Predictive and Adaptive SOC-Band Strategy for Series Hybrid Electric Vehicles

Abstract

Hybrid Electric Vehicles (HEVs) have emerged as an effective solution to reduce fuel consumption and emissions while maintaining desirable vehicle performance. In particular, series HEV architectures offer enhanced flexibility in power management due to the decoupling of the internal combustion engine from the drivetrain. However, the overall efficiency of such systems largely depends on the effectiveness of the Energy Management Strategy (EMS). This paper presents an adaptive state-of-charge (SOC) bandbased EMS for a series HEV to achieve an improved trade-off between fuel consumption and battery utilization. A detailed MAT-LAB/Simulink model of a series HEV is developed, incorporating engine, generator, battery, and traction motor subsystems. Four EMS strategies are implemented and comparatively analyzed, including a fixed baseline strategy, an SOC-based generator control strategy, an aggressive SOC and engine control strategy, and the proposed adaptive SOC-band strategy. The battery is modeled using an SOC-dependent open-circuit voltage formulation along with a coulomb-counting method for SOC estimation. The performance of all strategies is evaluated under the standard EPA Urban Dynamometer Driving Schedule (UDDS) drive cycle with an initial SOC of 0.6667. Simulation results demonstrate that while baseline strategies either minimize fuel consumption or maintain SOC individually, they fail to achieve a balanced performance. The proposed adaptive SOC-band EMS achieves a significant reduction in fuel consumption, reducing fuel usage from 0.0949 kg (baseline strategy) to 0.0597 kg, corresponding to an improvement of approximately 37.1%, while maintaining SOC within acceptable operational limits.