Cooperative Braking Research Articles

Use of Electric Driveline for a Cooperative Braking Strategy

Use of Electric Driveline for a Cooperative Braking Strategy

Modeling and design of cooperative braking in electric and hybrid vehicles using induction machine and hydraulic brake

In mixed-mode braking applications, the electric motor / generator (M/G) and hydraulic pressure valve are controlled to meet the driver’s braking demand. Controlling these braking elements is achieved by modulating the current generated by the M/G and adjusting the fluid pressure to the wheel brake cylinders. This paper aims to model and design combined regenerative and hydraulic braking systems which, comprise an induction electric machine, inverter, NiMH battery, controller, a pressure source, pressure control unit, and brake calipers. A 15 kW 1500 rpm induction machine equipped with a reduction gear having a gear ratio of 4 is used. A hydraulic brake capable to produce fluid pressure up to 40 bar is used. Direct torque control and pressure control are chosen as the control criteria in the M/G and the hydraulic solenoid valve. The braking demands for the system are derived from the Federal Testing Procedure (FTP) drive cycle. Two simulation models have been developed in Matlab®/Simulink® to analyze the performance of the control strategy in each braking system. The developed model is validated through experiment. It is concluded that the control system does introduce torque ripple and pressure oscillation in the braking system, but these effects do not affect vehicle braking performance due to the high frequency nature of pressure fluctuation and the damping effect of the vehicle inertia. Moreover, experiment results prove the effectiveness of the developed model.

Estimating Project-Level Vehicle Emissions with Vissim and MOVES-Matrix

Estimating transportation network emissions requires multiplying estimates of on-road vehicle activity (by source type and operating mode) by applicable emission rates for the observed source type and operating conditions. Coupling microsimulation model runs with emissions modeling can make fast assessments possible in transportation air quality planning. This research developed a tool with automated linkage between the Vissim microsimulation model and the Motor Vehicle Emission Simulator (MOVES) model. To link the two models, the research team used MOVES-Matrix, which was prepared by iteratively running MOVES across all possible iterations of vehicle source type, fuel, environmental and operating conditions, and other parameters (hundreds of millions of model runs) to create a multidimensional emission rate lookup matrix. A Vissim simulation of the major arterial roads and freeways at I-85 and Jimmy Carter Boulevard in Gwinnett County, Georgia, provided the case study for this MOVES-matrix application. The researchers present predicted emissions and the results of a sensitivity analysis to identify the potential impacts of various internal Vissim modeling parameters (such as minimum headway, maximum deceleration rate for cooperative braking, and emergency stop distance) on a case study’s emissions outputs. The sensitivity analysis found that internal Vissim parameters impacted emissions and that proper care should be taken in using Vissim for emissions analysis at the corridor and link level. The case study demonstrates that Vissim coupled with MOVES-Matrix can be an effective tool for emissions analysis.

Hierarchical Control Strategy for the Cooperative Braking System of Electric Vehicle.

This paper provides a hierarchical control strategy for cooperative braking system of an electric vehicle with separated driven axles. Two layers are defined: the top layer is used to optimize the braking stability based on two sliding mode control strategies, namely, the interaxle control mode and signal-axle control strategies; the interaxle control strategy generates the ideal braking force distribution in general braking condition, and the single-axle control strategy can ensure braking safety in emergency braking condition; the bottom layer is used to maximize the regenerative braking energy recovery efficiency with a reallocated braking torque strategy; the reallocated braking torque strategy can recovery braking energy as much as possible in the premise of meeting battery charging power. The simulation results show that the proposed hierarchical control strategy is reasonable and can adapt to different typical road surfaces and load cases; the vehicle braking stability and safety can be guaranteed; furthermore, the regenerative braking energy recovery efficiency can be improved.

A Predictive Distribution Model for Cooperative Braking System of an Electric Vehicle

A predictive distribution model for a series cooperative braking system of an electric vehicle is proposed, which can solve the real-time problem of the optimum braking force distribution. To get the predictive distribution model, firstly three disciplines of the maximum regenerative energy recovery capability, the maximum generating efficiency and the optimum braking stability are considered, then an off-line process optimization stream is designed, particularly the optimal Latin hypercube design (Opt LHD) method and radial basis function neural network (RBFNN) are utilized. In order to decouple the variables between different disciplines, a concurrent subspace design (CSD) algorithm is suggested. The established predictive distribution model is verified in a dynamic simulation. The off-line optimization results show that the proposed process optimization stream can improve the regenerative energy recovery efficiency, and optimize the braking stability simultaneously. Further simulation tests demonstrate that the predictive distribution model can achieve high prediction accuracy and is very beneficial for the cooperative braking system.

A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle

Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO). To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD) is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.

Cooperative Control of Regenerative Braking and Antilock Braking for a Hybrid Electric Vehicle

A new cooperative braking control strategy (CBCS) is proposed for a parallel hybrid electric vehicle (HEV) with both a regenerative braking system and an antilock braking system (ABS) to achieve improved braking performance and energy regeneration. The braking system of the vehicle is based on a new method of HEV braking torque distribution that makes the antilock braking system work together with the regenerative braking system harmoniously. In the cooperative braking control strategy, a sliding mode controller (SMC) for ABS is designed to maintain the wheel slip within an optimal range by adjusting the hydraulic braking torque continuously; to reduce the chattering in SMC, a boundary-layer method with moderate tuning of a saturation function is also investigated; based on the wheel slip ratio, battery state of charge (SOC), and the motor speed, a fuzzy logic control strategy (FLC) is applied to adjust the regenerative braking torque dynamically. In order to evaluate the performance of the cooperative braking control strategy, the braking system model of a hybrid electric vehicle is built in MATLAB/SIMULINK. It is found from the simulation that the cooperative braking control strategy suggested in this paper provides satisfactory braking performance, passenger comfort, and high regenerative efficiency.

Cooperative regenerative braking control algorithm for an automatic-transmission-based hybrid electric vehicle during a downshift

A cooperative regenerative braking control algorithm is proposed for a six-speed automatic-transmission-based parallel hybrid electric vehicle (HEV) during a downshift that satisfies the requirements for braking force and driving comfort. First, a downshift strategy during braking is suggested by considering the re-acceleration performance. To maintain driving comfort, a cooperative regenerative braking control algorithm is developed that considers the response characteristics of the electrohydraulic brake. Using the electrohydraulic brake’s hardware and an HEV simulator, a hardware-in-the-loop simulation (HILS) is performed. From the HILS results, it is found that the proposed cooperative regenerative braking control algorithm satisfies the demanded braking force and driving comfort during the downshift with regenerative braking.