Speaker
Description
In hybrid vehicles, the contrast between engine‑off and engine‑on states makes engine noise noticeable, and in series hybrid vehicles the engine is used only for power generation and does not need to match the driving state, so engine sound can become annoying. To improve engine sound quality, factors contributing to engine‑noise perception must be quantitatively identified.Using on‑road data from a series hybrid vehicle, a machine‑learning model is trained to predict engine noise perception from vehicle parameters and interior sound levels. Analysis of parameter importance and prediction errors clarifies empirical knowledge: perception depends not only on interior sound level but also on vehicle speed, acceleration state, and driver acceleration intention. The balance between engine noise and background noise (road and wind), together with acceleration intention, is identified as a key factor governing annoyance.Based on these findings, we propose a perceptually motivated broadband active noise control (ANC) targeting engine‑noise components below 300 Hz. Under low‑speed, low‑load conditions with little acceleration demand, where engine noise is noticed, the series‑hybrid architecture allows the engine speed to be held at a constant value, enabling broadband ANC to effectively reduce the target components. During acceleration phases the operating condition varies rapidly and the broadband ANC effect becomes smaller; however, the perception model indicates that large additional attenuation is not required in these conditions.Consistent with the identified perceptual characteristics, the proposed ANC and engine‑speed control strategy adapts across driving conditions. On‑road evaluations demonstrate reduced engine‑noise level in the target scenes and improved interior quietness in an electrified vehicle.