The electric vehicle (EV) is the most exciting object to apply "advanced motion control" technique. As an EV is driven by electric motors, it has the following three remarkable advantages: 1) motor torque generation is fast and accurate; 2) motors can be installed in two or four wheels; and 3) motor torque can be known precisely. These advantages enable us to easily realize: 1) high performance antilock braking system and traction control system with minor feedback control at each wheel; 2) chassis motion control like direct yaw control; and 3) estimation of road surface condition. "UOT Electric March II" is our novel experimental EV with four in-wheel motors. This EV is made for intensive study of advanced motion control of an EV.

Future vehicle driven by electricity and Control-research on four-wheel-motored "UOT electric march II"

Independent wheel torque control of 4WD electric vehicle for differential drive assisted steering

In this paper, novel direct yaw moment control and anti-skid control are proposed for electrical vehicles with two in-wheel motors. The proposed controllers are composed of double disturbance observers. The inner-loop observer controls the vehicle traction, and outer-loop observer stabilizes the yawing motion. The advantages of these approaches are 1) the stability robustness for road condition is guaranteed and 2) the proposed controllers require no immeasurable parameters. The experiments demonstrate the performance of these controllers under snowy conditions, and the stability is theoretically analyzed as inertia variation and dead-time system.

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Motion stabilization control of electric vehicle under snowy conditions based on yaw-moment observer

A control algorithm is proposed for an independent motor-drive vehicle. A sliding-mode control is designed based on the slip ratio, yaw rate, and lateral acceleration of the vehicle to reduce the motor torque at each wheel. The performance of the control algorithm is evaluated by MATLAB/Simulink-CarSim cosimulation and experiments. A test car with two independent drive motors at the rear wheels is developed. From the simulation and vehicle test, it is found that the control algorithm improves vehicle performance.

Control Algorithm for an Independent Motor-Drive Vehicle

In this paper, novel direct yaw-moment control (DYC) with road condition estimation and anti-slip control is proposed for electric vehicles. An inner-loop observer controls the vehicle traction, and an outer-loop controller stabilizes the yawing motion. An immeasurable parameter known as cornering stiffness is estimated from the detected yaw-rate, steering angle, wheel speed and yaw-moment observer output in real time. Thus, the accurate control input can be generated with the estimated parameters. The proposed adaptive control is compared with a conventional robust control on dry and snowy terrain conditions. The experimental results show that the yaw-rate error is properly attenuated by the proposed control algorithm.

Direct yaw-moment control of electric vehicle based on cornering stiffness estimation

In this paper we propose an optimal slip ratio estimation method based on fuzzy inference. One of the major advantages of electric vehicles is the quick and precise torque response of the electric motor, which realizes a novel traction control system. To prevent skidding, optimal slip ratio control has been successfully developed. It maintains the slip ratio at the optimum value that gives the maximum driving force. The remaining problem is how to generate the optimal slip ratio command sent to the controller. First we show that effective estimation of the optimal slip ratio is difficult to perform by the simple gradient method, which is a well-known optimization method. But various experimentally obtained data can be easily incorporated into fuzzy inference, and therefore its estimation performance can be easily improved by the accumulation of human experience. This is a major advantage in the nonlinear estimation of real road-tire characteristics. The effectiveness of the proposed estimation and control methods is confirmed by numerical simulation. © 2001 Scripta Technica, Electr Eng Jpn, 135(3): 56–63, 2001

Optimal slip ratio estimator for traction control system of electric vehicle based on fuzzy inference

A novel wheel skid detection method without chassis velocity for electric vehicle (EV) is proposed. The proposed method can sense the skid with only the values of current and rotating velocity of the motor. The output torque of the motor is easily comprehensible, and the proposed method utilizes this advantage of EV. Estimation of vehicle chassis velocity or wheel absolute velocity is not required. The experiments were carried out with laboratory-made electric vehicle “UOT Electric March I”, and the results show the effectiveness of this method. We also carried out some simulations to discuss how to prevent the wheel skid with this skid detector.

http://hflab.k.u-tokyo.ac.jp/hori_lab/paper_2004/paper/EV/sakai/SakaiJSAE2000_10.pdf

Novel skid detection method without vehicle chassis speed for electric vehicle

Motion Control of Electric Vehicle Utilizing Fast Torque Response of Electric Motor

In this paper, we propose the optimal slip ratio estimation method based on Fuzzy Inference. One of the remarkable advantages of electric vehicle (EV) is the quick and precise torque response of electric motor, which realizes a novel traction control system (TCS). To prevent wheel skid phenomena, the optimal slip ratio control has been successfully developed. It maintains the slip ratio to the optimal value which gives the maximum driving force. The remaining problem is how to generate the optimal slip ratio command to the controller. First, we indicate that the effective estimation of the optimal slip ratio is difficult for a simple gradient method, which is one of the well-known optimization methods. Various knowledge obtained by experiments can be easily installed into Fuzzy Inference. Therefore, its estimation performance can be easily improved by accumulation of human experiences. This is a remarkable advantage in nonlinear estimation of actual road-tire characteristics. The effectiveness of the proposed estimation and control methods is confirmed by numerical simulation.

https://www.jstage.jst.go.jp/article/ieejias1987/120/4/120_4_581/_pdf

Optimal Slip Ratio Estimator for Traction Control System of Electric Vehicle Based on Fuzzy Inference

Novel wheel skid detection method for electric vehicle (EV) is proposed and experimentally evaluated. This proposed method can detect the skid of wheel with only the values of current and rotating velocity of each motor. Estimation of vehicle chassis velocity or wheel absolute velocity is not required any more, and this is a remarkable advantage over conventional methods for anti skid brake system (ABS) or traction control system (TCS). In this proposed method, skid is detected with the dependence of traction force on motor torque. Traction force is estimated with simple disturbance observer. Therefore, the exact value of shaft input torque is required in this method. It is quite easy for EV, but not so for internal combustion vehicle (ICV). Proposed algorithm is examined with experimental results with laboratory-made electric vehicle “UOT Electric March-I”. These experimental data show the effectiveness of our new skid detection method. Another simulation results show that the skid prevention can be achieved with proposed skid detection method.

https://www.jstage.jst.go.jp/article/ieejias1987/120/2/120_2_281/_pdf

Hideo Sado

Papers

Optimal slip ratio estimator for traction control system of electric vehicle based on fuzzy inference

Novel skid detection method without vehicle chassis speed for electric vehicle

Motion Control of Electric Vehicle Utilizing Fast Torque Response of Electric Motor

Optimal Slip Ratio Estimator for Traction Control System of Electric Vehicle Based on Fuzzy Inference

Novel Wheel Skid Detection Method without Chassis Velocity for Electric Vehicle