This paper investigates the estimation of lateral tire forces and predictive control for vehicles equipped with intelligent tires. Motivated by the capability of intelligent tires to estimate lateral tire forces, we p...
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ISBN:
(数字)9798350354409
ISBN:
(纸本)9798350354416
This paper investigates the estimation of lateral tire forces and predictive control for vehicles equipped with intelligent tires. Motivated by the capability of intelligent tires to estimate lateral tire forces, we propose a control scheme that includes a predictor for lateral tire forces, utilizing the Gaussian Process Regression technique. In addition, a metric for online data management is proposed, which has the characteristic of retaining more data in regions where the change of the function value is relatively large. The proposed metric can be interpreted as an extension of the existing method, allowing for the control of dataset quality within its limited size. We apply the proposed control scheme to the model predictive contouring control problem. Numerical simulations demonstrate the robustness of the proposed control scheme to tire parameter uncertainty, in comparison to a baseline controller.
With intensifying concerns over emissions, hybrid electric vehicles (HEVs) offer a practical bridge toward electrification by combining an internal combustion engine and an electric battery for improved efficiency and...
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With intensifying concerns over emissions, hybrid electric vehicles (HEVs) offer a practical bridge toward electrification by combining an internal combustion engine and an electric battery for improved efficiency and lower emissions. This study proposes a driving pattern recognition network-hybrid model predictive control (DPRN-HMPC) framework to improve the energy management of parallel hybrid electric vehicles (PHEVs) while reducing computational complexity. DPRN-HMPC leverages a pre-trained deep neural network classifier to identify the driver’s current driving pattern, which is then used to predict the wheel torque demand input for the model predictive control (MPC). This approach effectively predicts wheel torque demand—a stochastic variable—without relying on a computationally expensive stochastic model. Simulation results demonstrate that DPRN-HMPC improves average energy efficiency by 1.08% over linear deterministic MPC (LDMPC) across ten driving cycles. It maintains performance comparable to scenario-based hybrid MPC (scHMPC) while reducing computation time by 77.3%, ensuring feasibility within the 1,180-second limit of the New European Driving Cycle. Additionally, DPRN-HMPC achieves a 0.75% improvement in energy efficiency across five unseen driving cycles, demonstrating adaptability to new driving scenarios. These findings highlight the effectiveness of DPRN-HMPC in providing both practical and energy-efficient control solutions for PHEV energy management.
Hybrid electric vehicles (HEVs) are attracting attention for their high fuel efficiency and low emissions compared to diesel-powered vehicles. The fuel efficiency of the HEV is highly dependent on the optimal power sp...
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Hybrid electric vehicles (HEVs) are attracting attention for their high fuel efficiency and low emissions compared to diesel-powered vehicles. The fuel efficiency of the HEV is highly dependent on the optimal power split ratio. The problem of finding the optimal power split ratio trajectory is difficult to solve with linear and/or deterministic controllers because it has nonlinear and stochastic characteristics. In this study, stochastic hybrid model predictive control (SHMPC) was applied to the HEV system. Piecewise affine (PWA) modeling of HEV systems was performed to model nonlinear systems with smaller model-plant mismatch compared to linear models. Furthermore, to account for the driver's stochastic behavior, a scenario-based stochastic model predictive control (SMPC) approach was employed. controllers of scenario-based linear SMPC and scenario-based SHMPC are built. The controllers were tested in MATLAB/Simulink HEVP2 Reference Application simulation environment.
The authors regret that Changbeom Hong, who contributed equally to this work alongside Hyungjun Kim, was not designated as a co–first author in the published article. Due to an oversight during the submission process...
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