Bo-Chiuan Chen

Differential-Braking-Based Rollover Prevention for Sport Utility Vehicles with Human-in-the-loop Evaluations

An anti-rollover control algorithm based on the Time-To-Rollover (TTR) metric is proposed in this paper. A simple model with steering and direct yaw moment control inputs was constructed to calculate the TTR in real-time. The TruckSim dynamic simulation software was used to verify the control performance, as well as to simulate the system dynamics in the UM-Oakland driving simulator. Both the simple and complex (TruckSim) models were tuned to match the behavior of a 1997 Jeep Cherokee vehicle with lateral acceleration up to 0.6 g. The performance of the proposed control system was compared with other threshold-based rollover-prevention control algorithms. Finally, a human-in-the-loop experiment was conducted to study the performance of the proposed algorithm under more realistic driving conditions.

Sideslip angle estimation using extended Kalman filter

Vehicle sideslip angle can be estimated using either dynamic or kinematic models. The dynamic model requires vehicle parameters, which might have uncertainties due to different load conditions, vehicle motions, and road frictions. Parameter uncertainties might result in estimation errors. Thus system identifications are required to estimate those parameters online. On the other hand, the kinematic model does not require these parameters. A closed-loop estimator can be formulated to estimate the sideslip angle using the kinematic model. Since the system matrix which consists of the yaw rate is time varying, the required input vector and output contain process and measurement noises, respectively, and the disturbance input matrix contains estimated states, extended Kalman filter is used to obtain the estimation gain in this paper. CarSim is used to evaluate the proposed approach under different driving scenarios and road frictions in Matlab/Simulink. The preliminary results show promising improvement of the sideslip angle estimation.

Rollover Warning for Articulated Heavy Vehicles Based on a Time-to-Rollover Metric

A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated heavy vehicle. The TTR metric accurately “counts-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately presented. There are two conflicting requirements in the implementation of TTR. On the one hand, a super-real-time model is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the design process is illustrated in an example. First, a simple yet reasonably accurate yaw/roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simple model. The NN takes the TTR generated by the simple model, vehicle roll angle, and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achieved across all the driving scenarios we tested.

Sliding-mode control for semi-active suspension with actuator dynamics

A sliding-mode controller (SMC) is proposed for semi-active suspensions to achieve ride comfort and handling performance simultaneously. First, a nonlinear quarter-car model of Macpherson strut suspension is established in Matlab/Simulink. Constrained damper force and actuator dynamics are considered for the damper model. System identification is applied to the nonlinear model for obtaining the linear model parameters. Kalman filter is designed based on the linear model and the actuator dynamics to estimate the state responses required for SMC. The sliding surface consists of tyre deflection and sprung mass acceleration. The proposed SMC is evaluated using the nonlinear model for both time and frequency domain responses. Robustness due to the increased sprung mass and deteriorated suspension is also investigated in this paper. Preliminary simulation results show improved ride comfort without sacrificing the road holding performance.

Adaptive Idle Speed Control for Spark-Ignition Engines

Due to the nonlinear time-varying nature of the sparkignition 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.

Adaptive Power Split Control for a Hybrid Electric Scooter

An adaptive power split control for a rear-wheel-driven hybrid electric scooter (HES) is proposed in this paper. It is designed using the concept of total equivalent fuel consumption. The equivalence factor is used to transform the electric energy into the equivalent fuel energy and is often selected to be a predetermined function of the state of charge (SOC) of the battery. However, the predetermined function might not be optimal for different driving cycles. An adaptive fuzzy sliding mode controller is used to adjust the equivalence factor according to the SOC deviation. An instantaneous cost function, which consists of the total equivalent fuel consumption, is then minimized to obtain the optimal power split between the internal combustion engine and the electric motor. Deterministic dynamic programming (DDP) is used to offer the performance upper bound to benchmark the proposed control strategy. Preliminary results show that suboptimal fuel economy, which is close to the DDP performance, can be achieved for various driving cycles.

Modeling and Control of Hybrid Electric Motorcycle with Direct-Driven Wheel Motor

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.

Design of an Electric Differential System for Three-Wheeled Electric Welfare Vehicles With Driver-in-the-Loop Verification

Two electric wheel motors are used to propel the rear-driven three-wheeled (3W) electric welfare vehicle independently. Due to the lack of the traditional mechanical differential, the right/left rear tractive forces and the corresponding rotational speeds cannot coordinate with each other automatically while cornering, which might increase the required steering angle and torque. An electric differential system is proposed in this paper to solve this problem. The proposed system controls the rotational speeds of both wheel motors according to different vehicle speeds and steering angles. A 12-degree-of-freedom 3W vehicle model, which is established in AutoSim and verified using the experimental data of the prototype vehicle, is used for the driver-in-the-loop verification in Matlab/Simulink. The preliminary result shows that the proposed system can effectively reduce the steering torques and roll angles during cornering.

Design of lane keeping system using adaptive model predictive control

A lane keeping system using adaptive model predictive control with linear time-variant prediction model is proposed in this paper. First, real-time on-line system identification using recursive least square method is employed to obtain the estimated tire cornering stiffness of the bicycle model. The vehicle velocity within the prediction horizon is predicted using the longitudinal acceleration to obtain the linear time-variant bicycle model. A cost function which consists of the errors between the target trajectory and predicted trajectory, and the steering angles within the prediction horizon is minimized to generate the optimal steering angle command to perform the lane keeping control. For curved road tests with different road frictions and non-constant speed profiles, simulation results show that the proposed control can effectively reduce the lateral displacement error and achieve better lane keeping performance than the conventional model predictive control and the adaptive model predictive control with linear time invariant system.

Design of electronic stability control for rollover prevention using sliding mode control

Electronic Stability Control (ESC) can be used for rollover prevention via coupled yaw-roll dynamics. A three-Degree-of-Freedom (3DOF) yaw plane model is used to design the sliding mode controller. The sliding surface consists of yaw rate following error, sideslip angle and lateral acceleration. Three trigger rules are used to activate the differential braking. CarSim is used to evaluate the proposed approach under Double Lane Change (DLC), FMVSS 126 and NHTSA Fishhook manoeuvres. Robustness of increased CG height, mass and inertia due to multi-passenger loading is also investigated. Simulation results show promising improvement for rollover prevention while meeting the requirement of yaw motion control.