Showing papers by "Giorgio Rizzoni published in 2018"

Robust Stability Analysis of DC Microgrids With Constant Power Loads

Abstract: This paper studies stability analysis of dc microgrids with uncertain constant power loads (CPLs). It is well known that CPLs have negative impedance effects, which may cause instability in a dc microgrid. Existing works often study the stability around a given equilibrium based on some nominal values of CPLs. However, in real applications, the equilibrium of a dc microgrid depends on the loading condition that often changes over time. Different from many previous results, this paper develops a framework that can analyze the DC microgrid stability for a given range of CPLs. The problem is formulated as a robust stability problem of a polytopic uncertain linear system. By exploiting the structure of the problem, we derive a set of sufficient conditions that can be efficiently checked by solving convex optimization problems to guarantee locally robust stability. The effectiveness and nonconservativeness of the proposed framework are demonstrated using simulation examples.

Fault Diagnosis and Fault Mitigation for Torque Safety of Drive-by-Wire Systems

Abstract: Torque safety is one of the key elements of functional safety for future automobile development. For drive-by-wire systems that are commonly used in modern vehicles, pedal signals are critical for the vehicle supervisory controller to generate appropriate torque request to the powertrain, in order to keep the vehicle speed as desired. Problems with pedals or pedal sensors will result in incorrect torque request sent to the drivetrain, possibly resulting in torque safety problems, such as sudden unintended acceleration. This paper proposes a fault detection and isolation strategy based on structural analysis as well as fault mitigation methods for torque safety of drive-by-wire systems, with focus on pedal mechanical stiction fault and pedal sensor faults. Before the diagnostic strategy is designed, fault modeling for these two types of faults are introduced to assist the design of diagnostic tests. In addition, for pedal mechanical stiction fault, this paper introduces an effective approach to calibrate the thresholds in the fault diagnostic system in order to achieve the desired tradeoff between false alarm rate and detection delay. The detection and mitigation strategies are calibrated and validated through model-in-the-loop and hardware-in-the-loop simulations using realistic pedal profiles from the testing results of the EcoCAR2 prototype vehicle developed at the authors’ institution.

An Improved Model-Based Self-Adaptive Filter for Online State-of-Charge Estimation of Li-Ion Batteries

Abstract: Accurate battery modeling is essential for the state-of-charge (SOC) estimation of electric vehicles, especially when vehicles are operated in dynamic processes. Temperature is a significant factor for battery characteristics, especially for the hysteresis phenomenon. Lack of existing literatures on the consideration of temperature influence in hysteresis voltage can result in errors in SOC estimation. Therefore, this study gives an insight to the equivalent circuit modeling, considering the hysteresis and temperature effects. A modified one-state hysteresis equivalent circuit model was proposed for battery modeling. The characterization of hysteresis voltage versus SOC at various temperatures was acquired by experimental tests to form a static look-up table. In addition, a strong tracking filter (STF) was applied for SOC estimation. Numerical simulations and experimental tests were performed in commercial 18650 type Li(Ni1/3Co1/3Mn1/3)O2 battery. The results were systematically compared with extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of comparison showed the following: (1) the modified model has more voltage tracking capability than the original model; and (2) the modified model with STF algorithm has better accuracy, robustness against initial SOC error, voltage measurement drift, and convergence behavior than EKF and UKF.

Mission-based Design Space Exploration for Powertrain Electrification of Series Plugin Hybrid Electric Delivery Truck

Abstract: Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportati ...

Real-time energy management strategy for hybrid electric vehicles with reduced battery aging

Abstract: Systems, methods, and computer program products for managing hybrid energy sources (116, 120). The use of energy sources (116, 120) may be adjusted by an Adaptive Equivalent Consumption Management Strategy (A-ECMS) implemented on a supervisory controller (12, 112). The A-ECMS may take into account both fuel economy and battery capacity degradation in a Hybrid Electric Vehicle (HEV) to optimize fuel consumption with consideration of battery aging as determined using a severity factor (σ) received from the HEV powertrain (14). Optimal control approaches including Dynamic Programming and Pontryagin' s Minimum Principle may be used to develop energy management strategies that optimally trade off fuel consumption and battery aging. Based on the optimal solutions, a real-time implementable battery-aging-conscious A-ECMS is implemented.

Influence of Battery Charging Current Limit on the Design of Range Extender Hybrid Electric Trucks

Abstract: In this paper, the influence of different charging current limits on the battery pack for a variety of powertrains for a hybrid electric truck is investigated. Dynamic Programming and a smart multi-objective search algorithm is used to facilitate the searching of optimal combinations of architectures, component sizes and charging current limits. Vehicle architecture with in-wheel motors is further analyzed to see the compromise between regenerative braking with high c-rates and battery aging. The results show that based on the battery chemistry and its aging and driving cycle characteristics, different decisions can be made for the vehicle powertrain architecture design and battery charging current limit settings.

Battery Discharge Strategies for Energy Management in Electrified Truck for Pickup and Delivery Application

Abstract: Fuel economy in a hybrid electric vehicle is significantly affected by the battery discharge strategy. This paper investigates the blended battery discharge strategy with four possible battery State of Charge (SOC) profiles to compare the fuel savings possible over a default Charge Discharge – Charge Sustaining strategy, given that the vehicle’s duty cycle is known. A pickup & delivery truck with Range Extended Electric Vehicle (REEV) powertrain architecture has been modeled. Vehicle speed control is implemented using a distance-based driver that matches the distance traveled from every start to stop in the drivecycle. On-board energy management is implemented using the Energy Consumption Minimization Strategy (ECMS). It is found that a predicted power consumption-based battery discharge profile results in the least fuel consumption. A distance based discharge has relatively higher fuel consumption but is quite close.

Design Space Exploration for Powertrain Electrification using Gaussian Processes

Abstract: Design space exploration of hybrid electric vehicles is an important multi-objective global optimization problem. One of the main objectives is to minimize fuel consumption while maintaining satisfactory driveability performance and vehicle cost. The design problem often includes multiple design options, including different driveline architectures and component sizes, where different candidates have various trade-offs between different, in many cases contradictory, performance requirements. Thus, there is no global optimum but a set of Pareto-optimal solutions to be explored. The objective functions can be expensive to evaluate, due to time-consuming simulations, which requires careful selection of which candidates to evaluate. A design space exploration algorithm is proposed for finding the set of Pareto-optimal solutions when the design search space includes multiple design options. As a case study, powertrain optimization is performed for a medium-sized series hybrid electric delivery truck.

Optimal Energy Management in a Range Extender PHEV Using a Cascaded Dynamic Programming Approach

Abstract: Dynamic programming is widely used to benchmark the performance of a hybrid electric vehicle. It is also well documented that it is a very computationally heavy procedure depending on the number of states and control inputs in the problem formulation. In this paper we investigate the possibility of reduction in the computational time by splitting the number of states and control inputs between two models and applying dynamic programming individually, using the output of one as an input to the other and hence cascading the two models. A range extended hybrid electric vehicle powertrain architecture is modeled with four states and four control inputs, which is considered as the full model. Further, the states and control inputs of the battery and engine are separated from the other states, splitting them between the two new DP models. The vehicle performance estimated from this ‘cascaded models approach’ is compared with that from the full model. Initial comparisons show a very good match with minor differences in performance and considerable a reduction in computation time from around 6 hours to around a minute.