One of the constraints that limits regenerative braking in electric vehicles is its inability to regenerate energy under low-speed conditions, due to insufficient back-electromotive force developed in the traction motor. Below a certain speed threshold known as the low-speed cutoff point, energy is extracted from the battery instead of being returned, while overcoming electrical losses in the motor drive system. Although methods implemented to determine dynamic low-speed threshold are reported to result in higher braking efficiencies than fixed point methods, former implementations typically involve dynamic detection of battery current direction, which poses sensing challenges due to factors such as current sensor accuracy, offset, filtering and delays. As an alternative method, this paper proposes a model-based approach to analytically determine the dynamic low-speed cutoff point. Additionally, a loss minimization framework is developed to enhance the amount of energy recovered by achieving a lower value of the cutoff point, thereby extending the braking duration. Simulation studies have been done on a vector-controlled induction motor drive used for a passenger electric car to ascertain braking efficiency improvement. Results indicate that the proposed loss-reducing approach results in the highest efficiency improvement during low torque, high speed operation, when compared to other regions. The magnitude of efficiency improvement depends on system parameters and actual operating conditions of the vehicle. Hence, an example drive cycle has been considered to examine the improvement in practical scenarios. During the region of braking where the motor is operating above its base speed, an overall loss reduction of 30% has been obtained with the low-speed cutoff point lowered from 35 mph to 24 mph. On the other hand, no significant efficiency improvement has been observed in other operating regions.

An Antilock Brake System (ABS) operating on the principle of wheel slip regulation requires real-time values of reference slip during emergency braking. This reference slip should be obtained while ensuring the computational efficiency and performance of ABS on an Electronic Control Unit (ECU). This study proposes an algorithm for reference slip estimation that is inherently adapted to work with a current automotive-grade ECU. The proposed algorithm utilized linear interpolation, recursion, and function approximation to improve the design of an existing optimal reference slip algorithm. The improved ABS was found to perform equally or better in terms of braking distance when compared to its computationally heavier version across various vehicle operating conditions. Moreover, it was able to run on an automotive-grade ECU in less than 5 ms, which illustrates its deployment readiness.

Electronic braking system can improve the response time during braking and improve safety. However, brake-by-wire implementation poses a higher susceptibility to faults when compared to its mechanical counterpart. This study presents a mathematical model for a dual-sensor electronic brake pedal that relates the sensor output to the brake pedal displacement considering the noise in power source. Based on this model, an algorithm that ascertains the driver's intent and identifies any faults during real-time operation is developed. The algorithm was experimentally evaluated in a hardware setup with and without emulation of fault conditions. The algorithm was found to respond accurately to the input in normal and faulty conditions with the corresponding fault flag.

Rollover prevention in Heavy Commercial Road Vehicles (HCRVs) requires timely intervention with appropriate monitoring of the roll dynamics of the vehicle. In this study, a 3 - Degrees of Freedom (DoF) model of roll dynamics is developed for monitoring potential rollover, and design a rollover prevention strategy. The accuracy of the model was evaluated by corroborating synthesized data from the vehicle dynamics simulation software IPG TruckMaker®, A threshold-based differential braking methodology was utilized to prevent impending rollover. The proposed algorithm was tested in a hardware-in-loop set-up, including the dynamics and delays associated with the brake actuator. The closed-loop implementation results show that the proposed controller effectively prevents an impending rollover.

Advanced Driver Assistance Systems in heavy commercial road vehicles (HCRVs) have become necessary with the increasing contribution of HCRVs to road accident fatalities, where human factors form the major contributor. Collision avoidance systems can help in reducing fatalities through automated braking when required. This study focused on the development of a framework for the computation of the minimum time headway, for a Vehicle-to-Vehicle (V2V) communication-based collision avoidance system for HCRVs, that must be maintained through automated braking to avoid a collision. The framework, as well as the developed Collision Avoidance Algorithm (CAA), considers critical attributes of an HCRV such as significant load variations and brake actuator dynamics. Further, the time headway formulation varies with the initial longitudinal speed and considers communication latency. Experiments were conducted in a Hardware-in-Loop test setup to evaluate the efficacy of the proposed formulations. It was observed that the incorporation of communication latency and initial longitudinal speed explicitly in the time headway formulation maintained the desired final spacing between the vehicles, over a range of communication latencies and various host vehicle longitudinal speeds, which was not the case when the above factors were not considered.

Agricultural tractors are used for various farm operations with an implement attached at the rear of the tractor. When such a tractor is driven on road, it experiences pitch plane oscillations that may become severe enough to lift the front wheels from the road. The implement is typically mounted on a hydraulic hitch system that is used to lift or lower the implement. This electro hydraulic hitch system was modeled in this study and a controller was developed to attenuate the disturbance forces at the hitch point by lifting or lowering the implement in accordance with the measured disturbance force. Using a systematic procedure, a second order transfer function model relating the current input and the change in force output was developed using experimental data. The model was then used to design a sliding mode controller via state predictor based delay compensation in order to attenuate the disturbance forces while overcoming the effect of input delay that was observed in the hitch system. This controller provided an average disturbance force attenuation of 64.2% while maintaining the control input within actuator limits.

Fuel Cell Hybrid Electric Vehicles (FCHEVs) have the potential for providing a solution for clean transportation. For FCHEVs to be successful, it is important to improve their fuel economy and acceleration performance. One of the important systems to be worked on to achieve these goals is the vehicle's powertrain. This research work aims at providing a systematic procedure for multi-objective optimum sizing of a FCHEV powertrain, with the objectives of minimizing the fuel consumption and maximizing the acceleration performance. The work was carried out for a fuel cell-battery hybrid heavy road vehicle for Indian driving cycle. The design variables in the component sizing process were chosen to be speed ratio (x) of the traction motor and degree of hybridization (H) of the power sources. The values of objective functions and constraints were evaluated using ADVISOR software for a sample set of design variables. Surrogate modelling technique was adopted for constructing models for objective functions and constraints. Then, the optimization was carried out using multi-objective genetic algorithm approach to find the Pareto optimal solutions for the problem. This study provides a framework for component sizing of FCHEV that considers x and H as design variables.

This paper focuses on the design of controllers that ensure string stability in Heavy Commercial Road Vehicle (HCRV) platoons. While dealing with controller design for HCRVs, due to the sluggish nature of a pneumatic brake actuation system, the actuator dynamics has to be considered explicitly. Sliding Mode Control (SMC), which is a widely accepted control strategy for string stability has been considered and the effects of actuator dynamics during deceleration as well as acceleration phase were analysed. It was observed that, the actuation delays caused string instability using the designed SMC controller. In this context, a Variable Time Headway (VTH) based controller redesign that is capable of ensuring string stability despite actuation delays is presented. The efficacy of the presented VTH policy has been evaluated through simulations and it was observed that this policy could make the platoon string stable while accounting for real world actuator dynamics.

This paper presents a dynamic brake force distribution (BFD) control strategy that allocates the brake force to each wheel based on the normal loads and coefficients of friction for a two-axle heavy commercial road vehicle (HCRV) for straightline braking. A three degree of freedom half car pitch model was used to calculate the dynamic load transfer on each wheel, which was corroborated using IPG TruckMaker. The Magic Formula tire model with load-dependent coefficients was utilized to obtain instantaneous coefficient of friction at all four tires. The BFD strategy was then compared with two static BFD strategies: one which employs a 50:50 BFD and another which employs a BFD corresponding to ideal BFD curve (I curve) for the fully laden vehicle between the front and rear wheels. The test cases for a comparative evaluation of the dynamic and static BFD strategies were generated from the IS 11852 standard. The dynamic BFD strategy was found to reduce braking distance in the range of 1 % to 27 % when compared to the static BFD strategies. Further, all the strategies resulted in stable braking with no wheel lock.

Active safety is of utmost importance in heavy road vehicles due to the relatively higher number of fatalities encountered in their accidents. Vehicle-to-Vehicle (V2V) technology, which is seen as a future of connected vehicles, can potentially complement onboard sensing to reduce the time taken for detection, and to plan the path with the information available from road side units (RSU). This paper investigates the effect of Iatency in (V2V) communication on a collision avoidance algorithm developed for heavy road vehicles. Experiments performed on a Hardware-in-Loop (HiL) setup were used to evaluate the effect of Iatency for various scenarios. It was found that Iatency had a counterbalancing effect on vehicle spacing and relative longitudinal speed that led to insignificant changes in the final spacing. Further, a sensitivity analysis done at different host vehicle longitudinal speeds demonstrated the need of a variable time headway.

Traffic density, defined as the number of vehicles per unit length, is the primary measure used for quantifying road congestion. However, the direct measurement of this variable is difficult due to its spatial nature and the only method to directly measure it from field is aerial photography. Hence, it is usually estimated from other easily measurable variables such as speed or flow. Some of the reported approaches to obtain density include the input output analysis, fundamental traffic flow relation, and occupancy-based measurements in addition to those based on statistics, machine learning or model-based approaches. However, for better performance, all these methods require the careful selection of the relevant input variables/parameters and their relationships. One way of obtaining these relationships is to perform a dimensional analysis of the variables/parameters involved, identifying the non-dimensional variables/parameters and then obtaining a relationship between them using experimental data. This approach has been attempted for estimating road traffic density in this paper. The appropriate non-dimensional variables/parameters that characterize road traffic flow were first determined and the relation between them was then found out using simulated data. This relationship was subsequently used to estimate density for other datasets and the results were found to be promising.

Reliable estimation of performance measures such as queue and delay at intersections is important for the proper management of traffic. The information about these variables is valuable for the development of various traffic control strategies. The spatial nature of queue and delay makes their direct measurement a challenging task. The present study estimated these performance measures for the scenario when the queue ends within the advance detector using the data obtained from loop detectors installed at the entry and the exit of the intersection. A detailed analysis of the data obtained from loop detectors revealed that there were errors in the data. Two model based schemes, namely the occupancy based method and the queue clearance based method, were used for estimation of queue and delay using the erroneous data obtained from loop detectors. The results showed that the queue clearance based method was performing better while estimating queue and delay compared to the occupancy based method. Thus, the queue clearance based method would be valuable for the estimation of queues and delays while implementing with erroneous field data.

Data fusion is one of the recent approaches in traffic analysis for the accurate estimation and prediction of traffic parameters. In this approach, the parameters are estimated using the data from more than one source for better accuracy. This paper discusses a model based approach to estimate the parameters of heterogeneous traffic using both location data and spatial data using data fusion. The proposed method uses the Kalman filtering technique for the estimation of traffic density. Traffic density is a spatial parameter which is difficult to measure directly from field and can be measured only using aerial photography. Hence, it is usually estimated from other easily measurable parameters such as speed, flow, etc., or from a combination of such parameters. The present study estimates density using the flow values measured from video and the travel time obtained from Global Positioning System (GPS) equipped vehicles. The study also reports density estimation using flow and Space Mean Speed (SMS) obtained from location based data alone without fusing with spatial data, using the Extended Kalman filter technique. The estimates are corroborated using actual values and the results show data fusion performing better while estimating density.

Travel time is one of the most preferred traffic information by a wide variety of travelers. Travel time information provided through variable message signs at the roadside could be viewed as a traffic management strategy designed to encourage drivers to take an alternate route. At the same time, it could also be viewed as a traveler information service designed to ensure that the driver has the best available information based on which they can make travel decisions. In an Intelligent Transportation Systems (ITS) context, both the Advanced Traveler Information Systems (ATIS) and the Advance Traffic Management Systems (ATMS) rely on accurate travel time prediction along arterials or freeways. In India, currently there is no permanent system of active test vehicles or license plate matching techniques to measure stream travel time in urban arterials. However, the public transit vehicles are being equipped with Global Positioning System (GPS) devices in major metropolitan cities of India for providing the bus arrival time information at bus stops. However, equipping private vehicles with GPS to enable the stream travel time measurement is difficult due to the requirement of public participation. The use of the GPS equipped buses as probe vehicles and estimating the stream travel time is a possible solution to this problem. The use of public transit as probes for travel time estimation offers advantages like frequent trips during peak hours, wide range network coverage, etc. However, the travel time characteristics of public transit buses are influenced by the transit characteristics like frequent acceleration, deceleration and stops due to bus stops besides their physical characteristics. Also, the sample size of public transit is less when compared to the total vehicle population. Thus mapping the bus travel time to stream travel time is a real challenge and this difficulty is more complex in traffic conditions like in India with its heterogeneity and lack of lane discipline. As a pilot study, a model based approach using the Kalman filtering technique to predict stream travel time from public transit is carried out in the present study. Since it is only a pilot study, only twowheeled vehicles have been considered as they constitute a major proportion in the study area. The prediction scheme is corroborated using field data collected by carrying GPS units in two-wheelers traveling along with the buses under consideration. The travel time estimates from the model were compared with the manually observed travel times and the results are encouraging.

The safe operation of vehicles on roads depends, among other things, on a properly functioning brake system. Air brake systems are widely used in commercial vehicles such as trucks, tractor-trailers, and buses. In these brake systems, compressed air is used as the energy transmitting medium to actuate the foundation brakes mounted on the axles. In this paper, a model-based diagnostic system for air brakes is presented. This diagnostic system is based on a nonlinear model for predicting the pressure transients in the brake chamber that correlates the brake chamber pressure to the treadle valve (brake application valve) plunger displacement and the pressure of the air supplied to the brake system. Leaks and "out-of-adjustment" of push rods are two prominent defects that affect the performance of the air brake system. Diagnostic schemes that will monitor the brake system for these defects will be presented and corroborated with experimental data obtained from the brake testing facility