CAJ | 학술논문

为克服传统比例-积分-微分（proportion integration differentiation，PID）以及模糊逻辑算法的缺陷、保障汽车经济性并改善乘员的乘坐舒适性，该文采用自适应模糊PID算法，建立了驾驶员模型。使用基于发动机输出转矩最优的能量管理控制策略，简述了驱动模式判别条件及转矩分配方法。提出1种“发动机调速+离合器模糊PID控制+发动机动态转矩查表+双电机转矩补偿控制”转矩协调控制方法，简述了模式切换步骤。在dSPACE实时仿真系统上对控制策略进行了硬件在环仿真。仿真结果表明，该控制策略在能量管理方面控制效果良好，动力部件的输出与控制策略完全吻合且平均车速误差下降37.1%。引入转矩协调之后，整车最大冲击度下降47.5%。该文的研究方法可以为制定复杂混合动力系统的控制策略提供参考。
위극복전통비례-적분-미분（proportion integration differentiation，PID）이급모호라집산법적결함、보장기차경제성병개선승원적승좌서괄성，해문채용자괄응모호PID산법，건립료가사원모형。사용기우발동궤수출전구최우적능량관리공제책략，간술료구동모식판별조건급전구분배방법。제출1충“발동궤조속+리합기모호PID공제+발동궤동태전구사표+쌍전궤전구보상공제”전구협조공제방법，간술료모식절환보취。재dSPACE실시방진계통상대공제책략진행료경건재배방진。방진결과표명，해공제책략재능량관리방면공제효과량호，동력부건적수출여공제책략완전문합차평균차속오차하강37.1%。인입전구협조지후，정차최대충격도하강47.5%。해문적연구방법가이위제정복잡혼합동력계통적공제책략제공삼고。
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.