The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance.
The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.
To preserve the fun of driving and enhance driving convenience, a smart regenerative braking system (SRS) is developed. The SRS provides automatic regeneration that is appropriate for the driving conditions, but the existing technology has a low level of acceptability and comfort. To solve this problem, this paper presents an automatic regenerative control system based on a deceleration model that reflects the driver’s characteristics. The deceleration model is designed as a parametric model that mimics the driver’s behavior. In addition, it consists of parameters that represent the driver’s characteristics. These parameters are updated online by a learning algorithm. The validation results of the vehicle testing show that the vehicle maintained a safe distance from the leading car while simulating a driver’s behavior. Of all the deceleration that occurred during the testing, 92% was conducted by the automatic regeneration system. In addition, the results of the online learning algorithm are different based on the driver’s deceleration pattern. The presented automatic regenerative control system can be safely used in diverse car-following situations. Moreover, the system’s acceptability is improved by updating the driver characteristics. In the future, the algorithm will be extended for use in more diverse deceleration situations by using intelligent transportation system information.
A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver convenience and energy efficiency by suppressing the frequent braking of the driver brake pedaling. In order to apply this assistance system, a deceleration planning algorithm should guarantee the safety deceleration under diverse driving situations. Furthermore, the planning algorithm suppresses a sense of heterogeneity by autonomous braking. To ensuring these requirements for deceleration planning, this study proposes a multi-level deceleration planning algorithm which consists of the two representative planning algorithms and one planning management. Two planning algorithms, which are the driver model-based planning and optimization-based planning, generate the deceleration profiles. Then, the planning management determines the optimal planning result among the deceleration profiles. To obtain an optimal result, planning management is updated based on the reinforcement learning algorithm. The proposed algorithm was learned and validated under a simulation environment using the real vehicle experimental data. As a result, the algorithm determines the optimal deceleration vehicle trajectory to autonomous regenerative braking.World Electric Vehicle Journal 2019, 10, 57 2 of 18 when the preceding vehicle is decelerating. Then, it should determine the deceleration profile that can guarantee safety by the automatic braking. Furthermore, the driver can feel the heterogeneity because the automatic braking control is difficult to apply to the driving style of the individual drivers [2,6,7].The SRB system requires a deceleration specified planning algorithm to determine the set-point for regenerative torque control. In general, the continuous optimization method based planning is widely applied to determine the vehicle trajectory for autonomous longitudinal vehicle control applications, such as an adaptive cruise control system [8][9][10]. The optimization method-based planning algorithm determines the optimal vehicle trajectory to minimize the selected cost function and constraints. This numerical optimization method using the mathematical model offers the advantages that the determined trajectory can apply to diverse driving conditions. However, this method cannot consider individual driver characteristics efficiently.The other method is an intelligent driver model (IDM) based planning. This method was introduced at [11] and provides the deceleration profile by reflecting the individual driver characteristics. The model consists of the mathematical equation and model parameters. The model configures the model parameters according to the driver characteristics. In our previous research, the deceleration specified planning algorithm was proposed based on the intelligent driver model for the SRB system [12]. This can determine the ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.