A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Hu, Qiuhao; Amini, Mohammad Reza; Wiese, Ashley; Seeds, Julia Buckland; Kolmanovsky, Ilya; Sun, Jing

doi:10.1115/1.4052819

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…In particular, the main topics concern platooning control, [23][24][25] Cooperative Adaptive Cruise Control (CACC), 26 cooperative driving with multiple CAVs (e.g., during lane changing) in mixed traffic conditions featured by human driven and automated vehicles, 27,28 maneuvers management at roundabouts 29 and intersections, [30][31][32] and control strategies for CAVs involving Energy Management Systems (EMS). [33][34][35] From the state-of-the-art literature, MPC results to be the most applied control strategy, used for both cooperative purposes and for longitudinal/lateral vehicle control. In particular, most of the recent published methods concern predictive control strategies for ADAS and Automated Vehicles (AVs), mainly adaptive [36][37][38][39] and nonlinear.…”

Section: Advanced Control Strategies For Adas and Cavsmentioning

confidence: 99%

“…Many topics have been studied, 20‐22 considering aspects related to both the road infrastructure and vehicles. In particular, the main topics concern platooning control, 23‐25 Cooperative Adaptive Cruise Control (CACC), 26 cooperative driving with multiple CAVs (e.g., during lane changing) in mixed traffic conditions featured by human driven and automated vehicles, 27,28 maneuvers management at roundabouts 29 and intersections, 30‐32 and control strategies for CAVs involving Energy Management Systems (EMS) 33‐35 . From the state‐of‐the‐art literature, MPC results to be the most applied control strategy, used for both cooperative purposes and for longitudinal/lateral vehicle control.…”

Section: Related Workmentioning

confidence: 99%

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Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Landolfi

Salvi²,

Troiano³

et al. 2023

Adaptive Control & Signal

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Summary Connected and automated vehicles will characterize the future of the mobility, featured by Smart Roads and Internet of Things technologies. Vehicles will behave as mobile nodes of a network enabling communication between them and with respect to the road infrastructure. In order to provide advanced driver assistance and automated driving along a Smart Road, control strategies need to take into account both parametric variations due to environmental and/or weather conditions (e.g., wind speed, presence of ice, and/or rain), and constraints related to road safety and vehicular traffic. For this reason, advanced adaptive control strategies result particularly suitable to provide a complete control of the vehicle. This article proposes an extension to a previous conference paper, concerning a Processor in the Loop testing of an Adaptive Model Predictive Control for coupled longitudinal/lateral vehicle dynamics, where the ego vehicle was simulated through a bicycle model. In the present work, a low‐cost real‐time Hardware In the Loop test bench, featured by Raspberry boards, is used to verify the control strategy in a way closer to a real application. A four‐wheel vehicle dynamic model is considered to simulate the ego vehicle, then robustness and sensitivity tests with respect to parametric variations related to road friction, wind speed and vehicle mass, and a comparison with an existing adaptive MPC are performed. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the control strategy, paving the way for future developments, where the goal will be the testing in a real road scenario by using an IoT gateway purposefully designed by the NetCom company.

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Section: Advanced Control Strategies For Adas and Cavsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Landolfi

Salvi²,

Troiano³

et al. 2023

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…Nonlinear MPC's are often combined with certain algorithms like the PSO‐based nonlinear MPC as mentioned in Reference 90. The PSO‐non‐linear MPC is adopted to realize the required force and moment with brake torque allocation and pressure regulation along with vehicle stability control features like calculating required longitudinal force, lateral force, etc 91 . In‐vehicle operation, the driver behaviour can be represented as a stochastic system since the driving conditions are random and unpredictable.…”

Section: Regenerative Braking Control Strategies In Electric Vehiclesmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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“…Many researchers have used vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication information in order to forecast the EVs' speed. For instance, the authors in [31] proposed a datadriven multi-range speed prediction strategy, which provides a preview of three different ranges; short, medium, and longrange. The authors used the V2V and V2I communications to obtain the short-range, a neural network (NN) for mediumrange, and a Bayesian Network (BN) for long-range speed prediction.…”

Section: Related Workmentioning

confidence: 99%

Embedded Real-Time Speed Forecasting for Electric Vehicles: A Case Study on RSK Urban Roads

et al. 2022

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During the past ten years, worldwide efforts have been pursuing an ambitious policy of sustainable development, particularly in the energy sector. This ambition was revealed by noticeable progress in the deployment and development of infrastructures for the production of renewable electrical energy. These infrastructures combined with the deployment of wired and wireless communications could support research actions in the field of connected electro-mobility. Also, this progress was manifested by the development of electric vehicles (EV), penetrating our transportation roads more and more. They are considered among the potential solutions, which are envisaged to further reduce road transport's greenhouse gas emissions, relying on low-carbon energy production. However, the uncertainty caused by both external road disturbances and drivers' behavior could influence the prediction of upcoming power demands. These latter are mainly affected by the unpredictability of the electric vehicles' speed on transportation roads. In this work, we introduce an energy management platform, which interfaces with in-vehicle components, using a developed embedded system, and external services, using IoT and big data technologies, for efficient battery power use. The platform was deployed in real-setting scenarios and tested for EV speed prediction. In fact, we have used driving data, which have been collected on Rabat-Salé-Kénitra (RSK) urban roads by our Twizy EV. A multivariate Long Short Term Memory (LSTM) algorithm was developed and deployed for speed forecasting. The effectiveness of LSTM was evaluated against well-known algorithms: Auto Regressive Integrated Moving Average (ARIMA), Convolutional Neural Network (CNN) and Convolutional LSTM (ConvLSTM). Experiments have been conducted using two approaches; the whole trajectory dataset and segmented trajectory datasets to train the models. The experimentation results show that LSTM outperforms the other used algorithms in terms of forecasting the speed, especially when using the trajectory segmentation approach.INDEX TERMS Electro-mobility, Intelligent Transportation Systems, Speed forecasting, Time-series forecasting, Deep learning I. INTRODUCTIONN OWADAYS, electric vehicles (EVs) are penetrating more and more transportation roads. There are three main categories of EVs: Hybrid Electric Vehicles (HEV), Plugin Hybrid Electric Vehicles (PHEV), and Batterypowered Electric Vehicles (BEV). BEVs are fully-electric vehicles with rechargeable batteries. HEV and PHEV are, however, powered by both gasoline and electricity. Unlike PHEV, which can be recharged through both regenerative braking and external electricity sources, HEV is charged from the electricity generated by the car's braking system. However, both of them are still using gasoline, which is one of the sources of greenhouse gas.BEVs have been getting more attention in the last years, as they are environment-friendly while having an acceptable driving range, particularly in urban areas. BEV driving range

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A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Cited by 13 publications

References 25 publications

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Embedded Real-Time Speed Forecasting for Electric Vehicles: A Case Study on RSK Urban Roads

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