Lulu Guo

Optimal Trajectory Planning of Motor Torque and Clutch Slip Speed for Gear Shift of a Two-Speed Electric Vehicle

In order to improve the shift quality of a 2-speed I-AMT of electric vehicle, optimal control is used to generate the reference trajectories of the clutch slip speed and motor torque. The off-line optimization results are fitted and used for online implementation. In order to compensate the disturbances and modeling errors, a PID controller is added to ensure the closed-loop control performance. The proposed controller is almost free of calibration effort, because the feedforward part of the proposed controller considered the simple but dominant system dynamics. Finally the control algorithm is confirmed through large amounts of tests on a complete power train simulation model, and the designed controller can provide satisfactory performance even under large variation of vehicle mass and road grade.

Online Shift Schedule Optimization of 2-Speed Electric Vehicle Using Moving Horizon Strategy

Energy consumption of a vehicle is greatly influenced by its driving behavior, which is reflected by gear shift strategy in powertrain systems. Because of the discrete gear ratio and nonlinear dynamics of vehicles, devising an optimal gear shift strategy is quite challenging. This paper presents an online shift schedule optimization strategy for improving energy efficiency of an electric vehicle (EV) with 2-speed automated manual transmission (AMT). The optimal gear shift strategy is obtained by solving a nonlinear time-varying optimal problem, wherein, the equivalent energy consumption, including motor power and travelling distance is chosen as the objective function. By exploiting the particular structure of the problem and combining Pontryagins minimum principle and numerical methods, a compuationally efficient solution approach is proposed, in the frame of moving horizon control. Simulation results on an AMESim model of an EV with 2-speed AMT over multiple driving cycles using different controllers are presented, and Hardware in the Loop simulation for experimental validation is also given in this paper. The results indicate that both energy efficiency and computational speed are improved.

Optimal Energy Management for HEVs in Eco-Driving Applications Using Bi-Level MPC

Wide usage of vehicle’s onboard navigation system offers vehicles better terms to improve energy efficiency. In this paper, a computationally effective energy management strategy using model predictive control (MPC) is proposed to find the energy optimal torque split, gear shift, and velocity control of a parallel hybrid electric vehicle (HEV). We consider the vehicles in urban driving, where the vehicle trajectory is constrained by the infrastructure (road signs) and other vehicles (traffic). Restricted by the discrete gear ratio, nonlinear dynamics of the vehicles, and especially different time scales between velocity trajectory and torque split optimization, finding these control variables in one optimal problem is quite challenging. Thus, this paper uses bi-level methodology to reduce computational time and simplify the hybrid optimal problem by decoupling its components into two subproblems. In the outer loop, the optimal velocity trajectory is obtained by solving a nonlinear time-varying optimal problem using a Krylov subspace method to improve computational efficiency. In the second subproblem, we provide an explicit solution of the optimal torque split ratio and gear shift schedule by combining Pontryagin’s minimum principle and numerical methods in the framework of MPC. Simulation results on an AMESim model of an HEV with seven-speed automated manual transmission over multiple driving cycles are presented. The results indicate that both energy efficiency and computational speed are improved.

A study on gear shifting schedule for 2-speed electric vehicle using dynamic programming

For a novel electric vehicle equipped with 2-speed Automated Manual Transmission (AMT), an optimal gear shifting schedule is designed to improve the energy economy of the vehicle. Given a driving cycle, the global optimal transmission ratio control sequence can be derived from Dynamic Programming algorithm. Then the optimal gear shifting schedule is achieved from the map of the optimal gear operational points of multiple driving cycles. Compared with some other selected gear shifting schedules, it is shown that the optimal shifting schedule improves energy consumption economy by about 5.5%.

Optimal control methods in intelligent vehicles

In recent years, intelligent vehicles (IVs) have become a hot spot in automotive industry. Key technologies of IVs range over the field of sensing, decision-making and control. Among them, control technology provides an enabling support for improving autonomous driving safety, reducing energy consumption and carbon emission. This paper focuses on some aspects of applying advanced control methodologies in IVs through several selected examples including eco-driving and MPC-based driver modelling.

On-line Optimal Control of the Gearshift Command for Multispeed Electric Vehicles

A design method for the gearshift strategy in powertrain systems is proposed to explore the energy saving potential of an electric vehicle (EV) equipped with a multispeed automated manual transmission (AMT). The optimal gearshift schedule is obtained by solving a nonlinear time-varying optimal problem in the framework of model predictive control, wherein, the vehicle driveability, represented by the drivers power demand satisfaction, and battery efficiency are considered. The solution approach is developed basing on the combination of Pontryagins minimum principle and numerical methods, in addition to the real-time applications. Simulation results for a passenger EV with four-speed AMT on different drive cycles show that compared with the case of a standard gearshift strategy, an additional fuel saving can reach 3–5% and even more when considering road characteristic such as road slope. Furthermore, hardware in the Loop simulation for experimental validation is also given in this paper. Results indicate that both energy efficiency and computational speed are improved.

Fuel economy optimization of hybrid electric vehicles

Fuel economy optimization of hybrid vehicles is essentially solving the optimal energy management mode and shift schedule under specified system constraints so as to realize the optimal control of hybrid vehicles. However, the global optimal dynamic programming control strategy cannot use in the real-time control since the uncertainty of the driving environment, an optimization control strategy which can be used in the practice is proposed in this paper. Under knowing the running state information of the car on a predicted future time domain provided by the vehicles navigation system (GPS/GIS), this paper solve the optimal shift schedule and energy management mode which minimize the equivalent fuel consumption of the forecast time domain with the dynamic programming algorithm(DP), and then under the framework of model predictive control to realize the rolling optimization. Finally, the energy management mode and shift schedule of hybrid electric vehicles have been verified by the simulation, and the simulation results show the effectiveness of the optimization control strategy of the hybrid electric vehicles.

A fast algorithm for nonlinear model predictive control applied to HEV energy management systems

This paper presents a fast algorithm for nonlinear model predictive control. In real-time implementation, a nonlinear optimal problem is often rewritten as a nonlinear programming (NLP) problem using the Euler method, which is based on dividing the prediction horizon into N steps in a given time interval. However, real-time optimization is usually limited to slow processes, since the sampling time must be sufficient to support the task’s computational needs. In this study, by combining the Gauss pseudospectral method and model predictive control, a fast algorithm is proposed using fewer discrete points to transcribe an optimal control problem into an NLP problem while ensuring the same computational accuracy as traditional discretization methods. The approach is applied to the torque split control for hybrid electric vehicles (HEV) with a predefined torque demand, and its computational time is at least half that of the Euler method with the same accuracy.

Predictive safety control for road vehicles after a tire blowout

Since driver error is considered a major cause in over 90% of all road crashes [1], driving assistance systems are widely used to improve driving safety, and are an important research direction in the intelligent vehicle field. With the rapid development of sensing, identification and communication technologies, the vehicle can obtain an increasing amount of information in real time, ranging from the vehicle status information to road information and traffic information. Therefore, the function of the driving assistance systems is gradually expanding. The application of the model predictive control theory in automobile control has received wide attention [2–5]. One of the main reasons is that it can predict the future state of the system. For an automobile’s active safety control under extreme operating conditions, the safety control input can be obtained through the coordination of safety, comfort, and other indexes. Depending on the degree of risk, the weights of the indexes will be different. By comparing the safety control input with the driver input, the correctness of the human driver’s action can be evaluated. Based on the evaluation results, the driving assistant system will decide whether to intervene in the movement of the vehicle. Since the safety control input is calculated by predicting the future state of the system, we call this control method the predictive safety control; a general block diagram that reflects the control idea is shown in Figure 1(a). It is an effective control method proposed for the driving assistance system under certain extreme operating conditions, for example, a tire blowout accident. In the following, we will combine the specific extreme operating condition of the tire blowout to explain how this control method can be used to design the driving assistance system. It is noteworthy that the example used in this article is only reflective of one case, and some modules have been simplified. For other cases of active safety problem, the predictive safety control diagram can be used flexibly.

Optimization of gearshift MAP based on DP for vehicles with automated transmission

Dear editor, The gearshift system is the key part of an automatic transmission vehicle that optimizes power and economy. An accurate and reasonable shift schedule has a significant theoretical and practical meaning for improving both dynamic and economic performances because it defines the control parameters used for the gearshift decision making and shift timing [1–3]. One gearshift strategy is to establish an optimal control problem to improve the overall performance. In the face of complex road conditions and individual driving requirements, many researchers adopt the method of building an intention recognition model to correct the shift schedule in real time [4,5]. However, the optimal problem cannot be approximated as a continuous one because the gear ratio is discrete, thereby making it a nonlinear problem, which is challenging to solve. Thus, the nonlinear optimization is often time consuming and not suitable for online implementation to a certain extent [6, 7].