Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

Lv, Chen; Hu, Xiaosong; Sangiovanni‐Vincentelli, Alberto; Li, Yutong; Martínez, Clara Marina; Cao, Dongpu

doi:10.1109/tie.2018.2850031

Cited by 236 publications

(100 citation statements)

References 39 publications

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“…Driving style is also crucial to vehicle energy efficiency and ride comfort. Aside from enhancing adaptation to various traffic conditions, the two driving modes also improve economic feasibility and driving performance [10][11][12]. The all-new Chevrolet Bolt is equipped with four driving modes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of regenerative braking based on single-pedal control for electric vehicles

Liu

et al. 2019

Front. Mech. Eng.

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More than 25% of vehicle kinetic energy can be recycled under urban driving cycles. A single-pedal control strategy for regenerative braking is proposed to further enhance energy efficiency. Acceleration and deceleration are controlled by a single pedal, which alleviates driving intensity and prompts energy recovery. Regenerative braking is theoretically analyzed based on the construction of the single-pedal system, vehicle braking dynamics, and energy conservation law. The single-pedal control strategy is developed by considering daily driving conditions, and a single-pedal simulation model is established. Typical driving cycles are simulated to verify the effectiveness of the single-pedal control strategy. A dynamometer test is conducted to confirm the validity of the simulation model. Results show that using the single-pedal control strategy for electric vehicles can effectively improve the energy recovery rate and extend the driving range under the premise of ensuring safety while braking. The study lays a technical foundation for the optimization of regenerative braking systems and development of single-pedal control systems, which are conducive to the promotion and popularization of electric vehicles.

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Section: Introductionmentioning

confidence: 99%

Evaluation of regenerative braking based on single-pedal control for electric vehicles

Liu

et al. 2019

Front. Mech. Eng.

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“…The power required to move the vehicle is the summation of the five terms in Equations (15)- (19): P w (k) = P roll (k) + P g (k) + P drag (k) + P acc (k) + P regen (k).…”

Section: Bus Energy Costingmentioning

confidence: 99%

“…Speed advisory systems incorporating V2I communication with traffic lights can result in significant energy reduction [18]. [19] gave different control strategies for different driving styles for automated electric vehicles, bearing energy efficiency in mind. In addition to energy management between stops, charging station allocation and charging strategies are in the spotlight [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Energy-aware predictive control for electrified bus networks

2019

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For an urban bus network to operate efficiently, conflicting objectives have to be considered: providing sufficient service quality while keeping energy consumption low. The paper focuses on energy efficient operation of bus lines, where bus stops are densely placed, and buses operate frequently with possibility of bunching. The proposed decentralized, bus fleet control solution aims to combine four conflicting goals incorporated into a multi-objective, nonlinear cost function. The multi-objective optimization is solved under a receding horizon model predictive framework. The four conflicting objectives are as follows. One is ensuring periodicity of headways by watching leading and following vehicles i.e. eliminating bus bunching. Equal headways are only a necessary condition for keeping a static, predefined, periodic timetable. The second objective is timetable tracking. A conflicting objective to the former is minimizing passenger waiting time. When more than the expected passengers are waiting for the bus it is desirable to haste the vehicle in order to prevent bunching. The final objective is energy efficiency. To this end, an energy consumption model is formulated considering battery electric vehicles with recuperation during braking. Different weighting strategies are compared and evaluated through realistic scenarios, realized in a validated microscopic traffic simulation environment. Simulation results suggest 3 − 8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses. Formulating the multi-objective cost functionBuses operate on a route based on their timetable. When the schedule is tight, their trajectory shall be carefully planned while considering the schedule, passenger demand, and energy efficiency.The proposed bus velocity control algorithm (referred hereafter as control algorithm) creates an optimal trajectory within a predefined prediction horizon, considering the schedule, the number of passengers waiting and the location of other buses. The trade-off in the optimization is the total energy consumed, which is modeled as a physical energy consumption model. During operation, due to irregular dwell times and traffic disturbances, they tend to get out of sync with the schedule and start bunching. The goal of the control algorithm is to ensure timetable and

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“…For instance, in [31] a hybrid electric vehicle (HEV) powertrain model is implemented using the backward power flow method, in which the acceleration, velocity and slope information is employed as simulation inputs, whereas the fuel consumption and the state‐of‐charge (SOC) are the monitored variables for the energy management. Also, the modeling of EV is accomplished in a Matlab & Simulink‐based environment in [14], emphasizing on the role of the driving style in the optimal performance and energy management. Likewise, in [13] dynamical modeling of an electrified powertrain was presented considering the two‐inertia moment model to represent the electric motor and dynamic load, focusing the analysis on control strategies based in the determination of “hybrid states”, such as backlash and half‐shaft torque.…”

Section: Introductionmentioning

confidence: 99%

Light electric vehicle powertrain: Modeling, simulation, and experimentation for engineering students using PSIM

Vidal‐Bravo

Cruz-Soto

Paternina

et al. 2020

Comp Applic In Engineering

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Electric transport has been gaining more interest and importance in the last years. Moreover, the variety of transport systems driven by an electric motor has also been increased. Therefore, it is very important to provide engineering students with solid and comprehensible knowledge, as well as handly tools, for modeling an electric traction system. So far, this has been a difficult task for undergraduate students due to a variety of subjects involved, such as electric machines, power converters, control systems, and load estimations. This work is intended to provide a detailed and comprehensible description of the analysis, modeling, and implementation of a powertrain of a light utility vehicle using power electronics simulation (PSIM) commercial software in order that the student acquires the required knowledge for analyzing multiple configurations in the powertrain.

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Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

Cited by 236 publications

References 39 publications

Evaluation of regenerative braking based on single-pedal control for electric vehicles

Evaluation of regenerative braking based on single-pedal control for electric vehicles

Energy-aware predictive control for electrified bus networks

Light electric vehicle powertrain: Modeling, simulation, and experimentation for engineering students using PSIM

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