Hongyu Shu's research while affiliated with Chongqing University and other places

... The planning of lane change trajectory needs to transform the time and distance planning problems into constraint optimization problems, and the evaluation indexes in the driving process need to be considered to calculate the reference trajectory satisfying the safety, comfort, and lane change efficiency [20]. The polynomial method [21], trapezoidal acceleration method [22], arc trajectory planning method [23], sine function method [24], and other trajectory functions are indeed common lane change reference trajectory design methods. Designers can choose a solution according to the characteristics of application scenarios as well as the advantages and disadvantages of each method [25]. ...

... Regarding the torque allocation method based on optimization, many optimization objective functions have been proposed to improve vehicle performance. For instance, reference [39] adopts a multi-objective driving/braking torque allocation algorithm to achieve minimum energy consumption while maintaining vehicle stability. Reference [40] proposes a method based on the minimum tire slip criterion to prevent sideslip due to tire force saturation. ...

... Through the structure design, the strain has been amplified by three times. Shu et al. [102] also adopted lever-type mechanisms to form a novel disk-shaped torque sensor. The authors present a lever-type method of strain exposure, which can optimize the strain uniformity and improve the trade-off of the conventional disk-shaped torque sensor. ...

... The extended Kalman filter is based on the Kalman filter algorithm, a recursive algorithm proposed by R.E. Kalman in 1960 for linear or nonlinear systems, which allows for measurement errors and model biases in the system and is robust to noise and is therefore widely used in industrial process control, navigation, and system identification [15]. The extended Kalman filter is divided into two processes: prediction and correction, where the prediction part is based on the estimation and covariance of the previous moment, and the correction part is based on the correction of the predicted system state from the measurement data [16]. ...

... Therefore, the research of new control strategy to improve the performance of the PMSM control system and ensure the reliable and safe operation of PMSM has become a research hotspot in the field of motor control (Zhu et al., 2014;Cheng et al., 2015;Deng et al., 2019;He and Wu, 2019). With the development of sensorless drive technology of permanent magnet synchronous motor, many methods have been proposed to estimate the speed and position of the rotor, such as the model reference adaptive control method (Zhong and Lin, 2017;Zhong et al., 2018;Wang et al., 2020a), sliding mode observer method (Liu et al., 2016;Liang et al., 2017;Lu et al., 2021), Kalman filter algorithm (Moon and Kwon, 2016;Yang et al., 2016;Luo et al., 2019), and artificial intelligence algorithm (Fadil et al., 2015). It is worth mentioning that the UKF can be applied to a nonlinear model and has been widely used in the estimation of the rotor position and speed of the PMSM drive system (An and Hameyer, 2014;Zhou et al., 2018;Tao and Guo, 2019;Yin et al., 2019). ...

... The Kalman filter equation is in a recursive form in the time domain, and thus there is no need to store large amounts of data when solving the equation; therefore, the Kalman filter method is widely used because of its fast operation speed and good real-time performance. In [26], an unscented Kalman filter (UKF) algorithm, based on the measurable variables was proposed. In this algorithm, the difficult-to-measure realtime state variables on the vehicle were estimated to provide accurate values for an integrated controller. ...

... Additionally, the two body segments are connected by a tri-axis translational and rotational springs and dampers at the waist. Thus, a simulation model considering the HVDC is constructed, as shown in figure 2. The spring and damping parameters at the human-seat interface are referred to the literature [16]. A comparison model neglecting the HVDC is built by assigning fixed joints between the driver and the MEV. ...

... Lv et al. [21] studied the impact of driver styles on energy consumption and found that the aggressive behaviour consumes 10% more energy than the calm behaviour. The same comparison study was also done in Yang et al. [22] to found that the energy consumption with aggressive behaviour is higher by 9.4% compared to calm behaviour. However, both of them have not studied energy consumption with reference to the normal driving style. ...

... Gong Tianyang et al. [29] designed a fuzzy control method, which takes the errors of yaw rate and sideslip angle as tracking targets, outputs additional yaw moment and rear wheel angle, and uses a genetic algorithm to optimize the membership function of fuzzy control. Yitong Song [30] proposed the estimation method of tire lateral force and vehicle sideslip angle based on an odorless Kalman filter and proposed a hierarchical control strategy where the upper controller is the sliding mode control and the lower controller is the optimal distribution controller, which improves the vehicle handling stability and energy efficiency. However, the nonlinear characteristics of vehicle motion cannot be avoided by using simple sliding mode control. ...