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Current regenerative braking systems in electric vehicles have several problems, such as complex structures, too many control parameters, and inconsistent braking responses. To solve these problems, a control algorithm with multidisciplinary design optimization (MDO) is proposed based on the novel regenerative-mechanical coupled brake-by-wire system. A dynamic model of the novel regenerative braking system was established to analyze the mechanism of coupled braking and propose a braking torque distribution strategy. To realize a better balance between the optimum braking stability and the maximum regenerative energy recovery based on the braking torque distribution strategy and sample points, the MDO mathematical model was developed to optimize the control parameters with the collaborative optimization algorithm. The finite sample points comprising the vehicle speed, battery state-of-charge, and braking severity were obtained through an optimal Latin hypercube design and represent the overall design space. A network was established based on the sample points and the optimization results. Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization.Energies 2018, 11, 2322 2 of 18 be modulated to control the overall braking torque for meeting the requirements [5]. The regenerative braking torque depends on the motor characteristics [6], the charging power capability of the battery [7], and the available tire-road friction [8]; hence, existing research has focused on improving the braking energy recovery efficiency by distributing the torque between regenerative braking and friction braking based on drivers' demands.In engineering practice, several rule-based regenerative braking strategies have been proposed and used in recent years. For a rear-driven electric truck, a modified control strategy that determines how to distribute the braking force between the front and rear axles was proposed by Zhang to improve the recovery efficiency [9]. Xiao presented an integrated control strategy to coordinate regenerative and friction braking forces to deal with the braking stability and recovery efficiency when a vehicle performed normal deceleration and emergency braking [3]. To regenerate more braking energy and move closer to the ideal braking force distribution curve, a combined braking control strategy was developed for the rear wheel-driven series hybrid electric EV to adjust the proportions of regenerative braking and friction braking [10]. A regenerative braking cooperative control strategy was proposed by Jiweon for hybrid EVs equipped with a hydraulic brake on the rear wheels and an electronic wedge brake on the front wheels [11].Optimization and...

Professor drivers, including racing drivers, can drive cars to achieve drift motions by taking control of the steering angle in high tire slip ratios, which provides a way to improve the driving safety of autonomous vehicles. The existing studies can be divided into two kinds based on analysis methods, and the theory-based is chosen in this study. Because the recent theory based is most applied for planar models with neglect of the rollover accident risk, the nonlinear vehicle model is established by considering longitudinal, lateral, roll, and yaw motions and rolling safety with the nonlinear tire model UniTire. The drift motion mechanism is analyzed in steady and transient states to obtain drift motion conditions, including the velocity limitation and the relationship between sideslip angle and yaw rate, and vehicle main status parameters including the velocity, side-slip angle and yaw rate in drift conditions. The state-feedback controller is designed based on robust theory and LMI (linear matrix inequation) with uncertain disturbances to realize circle motions in drift conditions. The designed controller in simulations realizes drift circle motions aiming at analyzed status target values by matching the front-wheel steering angle with saturated tire forces, which satisfies the Lyapunov stability with robustness. Robust control in drift conditions solves the problem of how to control vehicles to perform drift motions with uncertain disturbances and improves the driving safety of autonomous vehicles.

In view of the little research that has been conducted on the ride comfort of mini vehicles, an electric mini off-road vehicle was designed in this study and a 2 degree-of-freedom quarter car model was established to investigate the ride comfortability. The amplitude-frequency and vibration response characteristics of the suspension were analyzed with the natural frequencies of the front and rear suspensions selected in accordance with the required driving performance. A comprehensive objective function with respect to the safety and comfortability was established, and the damping ratio of the suspension was determined. The damping characteristics of the shock absorber were analyzed to derive an adjustment rule of the suspension damping ratio. The piecewise linear speed characteristics of the shock absorber were subsequently obtained, and suspension-parameter identification and ride comfort tests were conducted. The test results showed that the natural frequencies and damping ratios of the front and rear suspensions were 1.676 and 1.922 Hz, and 0.225 and 0.242, respectively. The results of a pulse input test and D-level road random running test also demonstrated the safety and good ride comfortability of the vehicle.

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