Search

There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified. Therefore, EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality. Firstly, the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted, respectively, then three physiological EEG features of PSD_β, PSD_γ and DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms. Subsequently, the Adaptive Genetic Algorithm (AGA) is proposed to optimize the Elman model, where an intelligent model (AGA–Elman) is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality. The results demonstrate that the error of the constructed AGA–Elman model is only 2.88%, which outperforms than the traditional BP and Elman model; Finally, two vehicle acceleration sounds (Design1 and Design2) are designed based on the constructed AGA–Elman model from the perspective of order modulation and frequency modulation, which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.

The eco‐driving strategy is of great significance in driving cost for plug‐in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure‐driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning‐based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning‐based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed‐integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections. This study proposes an ecological approach and departure‐driving strategy by using syncretic learning with trapezoidal collocation algorithm. The speed predictor is combined with the trapezoidal collocation algorithm to obtain the speed trajectory with higher accuracy. The driving interval optimization method is proposed to make vehicles cross the intersections without stopping.

Considering the trade‐off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi‐objective trade‐off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade‐off optimization is carried out using the NSGA‐II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware‐in‐the‐loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade‐off between economy and ride comfort for the MMHEV. This article focuses on the trade‐off between fuel economy and driving comfort of multimode hybrid electric vehicle (MMHEV). Based on equivalent consumption minimization strategy–Pareto joint algorithm, a bi‐objective trade‐off control strategy is designed. The results show that the driving comfort can be greatly improved with a small sacrifice of economy.

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

To improve fuel economy of a novel series-parallel hybrid electric bus (SPHEB) further, the control rule of battery power distribution is derived from the offline optimisation algorithm based on Pontryagin's minimum principle, internal relationship between the equivalence fuel factor λ and trend of state of charge (SOC) with different driving condition is established and calculation model of SOC is formed. On the basis of the above, according to the feature of the city bus driving cycle, the algorithm of adaptive fuzzy sliding-mode control (AFSMC) is introduced to periodically update the equivalence fuel factor, and then the power-balancing adaptive control strategy is developed. To validate the proposed strategy rationality, the hardware in-the-loop integrated with bench test is applied to compare the proposed control strategy with the previous optimal control strategy. The results indicate that the efficiency of SPHEB system is improved by the proposed control strategy.

To achieve better fuel economy for plug-in hybrid electric vehicles (PHEVs), this paper proposes a novel improved adaptive equivalent consumption minimization strategy (A-ECMS) integrated driving condition prediction using artificial neural network (ANN) combined the least-squares with forgetting factors. Firstly, the ANN method and the least-square with forgetting factors are used to predict the velocity of the vehicle and the slope of the road. Then a trip adaptive ECMS is proposed which the equivalent factor (EF) is adjusted in real-time according to the remaining distance. Furthermore, the driving condition prediction technology is integrated into A-ECMS to decrease fuel consumption further. Besides, the impact of different preview horizon lengths on fuel consumption is analyzed. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Fuzhou road network. Simulation results show that, compared with CD-CS, the A-ECMS combined with driving condition prediction can achieve better fuel economy with a fuel consumption reduction by 12.1%, which effectively improves the fuel economy of the PHEV.

The performance of the shift strategy can reduce the energy consumption of two automatic manual transmissions (AMT) electric vehicles while meeting the needs of various drivers. Hence, the adjustment of the shift strategy is complicated due to the uncertain driver behavior. To address the above issue, an online driver behavior adaptive shift strategy based on dynamic corrected factors is proposed. Firstly, the simplified models of the power system and conventional shift strategies are constructed for electric vehicles. Secondly, principal component analysis and k-means algorithms are implemented to classify driver styles. Next, Learning Vector Quantization neural network and Fuzzy neural network are applied to identifying driving style and driving intention in real-time. Then, according to the driver behavior, a dynamic corrected factor is introduced. The dynamic corrected factors of different driver styles are modified to adjust the proportion of power and economy in the shifting process. As a result, the proposed shift strategy based on dynamic corrected factors achieves a compromise between power and economy for two-speed AMT electric vehicles. The numerical validation results demonstrate that the proposed shift strategy is energy-saving compared with the conventional shift strategy and can satisfy the requirements of various driver styles.

•A SOC estimation method using AUKF based on an n-RC model is designed.•The estimation accuracy of n-RC models under different n is compared.•The accuracy analysis of different equivalent circuit models is conducted.•The nonlinear Least Square method is utilized for parameters identification.•The accuracy of the proposed method is verified under different conditions.

•The offline optimal control results considering battery life are obtained by an improved Pontryagin's minimum principle•The determination coefficient is introduced to determine the train data for neural network learning•Dual-state feedback control is applied to the energy management strategy•The adaptability and effectiveness of the proposed strategy are assessed by random driving cycles