Abstract
The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, it is difficult to get an accurate value of SOC, because the SOC cannot be directly measured by a sensor. In this paper, an adaptive gain nonlinear observer (AGNO) for SOC estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs) is proposed. The second-order resistor–capacitor (2RC) equivalent circuit model is used to simulate the dynamic behaviors of a LIB, based on which the state equations are derived to design the AGNO for SOC estimation. The model parameters are identified using the exponential-function fitting method. The sixth-order polynomial function is used to describe the highly nonlinear relationship between the open circuit voltage (OCV) and the SOC. The convergence of the proposed AGNO is proved using the Lyapunov stability theory. Two typical driving cycles, including the New European Driving Cycle (NEDC) and Federal Urban Driving Schedule (FUDS) are adopted to evaluate the performance of the AGNO by comparing with the unscented Kalman filter (UKF) algorithm. The experimental results show that the AGNO has better performance than the UKF algorithm in terms of reducing the computation cost, improving the estimation accuracy and enhancing the convergence ability.
Highlights
With the soaring energy crisis and environmental concerns on exhaust emission from traditional internal combustion engine vehicles (ICEVs), electric vehicles (EVs) have gained increased attention in recent years
The test procedure is listed as follows: (1) the battery is firstly charged to the fully charged state with the standard charging method at the room temperature, and it is left in the open-circuit condition for 5 h; (2) the battery terminal voltage is measured and the measured voltage is regarded as the equilibrium potential since the battery is assumed to reach the steady state; (3) the battery is discharged with a constant current of 0.1C by 10% of the nominal capacity, and it is left in the open-circuit condition for 2 h; and (4) Steps (2) and (3) are repeatedly performed until the battery reach a fully discharged state
The reason of comparing with unscented Kalman filter (UKF) algorithm is that it has been demonstrated to be a good method for state of charge (SOC) estimation of lithium-ion batteries (LIBs) [34,35]
Summary
With the soaring energy crisis and environmental concerns on exhaust emission from traditional internal combustion engine vehicles (ICEVs), electric vehicles (EVs) have gained increased attention in recent years. It can be used for SOC estimation of nonlinear battery systems with a non-Gaussian distribution and is not sensitive to the dimension of the system This method requires a massive number of particles and numerous matrix operations, so it has high requirements for hardware. The EKF [17,18,19,20,21,22,23,24,25,26,27] transforms a nonlinear system into a linear system by linearizing the nonlinear function based on the first-order Taylor series expansion, which results in large linearization error and the instability of the filter for highly nonlinear battery systems in EVs. the EKF has to compute the complicated Jacobian matrix, leading to the increase of computation cost and the instability. A novel approach for SOC estimation using an adaptive gain nonlinear observer (AGNO) is proposed This method does not demand complicated matrix operations, so it can reduce the computation cost.
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