Abstract
State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.
Highlights
In recent years, with the intensification of environmental pollution problem and energy shortage crisis, countries around the world have increased the development and utilization of clean energy [1].In the field of transportation, the promotion of electrical vehicles has been favored
European Driving Cycle (NEDC) schedule test results show that the average State of charge (SOC) estimation error of fuzzy logic sliding mode observer (FLSMO) algorithm is less than 1%
Battery status analysis is the basic of battery management system (BMS) and it includes state of charge estimation, state of health estimation, state of power estimation, and state of energy estimation
Summary
With the intensification of environmental pollution problem and energy shortage crisis, countries around the world have increased the development and utilization of clean energy [1]. The open circuit voltage method [5,6,7] performs SOC estimation based on the correspondence between open circuit voltage and battery state of charge. The biggest drawback of this method is that it requires the battery to stand for a long time so that its terminal voltage can equal to open circuit voltage They contain a large number of matrix operations, which is almost impossible to implement for the BMS system with limited computing power. The test results show that the FLSMO algorithm has high SOC estimation accuracy and considerable convergence rate It has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against current, voltage noise, and parameter disturbance.
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