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A number of transport vehicles have been fitted with electric traction motors and their inverters. The traction motor is required to continue to operate even if the inverter is under fault conditions. We propose a motor with two controllable rotors for a traction motor, which consists of 2 rotors and 1 stator. In this paper, we propose a fault-tolerant control method for a two-controllable-rotor motor using a 6-phase inverter. The operational principle of the two-controllable-rotor motor is described. The mathematical model under fault tolerant control is shown, and a fault-tolerant control method for the two-controllable-rotor motor is described. The proposed fault tolerant control method is verified by conducting finite element analysis and carrying out measurements on a prototype.

The use of green light optimal speed advisory (GLOSA) systems is seen as a key application for achieving more environmentally friendly, time-efficient, and safer traffic flows at signalized intersections. In previous papers, the authors have proposed a GLOSA system that informs drivers of the most appropriate position for their vehicle instead of their optimal speed. This paper reports on a performance evaluation of our proposed GLOSA system after application to morning rush-hour traffic flow in a simulation of a real-world signalized intersection. A performance evaluation of this numerical simulation showed that our GLOSA system increased the time headway of vehicles and decreased their deceleration rates in the vicinity of the signalized intersection. In addition, the use of the system moderately increased fuel efficiency without affecting vehicle travel time. From these results, it can be concluded that our proposed GLOSA system has the potential to create safer traffic flows in real-world signalized intersections without degrading traffic efficiency.

A number of automobiles have been fitted with electric motors and their inverters. In order to reduce the space of the motors, we have proposed a 2-controllable-rotor motor which consists of 2 rotors and 1 stator. However, this motor is driven by a 6-phase inverter, and the inverter size is almost the same as that for 2 motors driven by 3-phase inverters. In this paper, we propose a 2-controllable-rotor motor driven by a 5-phase current, and its operation principle is described. Furthermore, its characteristics are investigated using finite element analysis.

This paper proposes a dynamic simulation method of a magnetic gear using its torque map. The phase angle difference between the input and output rotors depends on a load of the output rotor. The load contains an external load and brake torque due to internal mechanical and iron losses. In this situation, the dynamic performance of a magnetic gear is computed by a transient analysis using a motion equation, and the computation cost is large. In order to reduce the computation cost, a dynamic simulation method using a torque map is proposed and discussed.

Early brake pedal operation and corresponding vehicle deceleration are crucial to mitigating rear-end collision risks. In this paper, a mathematical model, hereafter referred to as the deceleration intention inference system (DIIS), was developed to facilitate determining the intent inferences of driver deceleration behaviors. More specifically, a conventional neural network model was integrated into an unscented Kalman filter in an effort to describe the deceleration intentions that can be expected to occur a few seconds later. The numerical examples provided herein show that our proposed model is capable of inferring driver intentions more precisely than the conventional approach.

Most of electric machines have been fitted with a number of electric motors and their inverters. In order to reduce the space of the motors and inverters, we proposed a motor with two independently controllable rotors using a 6-phase inverter. However, winding factors of 3- and 6-phase rotors were 0.87 and 0.50, respectively, in a conventional winding arrangement. In order to increase the winding factor of the motor, this paper proposes a novel winding arrangement, and its effectiveness is verified using finite element analysis.

The use of green light optimal speed advisory (GLOSA) systems is seen as a key application for achieving more environmentally friendly, time efficient and safer traffic flows at signalized intersections. However, while previous GLOSA systems provide optimum speed advice to drivers, we have proposed a GLOSA system concept that informs drivers of the appropriate position of their vehicle instead of their optimal speed. This paper reports on a performance evaluation of our proposed GLOSA system using information distance variations. Information distance is defined as the distance between the subject vehicle and the closest traffic signal where the GLOSA system first becomes active. Traffic simulator experiments showed that the earlier activation of the GLOSA system is particularly effective when the approach traffic demand is relatively lower. In other situations, the partial assistance (PA) mode, in which the GLOSA system is activated only when the signal is red, performed well.

Electric vehicles are equipped with a number of electric motors and their inverters. In order to reduce the space of the motors, we have proposed a motor with two concentrically-arranged rotors which can be independently controlled, and a common outer stator. However, the previous motor was difficult to be practically used due to its complicated support structure. Therefore, we proposed an improved motor with two axially-arranged rotors. In this paper, the static and dynamic characteristics of the motor are computed and verified by carrying out measurements on a prototype. Both results were in good agreement, and the effectiveness of the proposed motor was shown.

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications have the potential to not only accelerate progress towards autonomous vehicles but also to facilitate the achievement of more appropriate driver behavior patterns and thus make traffic more energy efficient and environmentally friendly. This paper presents a driving advisory system for vehicles approaching traffic lights that is based on the use of a Green Light Optimal Speed Advisory (GLOSA) system and evaluates its impact on traffic flow characteristics. This system, which assumes the existence of a connection with the traffic light ahead, calculates the distance required to pass through the intersection without stopping during the remaining green signal time. The required distance is then projected onto the road surface as a green planar rectangle shape through a head-up display (HUD). As long as the vehicle is traveling on the green rectangle, the drivers will not need to make a complete stop or accelerate again in order to meet the onset of a green signal. Assuming that all vehicles are equipped with our proposed system, traffic simulations were carried out to evaluate the performance of the scheme. Numerical analyses showed that a higher fuel efficiency and lower carbon dioxide (CO 2 ) emissions were achievable, even under high-density traffic conditions, by combining the full and partial assistance levels of our proposed system.

The use of Green Light Optimal Speed Advisory (GLOSA) systems is currently considered one of the key applications for achieving more stable, environmentally friendly, and efficient traffic flows in the vicinity of signalized intersections. This paper addresses a driver advisory system that provides an efficient driving strategy based on our GLOSA system and evaluates its impact on traffic flow characteristics. Assuming five levels of traffic demand, traffic simulations were carried out to investigate the performance of the system in terms of the travel time, fuel consumption, and carbon dioxide (CO2) emissions of vehicles entering and exiting an artificial corridor. Our numerical analysis showed that our GLOSA system performs well in traffic flows where the arriving demand is less than 400 to 500 vehicles per hour. In other situations, it increases congestion, impedes efficiency, and significantly worsens the environment.

Rear-end collisions in longitudinal car-following situations occasionally cause severe multiple collision accidents, especially in high-density and high-speed traffic situations. However, if a preceding car driver's deceleration intention could be inferred in advance, even a second earlier, the risk of a collision could be decreased not only between vehicle pairs but also for car platoons. This paper reports on the application of our previously reported intent inference system to a platoon of vehicles and evaluates its impact on safety. The system utilizes an unscented Kalman filter (UKF) and attempts to infer the intent of a vehicle ahead 1.5 s in advance of its driver's most likely action. The inferred intention is then provided to the driver of the assisted vehicle through an interface based on a windshield display, and earlier experiments have proved that the system is quite effective for collision risk mitigation. In this paper, we report on an investigation into the safety impacts of our system on a platoon of vehicles following an assisted vehicle. The results of a traffic simulator experiment showed that the system could provide significant safety benefits to multiple cars traveling behind an assisted vehicle in a vehicle platoon.

A current superimposition variable flux reluctance motor for a traction motor of electric vehicles and hybrid electric vehicles are proposed. This motor can be driven using a 6-phase asymmetric inverter which is used to drive a switched reluctance motor. However, it is difficult to obtain a high efficiency in a low-speed and high-torque region. In order to solve this problem, a switching operation between the current superimposition variable flux reluctance motor and a switched reluctance motor is proposed in this paper.

The electrification of automobiles has been gaining momentum recently, and more and more automobiles are being fitted with electric motors and their inverters. In order to reduce the space of the motors and inverters, a motor with two controllable rotors was proposed [1- 2]. This motor has two rotors and one stator. The two rotors can be independently controlled using 3- and 6-phase superimposed currents created by a 6-phase inverter. One rotor is driven by a 3-phase current and the other is driven by a 6-phase current. However, this motor has problems such as low heat dissipation and complicated support structure of the stator because the stator is sandwiched between the two rotors. In order to solve these problems, a motor with its stator on the outside of its rotors was proposed [3]. This motor has also two rotors and one stator, and the two rotors can be independently controlled using 3- and 6-phase superimposed currents. However, this motor has also a problem of a complicated support structure of the middle rotor. Therefore, we proposed a novel motor with two controllable rotors to solve above problems [4]. In this paper, 3D-FEM analysis coupled with vector control is conducted in order to verify that the two rotors can be independently controlled.

This study applies a particle filter (PF) and an unscented Kalman filter (UKF) to estimate the headway and velocity of a six- vehicle platoon system. These two feedback estimators were used to estimate headway and velocity indirectly from several measurement variables, such as acceleration rate and velocity, of selected vehicles in the platoon. To evaluate the performance of the proposed two estimators, artificial car-following data were created to cover various speed ranges that include some acceleration and deceleration scenarios. Also, a comparison of estimation accuracy is conducted when varying the number of probe cars installed in the platoon system. Numerical analysis showed that the PF succeeded in estimating headway and velocity more accurately than the UKF, even when the number of probe cars installed is fewer and their location is varied within the platoon. The estimations by the UKF were inaccurate and the filter was unstable during all probe car penetrations except during the 100% installation scenario. The UKF is considered to yield stable and accurate estimates only when all vehicles are equipped with the sensing system, whereas the PF does not require numerous probe cars to generate accurate estimates regardless of their location in the platoon.

This paper proposed the unscented Kalman filter (UKF) in which the state equations are defined by trend, car-following and artificial neural network models to estimate the headway and velocity of platooning vehicles. Based on the estimates, the authors then attempt to predict the driver deceleration intent using a conventional car-following model. The UKF integrated with the car-following model is the best among the other models, but it still needs some improvement to maximize the prediction accuracy.

This paper focused on the remaining time to brake pedal operation (TTBP) and proposed the unscented Kalman filter (UKF)- and particle filter (PF)-based models to estimate the TTBP prior to the actual brake pedal operation. The state and measurement equations were simply modeled by partially using an ANN model. Also, the three-vehicle convoy data collected through a driving simulator (DS) experiment showed that the first car started decelerating and shortened the headway two to three seconds before the brake pedal operation. This made sure that the proposed UKF and PF could be applied to the estimation of TTBP by simply measuring the velocity and headway of the preceding cars.

The availability of turning movement data at intersections is essential in carrying out traffic simulations with software such as NETSIM. However, data collection is very time-consuming even for a small network. To estimate turning movements at intersections, a logit-based stochastic user equilibrium (SUE) model was integrated into a genetic algorithm. Three derivative models were developed: ( a) a doubly constrained estimator, ( b) a singly constrained estimator, and ( c) an origin-destination (O-D)-based estimator. In the first and second estimators, the SUE model was formulated as a joint trip distribution and traffic assignment (TD-TA) program, whereas in the third estimator, the SUE model was based on a standard O-D distribution program. These turning movement estimator models were examined by applying them to three road networks: a virtual road network with simulated data, a small road network in the field with data obtained by manual counting, and a road network with data measured with traffic detectors. Application of the models to the virtual road network proves that if prior information on the O-D distribution is available, the O-D-based estimator is most effective in estimating turning movements. Application of the models to real networks for which no prior information on the O-D flows is available shows that the doubly constrained TD-TA-based model is the most accurate and efficient. The turning movements on major links estimated with this model were in relatively good agreement with those actually measured. The correlation coefficient of link flow exceeded 0.90. However, the rationale of the range of unknown variables remains unresolved.

Traffic information from probe vehicles has great potential for improving the estimation accuracy of traffic situations, especially where no traffic detector is installed. A method for dealing with probe data along with conventional detector data to estimate traffic states is proposed. The probe data were integrated into the observation equation of the Kalman filter, in which state equations are represented by a macroscopic traffic-flow model. Estimated states were updated with information from both stationary detectors and probe vehicles. The method was tested under several traffic conditions by using hypothetical data, giving considerably improved estimation results compared to those estimated without probe data. Finally, the application of the proposed method was extended to the estimation and short-term prediction of travel time. Travel times were obtained indirectly through the conversion of speeds estimated or predicted by the proposed method. Experimental results show that the performance of travel-time estimation or prediction is comparable to that of some existing methods.

高速道路上の起終点 (OD) 旅行時間を推定する新手法を提案した. 各道路区間の交通状態を感知器からのフィードバック作用により推定し, 区間旅行時間の時空間的な合計によってOD旅行時間を推定した. 筆者らは現在までニューロカルマンフィルタ (NKF) を用い, 交通状態を推定せずにOD旅行時間を直接推定する手法を提案してきた. 今回既存のNKFによる手法と新提案手法を用いてOD旅行時間を推定した結果, 新提案手法が精度の高い推定を行うことを確認した. さらに提案手法を実際の高速道路単路部へ適用した結果, 密に感知器を設置することにより混雑状態を含めた広範な交通状態の推定が可能となる見通しを得た. これらの数値計算結果から, OD旅行時間推定に対する提案手法の実用化へ向けての課題を整理した.

A model was formulated for estimating dynamic origin-destination (O-D) travel time and flow on a long freeway with a neural Kalman filter originally developed by the authors. The model predicts O-D travel times and flows simultaneously by using traffic detector data such as link traffic volumes, spot speeds, and off-ramp volumes. The model is based on a Kalman filter that consists of two equations: state and measurement. First, the state and measurement equations of the Kalman filter were modified to consider the influence of traffic states for some previous time steps. Then artificial neural network models were integrated with the Kalman filter to enable nonlinear formulations of the state and measurement equations. Finally, a macroscopic traffic flow simulation model was introduced to simulate traffic states on a freeway in advance and predict traffic variables such as O-D travel times, link traffic volumes, spot speeds, and off-ramp volumes. The new model was compared with a regression Kalman filter in which the state and measurement equations are defined by regression models. The numerical analysis indicated that the new model was capable of estimating nonlinearity of dynamic O-D travel time and flow and helped to improve their estimation precision under free-flow traffic states as well as congested flow states.

This study evaluates the effect of prioritizing right-turning vehicles in traffic flow not only at an artificial intersection but also at an existing corridor equipped with several intersections. The traffic simulator used in this study quantifies the effect of prioritizing a right-turning vehicle on traffic efficiency, safety, and the environment of an intersection. The ratio of prioritizing a right-turning vehicle is given as stochastic probability density functions. Numerical analysis showed that low traffic demand and low probability of prioritizing a right-turning vehicle brought significant improvement to traffic efficiency, safety and the environment. The effect was as significant as installing a general right-turn pocket and its special signal phase. It was concluded that humans have potential to improve this efficiency as significant as that of when a right-turn pocket is installed, which is costly and time-consuming.

A dynamic feedback system is developed for estimating the velocity and headway distance in a longitudinal three-vehicle platoon. The estimation system is modeled using an extended Kalman filter (EKF) and a Neural Kalman filter (NKF) that estimate the velocity and headway distance by measuring the acceleration rate of some selected vehicles in the platoon. State equations of the EKF are analytically defined by a discrete conservation equation of vehicle speed and headway distance, whereas the measurement equation is based on a conventional car-following model. The NKF, however, defines both equations using artificial neural network models (ANNs) that enable both equations to be defined without using any analytical equations. Numerical analysis showed that the NKF reduces the estimation errors in most cases even under unexpected car-following situations as the ANNs have the capability of describing nonlinear car-following phenomena. However, some difficulties still remain unsolved in optimizing NKF parameters. It was found that alternate approaches may be required to yield more accurate estimates instead of using NKF.