Abstract
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.
Highlights
Electric vehicles (EVs) have received significant attention because of the global environmental crisis and pressure on energy sources
From the perspective of driving safety and energy economy, the optimum braking stability and the maximum regenerative energy recovery efficiency are treated as optimization objectives based on the novel regenerative–mechanical coupled brake-by-wire system discussed in this paper
Aiming at the novel regenerative–mechanical coupled brake-by-wire system, a deep learning network with an input layer, an output layer, and three hidden layers was built with the design of experiment sample points treated as input parameters and the optimization variables treated as output parameters
Summary
Electric vehicles (EVs) have received significant attention because of the global environmental crisis and pressure on energy sources. 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 control algorithm based on a model predictive control framework was proposed to recover more braking energy and maintain the optimal slip value. Sun presented a predictive control strategy using an offline process optimization stream to realize the balance between the maximum regenerative energy recuperation efficiency and the optimum braking stability [18,19].
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