Energy management and torque coordination control for plug-in 4WD hybrid electric vehicle

Abstract: This paper focuses on the control strategy of a plug-in 4-wheel-drive (4WD) hybrid electric vehicle (PHEV). To overcome the defects of the traditional proportion-integration-differentiation (PID) control method, an algorithm based on an adaptive fuzzy PID control method which provides better dynamic and static performances for the vehicle was adopted and a driver model was established using this algorithm. The input of the driver model was the difference between the cycle velocity and the actual output velocity of the vehicle. The output of the driver model was the required torque coefficient which reflects the driver's intention and thus can be used to calculate the actual required torque of the driver. The PID parameters can be revised real-time according to the change of the cycle conditions, and the principle to choose theses parameters to ensure the stability of the controller was introduced as well. The domain of discourse for the inputs and outputs of the fuzzy PID controller and their membership functions were analyzed and parts of the fuzzy rules were provided. The energy management control strategy based on engine optimal torque was adopted in order to improve the fuel economy of the vehicle. Because there was little possibility that the engine could drive the vehicle alone with the optimal engine output torque control strategy, and the general efficiency for the series mode was relatively low, the drive modes of the vehicle were only classified into four modes, including EV (electric vehicle) mode, parallel mode, 4WD mode, and E_charge (engine drives and charges the battery) mode. Mode judging rules and torque distribution methods were described, and a state-flow model in the paper was used to illustrate the energy management of the vehicle. In addition, a torque coordination control strategy based on "engine speed regulation+clutch fuzzy PID control+ engine dynamic torque lookup+2 motor compensation" was proposed. The engine dynamic torque related to the engine speed, throttle opening and its change rate were obtained by experiments, and they were fitted into a more detailed table through MATLAB programming. Aiming to have a more precise output oil pressure of the clutches, the two clutches were controlled by the combination of two fuzzy controllers and an adaptive fuzzy PID controller, and then a more reliable output of the required torque was obtained. One of the fuzzy controllers was used to calculate the oil pressure increment in the clutch, and the other was for the change rate of the original oil pressure. The fuzzy PID controller which was adaptive to different drive cycles was used to more accurately calculate the final oil pressure. The torque coordination control strategy was introduced by taking the transition between EV mode and parallel mode as an example. The detailed transition procedures were briefly introduced. The control strategy of the vehicle was simulated using hardware-in-loop(HIL) based on dSPACE with the cycle of 2*NEDC (which consists of two new European driving cycles) and the research results which include the output of the power components, SOC of the battery pack, and the velocity error which was reduced by 37.1% before and after the application of adaptive fuzzy PID indicate that the control strategy realized the basic energy management of the vehicle, and the jerk after the application of torque coordination control was reduced by 47.5% because of the coordination of the power components during mode transitions, and the adaptive fuzzy PID control of the two clutches. The control effectiveness of the control strategy was validated in this paper and it is of significance for controlling similar complicated hybrid systems.