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Vehicle transient motion control plays an important role in reducing energy consumption for both hybrid and electric vehicles since vehicle acceleration and deceleration can be optimized based on driving environment. In this article, nonlinear quadratic tracking (NQT) control is used for optimal acceleration and minimal principle is applied to deceleration to optimize energy recovery, where acceleration control generates the propulsion torque based on the current powertrain status and the error between vehicle speed and given reference provided by the connected system based on the surrounding traffic; and the deceleration (braking) control optimizes regenerative brake to maximize the recovered energy while obeying speed and braking distance constraints. Both control strategies are designed for real-time applications and updated online to respond to the rapid changing traffic environment. Cosimulation models, consisting of SUMO traffic and Simulink control models, are developed for validating the proposed control strategies through computer-in-the-loop (CIL) and hardware-in-the-loop (HIL) simulations. CIL simulation results show that in the eco-acceleration mode, the proposed strategy can save 6.8% energy over the rapid acceleration and brake control recovers 50.8% more energy than the vehicle driven by SUMO simulated human driver. HIL simulations further confirm reduction of energy consumption and adaptability to a changing traffic environment in real-time. [ABSTRACT FROM AUTHOR]

This research investigates the discrete-time adaptive backstepping control to track the reference clutch torque and estimate the clutch touchpoint displacement simultaneously for a transfer case clutch. Note that the clutch touchpoint changes due to the clutch wear, temperature variation, and other factors. As a result, it needs to be estimated in real time for accurate clutch torque control. Instead of using a separate algorithm to estimate touchpoints, this brief proposes to utilize an integrated scheme for both tracking reference torque and estimating varying touchpoints. More specifically, the deadbeat-based control law is designed based on a systematic non-Lyapunov-function-based backstepping scheme. The stability of the closed-loop system is guaranteed, and all closed-loop states are bounded. In addition, due to the deadbeat design, the tracking and estimation convergence rates are faster than or equal to any other backstepping designs. Furthermore, the closed-loop system performance is robust to external disturbances. Finally, the proposed method is validated through simulation studies using experimental data. [ABSTRACT FROM AUTHOR]

The clutch touchpoint of an automotive transfer case is a key parameter for accurate clutch torque control, which varies over time due to clutch wear, clutch plate temperature change, and clutch actuation system part-to-part variation. Typically, the touchpoint displacement is unknown and needs to be estimated online. This article, based on the clutch actuation system model, proposes an adaptive scheme using normalized gradient method to estimate the clutch touchpoint in real time. The proposed algorithm uses only the motor control voltage of the clutch actuator and measured cam position while the clutch is engaged. Simulation studies reveal that clutch actuation system friction plays an important role, leading to the investigation of multiple friction models. Among the Coulomb friction, Stribeck friction and proposed modified general kinetic friction (MGKF) models, the MGKF is proved to be the best in describing the friction in the clutch actuation system. Multiple experimental datasets from proving ground and general driving cycle tests are used to validate the effectiveness of the proposed scheme with the MGKF model. In addition, the proposed adaptive estimation results are compared with that of existing direct-detection method (based on the sudden change in clutch torque or wheel speed), showing improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

Exhaust gas recirculation valves used in internal combustion engines are highly nonlinear due to its nonlinear mechanism and dry friction dominated at low valve speed that could lead to large steady-state displacement errors. A multiple friction factor model is adopted in this article using a switched linear parameter-varying (LPV) modeling approach, where the scheduling parameter is the friction coefficient as a function of valve speed. Switched gain-scheduling LPV controllers are designed based on the hysteresis switching logic with guaranteed ${\mathscr{H}}_{\infty }$ performance, where parameterized linear matrix inequality (LMI) conditions are relaxed to a set of LMI conditions for synthesizing the switched LPV controllers by convex optimization. The LPV control performance is compared with that of proportional–integral–derivative controllers through both simulation and experimental studies, showing a notable improvement in the system response performance and robustness. [ABSTRACT FROM AUTHOR]

Engine electronic throttle control (ETC) is challenging due to its high system nonlinearities and the required fast response time. In this paper, an electronic throttle system was modeled as a linear parameter varying (LPV) system in discrete-time domain, where the vehicle battery voltage is modeled as the measurable time-varying parameter; the nonlinear friction is modeled as a function of the measurable throttle position; and the limp-home spring nonlinearity is compensated through feedforward control. Mixed constrained ${{H_2}/{H_\infty}}$ LPV controller was designed for the LPV throttle control system using the linear matrix inequality convex optimization approach. The system output and control weights were optimized through simulation studies to achieve the best performance; and the finalized LPV controller was experimentally validated on an ETC test bench. Comparing with the baseline well-tuned fixed gain proportional, integral, and derivative controller, the LPV controller reduces the 2% settling time from 0.30 to 0.15 s. Especially, smooth transient response was achieved when the throttle plate crosses the region with heavy spring nonlinearity required by the limp-home operation. [ABSTRACT FROM PUBLISHER]

Hybrid powertrain provides driving torque based upon the vehicle acceleration pedal position. Due to the slow response of the internal combustion (IC) engine, the electric motor(s) (EM) often has to provide additional torque during transient vehicle operations. During cold-start operations, the available battery power is significantly reduced due to cold temperatures, and due to the starting delay of the IC engine, the battery has to provide power at the level beyond its normal operation limit for the electrical motor to meet the torque demand, which would dramatically shorten the battery life. However, if the desired powertrain torque can be predicted, the IC engine could be started early when the predicted future powertrain power is higher than the battery normal operational range, and hence, the duration of battery operated over its normal operational range could be significantly reduced, leading to extended battery life. This makes predicting multistep future torque demand, based upon current and past torque demand data, an important subject. Amid many existing studies, few research studies were focused on this subject. This paper proposes an adaptive recursive prediction algorithm to estimate the future desired powertrain torque and compares the performance of the proposed algorithm with two existing prediction algorithms, i.e., step-by-step and fixed-gain algorithms. These three prediction algorithms are studied under the federal test procedure (FTP) and another four typical driving cycles. The simulation results show that the adaptive recursive prediction algorithm reduces the prediction error significantly with a 4% maximum prediction error, and in addition, the reduced computational load makes it feasible for real-time implementation. The introduced weighting factors for the past and current data are the key to improve prediction accuracy and avoid prediction calculation over- and underflow. In addition, due to the online update of regression gain, the proposed algorithm is also robust to driving behavior variations. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology of calculating the optimal torque and speed commands for the engine–generator system of a series hybrid electric vehicle (HEV). In series HEVs, the engine–generator subsystem provides electrical energy to the dc link. This paper proposes an optimal control strategy of the engine–generator subsystem to generate a desired amount of energy within a given period of time. The optimization algorithm, based on trajectory optimization, determines the torque and speed reference signals for the engine–generator subsystem that achieve maximum efficiency. A simplified version of the controller is also presented for online implementation. The proposed control strategy is compared with nonoptimized control techniques, and simulation results show the improvements in energy efficiency. [ABSTRACT FROM PUBLISHER]