A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Component Optimization of a Parallel P4 Hybrid Electric Vehicle Utilizing an Equivalent Consumption Minimization Strategy

Advancements in battery and electric motor technology have driven the development of hybrid electric vehicles to improve fuel economy. Hybrid electric vehicles can utilize an internal combustion engine and an electric motor in many configurations, requiring the development of advanced energy management strategies for a range of component configurations. The Equivalent Consumption Minimization Strategy (ECMS) is an advanced energy management strategy that can be calculated in-vehicle in real-time operation. This energy management strategy uses an equivalence factor to equate electrical to mechanical power when performing the torque split determination between the internal combustion engine and electric motor. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimized fuel economy results, while maintaining a target state of charge of the battery. The goal of this work is to analyze how the algorithm operates with the WVU Chevy Blazer to find an optimal equivalence factor that can maintain a strict charge sustaining window of operation for the high voltage battery, while improving the fuel economy based on dynamic programing results calculated for this vehicle architecture. Different electric motor sizes are then explored by changing the max torque and max power to analyze how the equivalence factor changes to operate the ECMS algorithm. This research mainly focused on utilizing both the UDDS drive cycle and HwFET drive cycle to determine the effectiveness of the ECMS algorithm. The results show that as the max torque and max power of the electric motor increased, the equivalence factor found for the UDDS drive cycle and the HwFET drive cycle converged to similar value. The convergence of the equivalence factor allowed the ECMS algorithm to better maintain the target state of charge of the battery while maintaining the fuel economy and improving the fuel economy for the UDDS drive cycle and HwFET drive cycle, respectively

Predictive Aecms By Utilization Of Intelligent Transportation Systems For Hybrid Electric Vehicle Powertrain Control

Information obtainable from intelligent transportation systems (ITS) provides the possibility of improving safety and efficiency of vehicles at different levels. In particular, such information also has the potential to be utilized for the prediction of driving conditions and traffic flow, which allows Hybrid Electric Vehicles (HEVs) to run their powertrain components in corresponding optimum operating regions. This paper proposes to improve the performance of one of the most promising realtime powertrain control strategies, called adaptive equivalent consumption minimization strategy (AECMS), using predicted driving conditions. In this paper, three real-time powertrain control strategies are proposed for HEVs, each of which introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. These factors are proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy, number of engine transients (ON/OFF), and charge sustainability of the battery

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles has accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. The design and implementation of an optimized control strategy is a complex challenge. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. Real-time strategies incorporate methods such as drive cycle prediction algorithms, parameter feedback, driving pattern recognition algorithms, etc. The goal of this work is to use a previously defined strategy which has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS) [1], which uses an equivalence factor to define the control strategy. The equivalence factor essentially defines the torque split between the electric motor and internal combustion engine. Consequently, the equivalence factor greatly affects fuel economy. An equivalence factor that is optimal (with respect to fuel economy) for a single drive cycle can be found offline – with a priori knowledge of the drive cycle. The RBF ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data are used to train the RBF ANN, each set contains characteristics from a different drive cycle. Each drive cycle is characterized by 9 parameters. For each drive cycle, the optimal equivalence factor is determined and included in the training data. The performance of the RBF ANN is evaluated against the fuel economy obtained with the optimal equivalence factor from the ECMS. For the majority of drive cycles examined, the RBF ANN implementation is shown to produce fuel economy values that are within +/- 2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF ANN is that it does not require a priori drive cycle knowledge and is able to be implemented real time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF ANN could be improved to produce better results across a greater array of driving conditions

Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% regardless of starting SOC. Recommendations for modification of the fuzzy logic controller are made to produce additional fuel economy and charge sustaining benefits from the parallel hybrid vehicle model