Abstract
State of charge (SOC) estimation is becoming increasingly important, along with electric vehicle (EV) rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN) model combining the discrete wavelet transform (DWT) method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M) algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN), L-M based BPNN (LMBPNN), L-M based WNN (LMWNN), DWT with L-M based BPNN (DWTLMBPNN) and extend Kalman filter (EKF), the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC), the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test).
Highlights
The battery management system (BMS) is responsible for monitoring power batteries’ complete information to guarantee the electric vehicle (EV) performance, because the batteries’ parameters such as current, voltage, resistance, and temperature are of importance, indicating the safety and normality of the power system [1]
State of Charge (SOC) has an undoubted critical position for BMS to realize management functions, and it indicates the remaining energy, whose accuracy is of great significance for service safety and life of batteries [2,3]
The limitation on the progress of BMS is mostly due to SOC unmeasurable and dynamic properties that are similar to the characteristics of the batteries, which are influenced by various factors [4], such as discharge rate, ambient temperature, battery degeneration, and external disturbance
Summary
The battery management system (BMS) is responsible for monitoring power batteries’ complete information to guarantee the electric vehicle (EV) performance, because the batteries’ parameters such as current, voltage, resistance, and temperature are of importance, indicating the safety and normality of the power system [1]. State of Charge (SOC) has an undoubted critical position for BMS to realize management functions, and it indicates the remaining energy, whose accuracy is of great significance for service safety and life of batteries [2,3]. The study of high accuracy SOC estimation methods is vitally important using measureable variables, such as current, voltage and temperature. With the rapid development of EV and the increasing importance of BMS, numbers of estimation approaches have been proposed to monitor the SOC. The ampere-hour (A·h) integral or Coulomb counting method [5,6] and open-circuit voltage method [7] are simple to implement, but non-model
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