Age Estimation of Li-ion Batteries Based on Internal Resistance for Electric Vehicle Applications
In electric vehicles and stationary storage applications, the increasing demand for Lithium-ion battery (LIB) systems highlights the need for precise state-of-health (SOH) estimation. Traditional approaches often rely on resource-heavy computations or impractical setups, such as controlled charging or high-frequency sampling, misaligned with the constraints of large-scale battery management systems (BMS) that operate with limited computational power and low-frequency data collected over seconds. This paper proposes a computationally efficient algorithm, using a nonlinear input-output neural network (NIONN) for state-of-charge (SOC) estimation and internal resistance derived from voltage drops during current transients for SOH estimation. Furthermore, temperature compensation through polynomial fitting enhances the accuracy of the estimate. Validated on the NASA battery aging dataset, the proposed approach achieves an average error below 3%. With only 41 arithmetic operations and adaptability to diverse conditions. The proposed method with real-time SOH monitoring for electric vehicles and large-scale energy storage offers a practical, robust solution tailored to BMS limitations.
