Potential Energy Saving of V2V-Connected Vehicles in Large-Scale Traffic

Hyeon, Eunjeong; Han, Jeongwoo; Shen, Daliang; Karbowski, Dominik; Kim, Nam Wook; Rousseau, Aymeric

doi:10.1016/j.ifacol.2022.10.265

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…1, an ego vehicle is able to acquire real-time road information from GPS or roadside units (RSUs), such as legal speed limit, road curvature, and slope angle, [38], [39]. The front traffic can be reasonably formulated as a leading vehicle [40], and in the present framework, it is assumed that a speed profile of the leading vehicle is available for the ego vehicle, which can be obtained from the leading vehicle via V2V [41] or from the RSUs via V2I [42] communication. Furthermore, it is assumed that no lane changing or overtaking of the leading vehicle is taking place.…”

Section: Uncertaintiesmentioning

confidence: 99%

A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles

Yu,

Pan,

Georgiou

et al. 2024

IEEE Trans. Transp. Electrific.

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This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is solved by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed realtime robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the spacedomain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.

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Section: Uncertaintiesmentioning

confidence: 99%

A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles

Yu,

Pan,

Georgiou

et al. 2024

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

show abstract

“…1, an ego vehicle is able to acquire real-time road information from GPS or roadside units (RSUs), such as legal speed limit, road curvature, and slope angle, [34], [35]. The front traffic can be reasonably formulated as a leading vehicle [36], and in the present framework, it is assumed that a speed profile of the leading vehicle is available for the ego vehicle, which can be obtained from the leading vehicle via V2V [37] or from the RSUs via V2I [38] communication. Furthermore, it is assumed that no lane changing or overtaking of the leading vehicle is taking place.…”

Section: Statement Of Eacc Problemmentioning

confidence: 99%

“…Then, the prediction of the leading vehicle ṽl (k:k+N−1) available to the ego vehicle is generated by setting ṽl (k:k+N−1)=v l, f (k:k+N−1), ∀k ∈ [0, k s ]) to emulate non-optimal behaviours and inevitable communication or sensing error of the leading vehicle velocity. The resulting velocity error further results in the disturbance on the time gap state, d t , (determined by (37)).…”

Section: A Simulation Setupmentioning

confidence: 99%

A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

Pan

Georgiou

et al. 2023

2023 IEEE International Conference on Mechatronics (ICM)

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This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is approached by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.

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“…On the other hand, model predictive control (MPC) has been gaining popularity for EV controllers, especially with the advancement of connected and autonomous vehicle (CAV) technology enabled by vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications [7], [8]. With these communications, the ego vehicle is aware of the driving environment and the travel plans of other vehicles on the road.…”

mentioning

confidence: 99%

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Chen,

Kwak,

Kim

et al. 2024

IEEE Access

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The thermal management system (TMS) in electric vehicles (EVs), including climate control and battery thermal regulation, consumes more energy than any other auxiliary components. Therefore, optimizing TMS control is crucial for enhancing EV driving range. However, the complexity of the TMS, described by a differential algebraic system, poses challenges for real-time optimal control. This study proposes model predictive control (MPC)-based solutions for integrated TMS operation in EVs. An optimal thermal management problem is formulated using economic nonlinear MPC (NMPC), and its performance is evaluated. To reduce computational load, an approximated value function (VF) is introduced based on the economic NMPC results. A linear-time-varying MPC (LTV-MPC) with the approximated VF is proposed for real-time implementation using quadratic programming, and through simulations it is compared with the baseline NMPC controller and a rule-based (RB) controller. Results reveal that the LTV-MPC with an approximated VF performs similarly to NMPC while offering slightly compromised cooling performance. It also significantly reduces the computational time by a factor of 10 4 compared with NMPC owing to the short prediction horizon enabled by the approximated VF. Furthermore, when compared with the RB controller, the proposed LTV-MPC achieves energy savings in the range of 22.3% to 29.8%.

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Potential Energy Saving of V2V-Connected Vehicles in Large-Scale Traffic

Cited by 6 publications

References 7 publications

A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles

A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles

A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

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