Driver intention based coordinate control of regenerative and plugging braking for electric vehicles with in‐wheel PMSMs

Abstract

Electric vehicles have been the focus of the automotive industry in recent years. However, relatively small driving range of electric vehicles makes it not be broadly adopted in the market. Regenerative braking is one of the most effective ways to extend the endurance of electric vehicles. To sufficiently utilise the regenerative braking of electric vehicles and explore the potential of the electric motor plugging braking capability to simplify the braking system structure and reduce the cost, a new braking strategy based on the driver's braking intention and motor working characteristics is proposed. Driver's braking intention is classified as the emergency braking and the normal braking. In the case of normal braking, model predictive control (MPC) is used to express driver's braking intention. By adjusting the weight of the MPC cost function, different braking intentions can be achieved. This strategy is able to achieve as much as possible braking energy recovery without violating the driver's braking intention. In the case of the emergency braking, the sliding mode based optimal slip ratio control is adopted and it is able to obtain the shortest braking distance. In order to validate the effectiveness of the proposed approach, numerical simulations on a quarter-vehicle braking model are tested.

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

“…Besides, the state transfer of vehicle velocity and time interval in k step are calculated by Eqs. (13) and (14), respectively.…”

“…Xu et.al [12] presented two braking regenerative energy optimization controllers to promote regenerative energy, it considering motor efficiency to distribute the friction and motor braking torque of the front and rear wheels. Li et al [13] proposed a composite RBS to optimize regenerative and plugging 1 School of Mechanical Engineering, Southeast University, Nanjing 211189, China braking simultaneously with the driver's intention recognition. Lian et al [14] designed an optimal braking force distribution strategy, while uses a safety distance model to avoid collision.…”

A Braking Intention Identification Method Based on Data Mining for Electric Vehicles

“…In [19], a layering hidden Markov model and adaptive neurofuzzy inference system for braking intention identification were built to optimize AMT shift control strategy to improve the braking energy recovery rate of the extended-range heavy commercial electric vehicle. In [20], a new regenerative braking control strategy based on braking intention identification and motor working characteristics is proposed. In the paper, braking intention is classified as the emergency braking and the normal braking.…”

“…The iterative equations of the fuzzy C-means clustering algorithm are shown in (18) and (19). The iterative termination condition is shown in (20). is the number of samples.…”

Adaptive Robust Control System for Axial Flux Permanent Magnet Synchronous Motor of Electric Medium Bus Based on Torque Optimal Distribution Method

“…The particle swarm optimization algorithm is used to optimize the torque distribution mathematical model to obtain the optimal torque distribution solution. In addition, regenerative braking is one of the most effective ways to extend the durability of electric vehicles [10][11][12]. In order to further increase the cruising range, this paper applies the previously described optimal torque distribution method to the braking situation.…”