Article
Chemistry, Multidisciplinary
Hideki Ogawa, Yasutake Takahashi
Summary: The study introduces a new control method based on reservoir computing and MPC, which incorporates the predicted disturbance of a time-varying trajectory to achieve active vibration control of hybrid electric vehicle powertrains. This method allows real-time control by predicting future disturbance and calculating optimal control signals before the subsequent control period occurs.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
Chao Yang, Muyao Wang, Weida Wang, Ruihu Chen, Yue Ma, Changle Xiang, Gen Zeng
Summary: This study proposes a power preconditioning-based power flow predictive control strategy for heavy-duty series hybrid electric vehicles (SHEVs). The strategy uses demand power prediction to increase power output in advance and employs a fast iteration sequential quadratic programming (SQP) algorithm for model predictive control. Simulation and hardware-in-loop tests validate the strategy, showing a 5.39% reduction in fuel consumption compared to strategies without power preconditioning, a 40 V higher battery voltage, and a 20 ms shorter computing step size.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Electrical & Electronic
Maryam Razi, Nikolce Murgovski, Tomas McKelvey, Torsten Wik
Summary: This paper introduces predictive energy management of hybrid electric vehicles using computationally efficient multi-layer control. It involves optimizing gear, engine, battery, and electric machine decisions, and proposes efficient computation methods. The approach aims to optimize driving performance, prolong battery life, and improve fuel efficiency.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Automation & Control Systems
Yang Zhao, Yanguang Cai, Qiwen Song
Summary: The paper suggests using slope, elevation, speed, and route distance preview to optimize energy management of PHEVs, with the goal of reducing fuel consumption. The approach involves identifying route features, using information fusion and traffic prediction models, and applying dynamic programming to calculate optimal energy management solutions. Further exploration into predictive control models shows promising results in reducing fuel consumption along previewed routes.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Muyao Wang, Chao Yang, Weida Wang, Ruihu Chen, Liuquan Yang, Jie Su
Summary: This study proposes a short-term prediction-based power control strategy using the modified iteration sequential clustering quadratic programming (MISCQP) algorithm for heavy-duty series hybrid electric vehicles (SHEVs). The strategy ensures stable power output under transient high-power conditions and achieves real-time control by improving the iteration efficiency.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Green & Sustainable Science & Technology
Chuanshen Wu, Shan Gao, Yu Liu, Tiancheng E. Song, Haiteng Han
Summary: This study focuses on managing uncertainties in microgrids by establishing a two-layer model predictive control strategy with multi-uncertainty sampling for aggregated electric vehicles. Simulation results show that the proposed strategy outperforms conventional strategies in reducing forecasting error and regulating the charging and discharging of EVs.
Article
Engineering, Electrical & Electronic
Xiaolin Tang, Tong Jia, Xiaosong Hu, Yanjun Huang, Zhongwei Deng, Huayan Pu
Summary: The study proposed a predictive energy management strategy considering travel route information for exploring the energy-saving potential of plug-in hybrid electric vehicles. By training speed predictor based on real-world historical speed information, higher prediction accuracy was achieved. Moreover, adjusting battery temperature and ambient temperature can have significant impacts on total cost and energy consumption of vehicles.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Chemistry, Physical
Ruihu Chen, Chao Yang, Lijin Han, Weida Wang, Yue Ma, Changle Xiang
Summary: A power reserve predictive control strategy for series hybrid electric vehicles (SHEVs) is proposed, which improves engine responsiveness by predicting future demand power and regulating engine operation points. The strategy is validated in simulation and hardware-in-loop tests, showing improvements in expected output power and fuel economy compared to rule-based strategies.
JOURNAL OF POWER SOURCES
(2022)
Article
Engineering, Electrical & Electronic
Yu He, Kyoung Hyun Kwak, Youngki Kim, Dewey Jung, Jason Hoon Lee, Jinho Ha
Summary: In this study, a real-time torque-split strategy for a 48-V P0+P4 mild hybrid electric vehicle (MHEV) is proposed. The strategy considers realistic operational constraints and is optimized using dynamic programming. Simulation results show that the proposed strategy achieves close to global optimality in terms of fuel economy.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
X. M. Zhang, D. P. Yang, X. H. Zeng, Q. T. Wu, Q. F. Qian, D. F. Song
Summary: This paper proposes a data-driven Fast-MPC (FMPC) based dynamic coordination control strategy (DCCS) for a power-split hybrid electric bus to solve the challenge of model accuracy and computational complexity in existing MPC-based DCCS. The proposed strategy ensures the accuracy of the engine dynamic model through a piecewise data fitting method and simplifies the high dimension control model to solve the quadratic optimization problem of MPC. Simulation results demonstrate the effectiveness of the proposed control strategy in achieving good driving comfort and real-time performance.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Yanzhao Su, Minghui Hu, Jin Huang, Datong Qin, Chunyun Fu, Yi Zhang
Summary: The proposed dynamic torque-coordinated control method effectively reduces jerks of a hybrid vehicle under engine starting conditions by including engine segment active control, feedforward and feedback control of engine starting conditions, as well as active damping feedback compensation control for system resonance.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Fengqi Zhang, Xiaosong Hu, Teng Liu, Kanghui Xu, Ziwen Duan, Hui Pang
Summary: The paper proposes a computationally efficient energy management approach for parallel HEVs based on MPC framework, predicting velocity and introducing ECMS strategy to optimize torque split and gearshift while balancing fuel economy and drivability. Conducted sensitivity study and devised EF adaptation law for ECMS-based MPC, showing promising computational efficiency and global convergence to fuel economy produced by DP.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Chemistry, Multidisciplinary
Trieu Minh Vu, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava, Michal Petru
Summary: This paper presents the modelling and calculations for a hybrid electric vehicle in parallel configuration, analyzing the impact of different control parameters on vehicle drivability and comfortability, and proposing a new model predictive control scheme with softened constraints, demonstrating its advantages in performance and smoothness.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Seyedeh Mahsa Sotoudeh, Baisravan HomChaudhuri
Summary: This article proposes a deep-learning-based hierarchical control framework for eco-driving-based energy management of connected and automated hybrid electric vehicles (HEVs). The framework utilizes deep neural networks for control law learning and optimizes the energy management of the driving cycle and powertrain through long- and short-term decision-making.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Engineering, Electrical & Electronic
Philip Griefnow, Moritz Jakoby, Lorenz Doerschel, Jakob Andert
Summary: This study introduces a nonlinear model predictive control (NMPC) approach for a 48V mild hybrid powertrain, which can enhance both response behavior and energy consumption simultaneously. Through analysis and verification, it is found that NMPC is effective in controlling the system even under transient situations with disturbance variables. This control method enables fuel saving and improved driving dynamics.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Automation & Control Systems
Zejiang Wang, Xingyu Zhou, Junmin Wang
Summary: Traditional automated vehicle path-tracking algorithms that rely on plant models face challenges in obtaining accurate vehicle models due to complex tire-road interaction. Data-driven controllers, such as model-free control (MFC), have gained popularity. However, the control gain tuning of MFC remains a trial-and-error process. This article proposes integrating MFC with extremum-seeking control (ESC) to improve control performance.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Zejiang Wang, Xingyu Zhou, Heran Shen, Junmin Wang
Summary: Modeling driver steering behavior is crucial in automotive dynamics and control applications. Understanding individual steering characteristics enables advanced driver assistance systems to adapt to drivers and provide enhanced protection. This paper proposes an algebraic method for parameter identification of a driver steering model, which achieves fast convergence compared to traditional methods.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2022)
Article
Energy & Fuels
Heran Shen, Xingyu Zhou, Zejiang Wang, Junmin Wang
Summary: This paper introduces a new method to estimate the battery SOC using a Transformer neural network and I&I adaptive observer. Experimental results demonstrate higher accuracy compared to traditional baseline methods.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Civil
Yao Ma, Junmin Wang
Summary: This study presents a fuel-economical driving strategy for connected and automated vehicles (CAVs) which can significantly reduce fuel consumption by avoiding unnecessary braking and acceleration maneuvers. It also improves fuel performance for human-driven vehicles and has positive impacts on the energy efficiency of the transportation sector.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Mingcong Cao, Rongrong Wang, Nan Chen, Junmin Wang
Summary: This article proposes an integrated learning-based solution to accurately track the trajectory of the preceding vehicle under LiDAR failure and various lighting conditions in autonomous vehicles. The proposed method combines QLGMM, a weight-scheduled method, and a switchable dual-level LSTM network to improve trajectory tracking and fusion.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Xingyu Zhou, Zejiang Wang, Heran Shen, Junmin Wang
Summary: This paper introduces a mixed L-1/H-2 observer for accurately estimating the driver's steering torque in automated and assistance driving systems. Experimental results demonstrate that the proposed method outperforms the traditional method.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2022)
Article
Computer Science, Theory & Methods
Yunhao Bai, Li Li, Zejiang Wang, Xiaorui Wang, Junmin Wang
Summary: The rapid growth of autonomous driving presents new challenges to traditional vehicle control systems, leading to the proposal of a two-tier real-time scheduling framework AutoE2E that reduces deadline miss ratio and maximizes computing precision for driving control.
Article
Engineering, Civil
Zejiang Wang, Xingyu Zhou, Junmin Wang
Summary: This paper proposes an algebraic framework for evaluating a class of car-following models with linearly identified parameters, providing a new method for evaluating and comparing different car-following models.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yujing Zhou, Zejiang Wang, Junmin Wang
Summary: This article presents a novel and computationally efficient algorithm for illumination-resilient lane detection and path-following tasks in autonomous driving. The algorithm performs lane detection in the hue-saturation-value color space by distinguishing colored lane marks from the background.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Heran Shen, Zejiang Wang, Xingyu Zhou, Maxavier Lamantia, Kuo Yang, Pingen Chen, Junmin Wang
Summary: This article proposes a novel hybrid deterministic-stochastic methodology to accurately predict the future velocity and energy consumption of electric vehicles using various inputs, and experimental results demonstrate its superior performance compared to two popular baseline algorithms in velocity and energy consumption estimation.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Electrical & Electronic
Heran Shen, Xingyu Zhou, Hyunjin Ahn, Maxavier Lamantia, Pingen Chen, Junmin Wang
Summary: This article proposes a data-driven approach to predict the speed and energy consumption of electric vehicles, taking into account road features and individual driving characteristics. Improved neural networks are used to extract information and a novel energy consumption model is suggested. Experimental results show that the proposed method provides accurate predictions.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Automation & Control Systems
Zejiang Wang, Xingyu Zhou, Adrian Cosio, Junmin Wang
Summary: This paper proposes a novel nonlinear driver-vehicle-road (DVR) model that explicitly considers road curvature. A flatness-based lane-keeping assistance (LKA) system is designed based on this model. Model-Free Control is introduced to compensate for system modeling errors, and the proposed control framework is validated through simulations and experiments.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Automation & Control Systems
Xingyu Zhou, Heran Shen, Zejiang Wang, Hyunjin Ahn, Junmin Wang
Summary: This article proposes a novel driver-centric and neuro-adaptive-control-based lane-keeping assistance system (LKAS) to address the issue of vehicle roadway departure accidents. The system utilizes a noncertainty-equivalent adaptive control design scheme and an adaptive neural network that captures the human driver's steering behavior. A pilot study using a high-fidelity driving simulator validates the effectiveness of the proposed LKAS.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Xingyu Zhou, Zejiang Wang, Heran Shen, Junmin Wang
Summary: This paper proposes a novel control architecture to tackle the backlash issue in ground vehicle path-tracking. The dynamics of the steering system's backlash are compensated using an adaptive inverse controller and robustified with sigma modification. Hardware experiments demonstrate the superiority of the proposed solution.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Automation & Control Systems
Zejiang Wang, Xingyu Zhou, Junmin Wang
Summary: Traditional automated vehicle path-tracking algorithms require accurate plant models, while data-driven controllers have become popular for not relying on predefined plant models. Model-free control (MFC) offers a straightforward solution for ground vehicle path tracking, but its control gain tuning remains a challenge.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
