation of the next moment. Therefore, better estimation results are achieved using a small amount of data; second, the moving window method is used against the gradient explosion and the gradient vanishing that may occur in the NARX model training process. Third, by comparing it with other methods under different working conditions and different temperatures, the validity of the proposed model is verified. The results indicate that the proposed model has a higher accuracy and speed of the SOC estimation. The RMSE performance of the proposed model is reduced by approximately 65%, and the execution time is shortened by approximately 50%.
Highlights
With the depletion of global fossil energy, traction batteries are considered a promising choice for electric vehicles
By analysing the estimation results of Model (1-3) under constant current and constant voltage (CCCV) conditions, we can see that Model-2 has a larger error: the maximum error and the root mean square error (RMSE) are 0.023, 0.53%, respectively, and the execution time is longer: 2.68775 s; the execution time of Model-3 is 1.24973 s, the error of the estimation result is larger: the maximum error and the RMSE is 0.7 and 0.67%, respectively
To overcome the problem of gradient exploding and gradient vanishing of traditional NARX recurrent neural networks, a model based on NARX and the moving window method is proposed to improve the estimation accuracy
Summary
With the depletion of global fossil energy, traction batteries are considered a promising choice for electric vehicles. Compared with other types of batteries, traction batteries have the advantages of high energy density, no memory effect, long cycle life, and low self-discharge rate [1]. In the existing batteries technology, a reliable battery management system (BMS) is still a design problem. SOC is one of the basic technology of BMS which is the prerequisite for preventing battery overcharging and over discharging [2]. Due to the emergency braking, sudden temperature changes and external interference phenomenon, accurate SOC estimation. The associate editor coordinating the review of this manuscript and approving it for publication was Cheng Chin. Accurate SOC estimation is of great significance
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