The battery is an important part of pure electric vehicles and hybrid electric vehicles, and its state and parameter estimation has always been a big problem. To determine the available energy stored in a battery, it is necessary to know the current state-of-charge (SOC) and the capacity of the battery. For the determination of the battery SOC and capacity, it is generally estimated according to the Electromotive Force (EMF) of the battery, which is the open-circuit-voltage (OCV) of the battery in a stable state. An off-line battery SOC and capacity estimation method for lithium-ion batteries is proposed in this paper. The BP neural network with a high accuracy is trained in the case of sufficient data with the new neural network intelligent algorithm, and the OCV can be accurately predicted in a short time. The model training requires a large amount of data, so different experiments were designed and carried out. Based on the experimental data, the feasibility of this method is verified. The results show that the neural network model can accurately predict the OCV, and the error of capacity estimation is controlled within 3%. The mentioned method was also carried out in a real vehicle by using its cloud data, and the capacity estimation can be easily realized while limiting inaccuracy to less than 5%.

The state estimation of a battery is a significant component of a BMS. Due to the poor temperature performance and voltage plateau phase in LiFePO₄ batteries, the difficulty of state estimation is greatly increased. At the same time, the ambient temperature in which the battery operates is changeable, and its parameters will vary with the temperature. Therefore, it is extremely challenging to estimate the state of LiFePO₄ batteries under variable temperatures. In an effort to accurately estimate the SOC of LiFePO₄ batteries at different and variable temperatures, as well as its capacity at low temperature, the characteristics of LiFePO₄ batteries at different temperatures are first tested. In addition, a variable temperature OCV experiment is designed to obtain the OCV of the full SOC range. Then, the ECM considering temperature is established and all parameters are identified by PSO. Finally, an improved EKF algorithm is presented to accurately estimate the SOC of LiFePO₄ batteries at different and variable temperatures. Meanwhile, the battery capacity at low temperature is further estimated based on the estimated SOC result. The results show that SOC estimation errors at variable temperature are all within 3%, and the capacity estimation errors at low temperature are all within 1%.

Owing to the continuous development of the automobile industry, increasingly stringent performance requirements for noise, vibration, and harshness of automobiles are being presented. Interior noise control in high-speed vehicles has not been adequately addressed, owing to the complex mechanism of noise generation. As simulations performed previously focused on vehicle wind noise and tyre noise cannot adequately predict the effect on passenger ear-side noise, these issues are investigated in this study. Their effects on passengers are investigated using transfer path analysis. An anti-noise operational transfer path analysis is proposed to study noise generated in high-speed vehicles. The established anti-noise operational transfer path analysis model can eliminate crosstalks between noise source signals of different transmission paths. The model is validated by comparing the measured and calculated values of the anti-noise operational transfer path analysis model. The coherence of the input noise signal and the ear-side noise signal of the passenger is assessed using coherence analysis. By calculating and categorising the contributions of different noise sources in different locations and types, the main noise sources affecting passenger comfort are determined. The result indicates that the main noise sources affecting the passenger’s ear-side noise change from engine noise to left-A wind noise and tyre radiation noise with increasing vehicle speed, in which the proportion also increase. The proposed anti-noise operational transfer path analysis is suitable for the interior-noise analysis of high-speed vehicles, and this study may serve as a reference for future studies regarding active and passive noise control in high-speed vehicles.

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.