Search Results

A model-based sensor fault detection algorithm is proposed in this paper to detect and isolate the faulty sensor. Wheel speeds are validated using the wheel speed deviations before being employed to check the sensor measurements of the vehicle dynamics. Kinematic models are employed to estimate yaw rate, lateral acceleration, and steering wheel angle. A Kalman filter based on a point mass model is employed to estimate longitudinal speed and acceleration. The estimated vehicle dynamics and sensor measurements are used to calculate the residuals. Adaptive threshold values are employed to identify the abnormal increments of residuals. Recursive least square method is used to design the coefficients of the expressions for adaptive threshold values, such that the false alarms caused by model uncertainties can be prevented. Different combinations of estimations are employed to obtain 18 residuals.

An accurate estimation of the state of charge (SOC) is necessary not only for optimal energy management but also for protecting the lithium-ion batteries (LIB) from being deeply discharged or overcharged. In this paper, an equivalent circuit model (ECM) is established to simulate the dynamic behavior of LIB. Parameters of internal resistance, diffusion resistance and diffusion capacitance are identified using the recursive least square method. Because open circuit voltage (OCV) and SOC have an obviously nonlinear relationship, an extended Kalman filter is proposed to estimate the SOC based on the ECM model. Local linearization is employed to approximate the nonlinear SOC-OCV curve by a straight line with the slope and intersection around the operating point. Simulation results show that the estimation error of the proposed algorithm is less than 5% for the test patterns.

This paper develops an adaptive idle speed control strategy for a V2, 1000 cc four-stroke, water-cooled, port injection SI engine. In order to verify the proposed strategy, the non-dimensional engine model including charging and torque dynamics is established in Matlab/Simulink software based on previously experimental verification. The integration of dynamics above will be a multi-input-single-output (MISO) system, which inputs are throttle angle and spark advance angle, and the output is engine speed. The proposed adaptive controller is developed on the model-based structure. The system parameters are updated by recursive least square (RLS) method so the system is able to represent the actual operation. The updated system parameters adjust control gain by derivation of closed-loop gain and pole placement.

In this paper, the off-line analysis method is applied to develop three-way catalytic converter (TWC) diagnostic strategy including oxygen sensor (O2 sensor) diagnostic strategy with common narrow band oxygen sensor (EGO sensor) in Matlab/Simulink software. Moreover, Mototron ECU (Engine Control Unit) is used for monitoring catalyst deterioration. However, aged catalyst is difficult to possess so the different catalytic conversion efficiency (CCE) is simulated by using exhaust by-pass valve. The CEC (China Engine Corporation) 1L V2, four stroke, water-cooled engine is selected as target engine to conduct engine dynamometer test. After making sure O2 sensor operates normally, the catalyst diagnostic strategy test is executed. The experimental result shows that the catalyst deterioration accurately can be detected when the failure phenomenon takes place.

Fuel film dynamics in the intake manifold are considered to develop air fuel ratio (AFR) control strategy with on-line system identification for a V2 engine in this paper. A1000 cc four-stroke two-cylinder, water-cooled port injection SI engine is used as the target engine to develop the engine model in Matlab/Simulink. The model which consists of charging, fueling, combustion, friction, and engine rotational dynamics is used to verify the proposed AFR control. Since the fuel film dynamics changes with different engine operating conditions, the fuel film parameters are often listed as look-up tables for fuel film dynamics calculation in the conventional AFR control. However, those parameters might be inaccurate during transient engine operation. Different intake port temperature will affect the accuracy of those fuel film parameters as well. In order to solve this problem, recursive least square (RLS) is used to identify those parameters on-line.

This paper develops an engine model for the model-in-the-loop (MIL) and hardware-in-the-loop (HIL) application to shorten the time duration and reduce the costs of developing and verifying the engine management system (EMS). The target engine is a 1.0L V-type two cylinder water-cooled spark-ignition engine. The engine model is developed using a so-called modulization method, which includes to: (1) separate the sub-models according to the different physical phenomena; (2) collect the sub-models to establish a library; (3) execute the component modules based on a pre-determined sequence by a more flexible way. The engine model is then applied in MIL structure for testing and verifying the control strategies in the developed EMS. After all strategies are verified, the HIL structure is constructed by a hardware controller and a virtual engine in the xPC target. The execution time-step of engine model is analyzed to keep enough accuracy and numeric stability for real-time simulation.

Time-to-collision (TTC) can be used to design the forward collision warning system (FCWS). Conventional TTC is defined as the relative distance divided by the relative velocity. It might result in estimation errors for maneuvers with non-zero relative accelerations. In order to improve the accuracy of TTC estimation, we consider the relative acceleration and extend the region of interest for FCWS from the main lane to adjacent lanes. If we assume constant relative acceleration within the sample time, the relative distance can be approximated using a second order polynomial. Recursive least square technique is employed to estimate associated coefficients. Variable forgetting factor adjusted using relative acceleration is proposed to enhance the approximation accuracies. Similar process is applied to estimate the coefficients of a second order polynomial for the relative polar angle.

Engine control units manage various conditions in an operating engine, including fuel injection, spark ignition and valve timing, in order to achieve the goals of high performance, high fuel efficiency and low emissions. Typically, engine models are necessary for developing engine control systems. Most mean value engine models (MVEM) are based on empirical volumetric efficiency, which contributes to calculating intake air flow rate. Therefore, they are not capable of simulating changes in valve lift and valve timing, and cannot be used for a variable valve train (VVT) engine. A method of calculating intake air flow rate with variable valve lift and valve timing is needed to adapt to the demands on VVT engine models. An engine model is proposed that focuses on a charging model, developed by using a filling-and-emptying model to simulate the air exchange in an engine, including intake- and exhaust-air flows.

The phenomenon of fuel-film dynamics for four-stroke small-scale spark-ignition engines is investigated in this paper. A first-order fuel-film model, so-called tau-x model, is used to represent the fuel dynamics. The parameters of fuel-film model, which consists of the portion of fuel that deposited on the manifold wall and the time constant of the fuel evaporation process, are identified using the recursive least squared technique. Performances of the proposed algorithm are evaluated using a nonlinear engine model in Matlab/Simulink. The preliminary simulation results show that the proposed algorithm can accurately be used to identify the parameters of fuel-film model. The experimental data are then utilized to study the characteristics of fuel-film dynamics, and show that the fuel-film dynamics is significantly affected by engine speed, throttle opening, injection timing, and intake temperature.

This paper constructed a parallel type hybrid-electric bus model based on GM-Allison system which is consisted of three planetary gear-sets and two motors. The simulation model is modified from the backward-looking simulation of ADVISOR. The hybrid-electric bus (HEB) is simulated to compare with a conventional diesel bus. Four different driving cycles are employed: Taipei City Bus Cycle, New York Bus Cycle, Manhattan Cycle, and Central Business District Cycle. Three different control strategies, namely speed control, torque control, and power control are studied. The simulation results show that the GM-Allison hybrid-electric bus with good control strategy can improve fuel economy by 66% compared to a conventional diesel bus. As for exhaust emissions, CO, HC, NOx and PM are reduced by 83%, 59%, 66% and 62%, respectively.

A heat transfer model for small-scale spark-ignition engines has been proposed by authors in previous study. However, this model is only based on single engine, it may not be suitable for the others. In order to improve the accuracy of predicted heat transfer rate for different small-scale engines, another heat transfer model using Stanton number based on two engines is proposed. Prediction results of instantaneous heat flux and global heat transfer based on the proposed model are compared with the experimental results and prediction results of previous model. It is found that the proposed model has prediction results closer to the measured data than the previous models.

Steering wheel might not be able to return to the center position due to friction torque in the steering mechanism. A return control strategy for the electric power steering using sliding mode approach is proposed in this paper. The sliding mode controller (SMC) is designed based on the rotational dynamics of the steering column. The uncertainties of the system parameters and friction torque are included in the control discontinuity. A trigger rule is also proposed to switch between assist and return controls. Preliminary simulation results show that the proposed algorithm can not only improve the returnability, but also provide the damping compensation.

Due to the nonlinear time-varying nature of the spark-ignition engine, an adaptive multi-input single-output (MISO) controller based on self-tuning regulator (STR) is proposed for idle speed control in this paper. The spark timing and idle air control are simultaneously employed as control inputs for maintaining the desired idle speed, and are designed based on P and PI type STR, respectively. The Recursive Least Square technique is employed to identify the engine as a first-order MISO linear model. Pole placement technique is then used to design the adaptive MISO controller. Performances of the proposed algorithm are evaluated using a nonlinear engine model in Matlab/Simulink. The system parameters with 10% uncertainties are also utilized to perform the associated robustness analysis. Preliminary simulation results show significant reduction of speed deviations under the presence of torque disturbances and model uncertainties.

In order to reduce engine control strategy development time and cost, the Hardware-In-the-Loop (HIL) simulation technology is developed. This paper establishes a simulation model and control platform based on the HIL structure for studying control strategy development and verification. A 125c.c. engine model verified by the experimental data is established in Matlab/Simulink, which is used as a virtual engine and then implemented in the xPC real-time system. The Motorola MC 68376 controller chip provides control signals included injection duration/timing and ignition timing for the virtual engine. A PCI-6024E input/output board is used as an interface between the controller and the virtual engine. A simulation model, which consists of engine, powertrain, tire, and pitch plane dynamics, is used to evaluate the response of engine dynamics and longitudinal dynamics via HIL simulation.

In order to study the noise effect of the crank angle sensor on electronic fuel injection (EFI) control system, a Kalman filter with stroke identification is employed to estimate the crankshaft rotational dynamics. Estimated crank angle and speed are then used for EFI system. A 125 c.c. scooter engine verified by the experimental data is used to design the Kalman filter. A simulation model, which consists of nonlinear engine dynamics, powertrain dynamics, tire dynamics, and pitch-plane motorcycle dynamics, is established in Matlab/Simulink to evaluate the performance of the Kalman filter at various noise conditions.

A Hybrid Electric Motorcycle (HEM) with a variable-winding brushed DC wheel motor is proposed in this paper. When the windings are connected in parallel, the motor has wide speed range suitable for high-speed cruising. When the windings are connected in series, the motor can provide high starting torque and thus reduce the necessary engine size for the proposed HEM. The optimal upper and lower power limits of the engine operating region are obtained from the optimal design theory. A dynamic simulation model is established in Matlab/Simulink to evaluate the performance of the proposed HEM and traditional motorcycles using the ECE40 driving cycle.

Electronic Control has been widely used in automotive systems, such as Vehicle Dynamics Control, Anti-Lock Braking Systems, Active Suspension and Steering System control, etc. The rapid prototyping development of an embedded controller for an electronic power assisted steering system (EPAS) will be presented in this paper. A steering model is established in MATLAB/SIMULINK to verify the proposed EPAS in this paper. This model includes the effects of kingpin inclination, caster, camber and lateral offset to calculate the total steering torque. The EPAS control strategy has considered the driver commands and vehicle speed to provide multiple stages of steering torque assistance. TMS320F240 DSP is used as the embedded controller of EPAS. EPAS Control algorithms have been designed using MATLAB/SIMULINK/Real-Time Workshop and Input/Output of Blocksets of TMS320F240 DSP have been developed in SIMULINK.

A Hybrid Electric Motorcycle (HEM) with a direct-driven wheel motor is proposed in this paper. The rear wheel is driven by an internal combustion engine and a powertrain system of a traditional motorcycle with minor modifications. The front wheel is driven by a direct-driven wheel motor. The proposed HEM is a parallel configuration. Both wheels can supply tractive forces simultaneously to drive the motorcycle when necessary. A rule-based structure is used to design the power split controller of the proposed HEM. Fuel economy of the proposed design will be evaluated by a dynamic simulation model in Matlab/Simulink using ECE-R40 driving cycle.

For a conventional scooter engine, not only the crankshaft position estimation is insufficient based on the one-tooth crankshaft wheel, but also the speed measurement might be contaminated by sensor noise easily. The authors propose a technique using Kalman filter to estimate crankshaft position and engine speed for digital ignition control of a scooter engine. A 125cc engine model, which is verified by the experimental data of the target engine, will be used to design the Kalman filter. A simulation model, which consists of nonlinear engine dynamics, powertrain dynamics, tire dynamics, and pitch plane dynamics, is used to evaluate the performance of proposed estimation algorithm with different tooth numbers of crankshaft wheel and various noise conditions.