Abstract

Accurate state of charge (SOC) estimation can prolong lithium-ion battery life and improve its performance in practice. This paper proposes a new method for SOC estimation. The second-order resistor-capacitor (2RC) equivalent circuit model (ECM) is applied to describe the dynamic behavior of lithium-ion battery on deriving state space equations. A novel method for SOC estimation is then presented. This method does not require any matrix calculation, so the computation cost can be very low, making it more suitable for hardware implementation. The Federal Urban Driving Schedule (FUDS), The New European Driving Cycle (NEDC), and the West Virginia Suburban Driving Schedule (WVUSUB) experiments are carried to evaluate the performance of the proposed method. Experimental results show that the SOC estimation error can converge to 3% error boundary within 30 seconds when the initial SOC estimation error is 20%, and the proposed method can maintain an estimation error less than 3% with 1% voltage noise and 5% current noise. Further, the proposed method has excellent robustness against parameter disturbance. Also, it has higher estimation accuracy than the extended Kalman filter (EKF), but with decreased hardware requirements and faster convergence rate.

Highlights

  • In recent years, electric vehicles (EVs) have been of increased interest because of the global energy shortage and growing environmental pollution [1]

  • Many governments are promoting the use of electric vehicles, including battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), and hybrid electric vehicles (HEVs)

  • Before the equivalent circuit model (ECM) described above can be used for state of charge (SOC) estimation, the values of parameters

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Summary

Introduction

Electric vehicles (EVs) have been of increased interest because of the global energy shortage and growing environmental pollution [1]. Artificial neural networks, fuzzy logic, and support vector machine methods are intelligent computational algorithms that can theoretically calculate SOC with high precision, but these methods require a significant amount of training data. This training process is time-consuming and almost impossible to accomplish because of the complexity of practical driving conditions. The validation results show that the proposed method has good performance in terms of estimation accuracy and robustness against measurement noise and parameter uncertainty. This method does not require matrix calculation, so the computation cost is significantly low.

Battery Equivalent Circuit Model
Schematic
Parameter Identification
Measured fitted
Transient
Model Validation Test
Schedule model output
Design of the Novel
Experiments and Discussion
Schedule
Figure
Robustness
Capacities and parametersof of equivalent equivalent circuit
Comparison
Values
Findings
Methods
Conclusions
Full Text
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