Article
Engineering, Electrical & Electronic
Kyunghwan Choi, Jihye Byun, Sangmin Lee, In Gwun Jang
Summary: In this study, a novel energy management strategy for hybrid electric vehicles (HEVs) is proposed, which considers actual driving conditions to provide near-optimal performance. The strategy defines a near-optimal equivalent factor condition and presents an iterative scheme to adjust this condition. It shows better adaptability to changes in the driving conditions with a smaller loss of optimality.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Thermodynamics
Kai Gao, Pan Luo, Jin Xie, Bin Chen, Yue Wu, Ronghua Du
Summary: This paper proposes an improved energy management strategy for plug-in hybrid electric vehicles (PHEVs) by integrating driving intention and LIDAR data to achieve accurate speed prediction and optimize energy management in real-time.
Article
Engineering, Electrical & Electronic
Zhiqi Guo, Jianhua Guo, Liang Chu, Chong Guo, Jincheng Hu, Zhuoran Hou
Summary: In this paper, a hierarchical energy management strategy (H-EMS) is proposed to achieve energy management optimization for 4WD PHEVs, including model predictive control (MPC) based on future speed information and power component distribution based on an equivalent consumption minimization strategy (ECMS). Simulation results validate that the proposed method has higher energy-saving capabilities and improves economy by 11.87% compared to rule-based (RB) energy management strategies.
Article
Engineering, Marine
Yuequn Ge, Jundong Zhang, Kunxin Zhou, Jinting Zhu, Yongkang Wang
Summary: This paper analyzes a hybrid power system containing a fuel cell and proposes an improved scheme involving the replacement of a single energy storage system with a hybrid energy storage system. An efficient energy management system based on the equivalent consumption minimization strategy is proposed to achieve a reasonable power distribution and stable operation. The proposed strategy outperforms the state-based and fuzzy logic-based EMS in terms of stabilizing the hybrid power system and reducing hydrogen consumption.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Chemistry, Analytical
Shilin Pu, Liang Chu, Jincheng Hu, Shibo Li, Zhuoran Hou
Summary: An EP-ECMS is proposed for parallel plug-in hybrid vehicles, which utilizes the traffic characteristic information obtained from the intelligent traffic system to guide the adjustment of equivalence factor and improve the environmental adaptiveness of ECMS. A high-accuracy environmental perceiver based on GCN and attention mechanism is developed for traffic state recognition, and the optimal equivalent factor is searched using the Harris hawk optimization algorithm.
Article
Green & Sustainable Science & Technology
Kun He, Dongchen Qin, Jiangyi Chen, Tingting Wang, Hongxia Wu, Peizhuo Wang
Summary: In this paper, an online adaptive equivalent consumption minimum strategy (A-ECMS) based on driving style recognition is proposed to reduce hydrogen consumption and prolong the fuel cell life of fuel cell buses (FCBs).
Article
Computer Science, Information Systems
Heeyun Lee, Suk Won Cha
Summary: This study introduces a reinforcement learning-based approach to determine the equivalent factor in hybrid electric vehicles, indirectly extracted from reinforcement learning results. By combining reinforcement learning with the equivalent consumption minimization strategy, the proposed method achieves near-optimal performance compared to dynamic programming and improves performance compared to existing strategies.
Article
Automation & Control Systems
Xiaodong Sun, Yunfei Cao, Zhijia Jin, Xiang Tian, Mingzhou Xue
Summary: In this article, an adaptive energy management strategy based on real-time traffic information is proposed to improve the efficiency of a parallel hybrid electric bus. The system consists of offline and online components, and utilizes velocity characteristic parameters and Markov transition matrices to predict vehicle speed on different road types. The online component incorporates predicted speed and road information into an equivalent consumption minimization strategy for adaptive changes. Simulation studies show the superiority of the proposed strategy in improving fuel economy, and hardware-in-the-loop tests confirm its compatibility with the original design intent.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Chemistry, Analytical
Jianhua Guo, Zhiqi Guo, Liang Chu, Di Zhao, Jincheng Hu, Zhuoran Hou
Summary: This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in 4WD PHEV. By introducing future driving condition categories, the strategy optimizes the management of the multi-energy system and improves the economy, unlocking the energy-saving potential.
Article
Computer Science, Information Systems
Dongdong Chen, Tie Wang, Tianyou Qiao, Tiantian Yang, Zhiyong Ji
Summary: This study proposes an adaptive equivalent consumption minimization strategy (A-ECMS) based on driving cycle recognition for a parallel hybrid electric vehicle (HEV). By training a neural network for accurate driving cycle recognition, the optimal equivalent factor is selected for the current driving cycle. Simulation results show that compared to logic-based EMS, A-ECMS can reduce fuel consumption and improve battery state of charge in different driving cycles.
Article
Green & Sustainable Science & Technology
Xueqin Lu, Ruidong Meng, Ruiyu Deng, Liyuan Long, Yinbo Wu
Summary: In order to improve the motion range of mobile welding robot, the energy management strategy of a fuel cell hybrid power system was studied. The strategy involves energy economy optimization and comprehensive performance improvement. The strategy adopts hierarchical optimization control, with the inner layer optimizing power distribution and energy output using MPPT and an improved ECMS, and the outer layer switching control strategy based on the system's operating state using state machine control. Experimental results show that the strategy improves the economy of system energy consumption and the performance of the hybrid power system.
Article
Thermodynamics
Fengqi Zhang, Xiaosong Hu, Reza Langari, Lihua Wang, Yahui Cui, Hui Pang
Summary: An adaptive energy management strategy based on the equivalent consumption minimization strategy (ECMS) framework is developed to optimize gearshift commands and torque distribution for automated parallel hybrid electric vehicles. The methodology utilizes flexible torque requests to simultaneously consider drivability and fuel economy, resulting in improved powertrain optimization and promising fuel efficiency.
Article
Engineering, Chemical
Dapai Shi, Shipeng Li, Kangjie Liu, Yun Wang, Ruijun Liu, Junjie Guo
Summary: Under the dual-carbon goal, research on energy conservation and emission reduction of new energy vehicles has once again become a hot topic. This study proposes an adaptive energy management strategy for plug-in hybrid electric vehicles (PHEVs) to improve fuel economy based on intelligent prediction of driving cycles. Simulation results show that the proposed strategy achieves a 9.85% higher fuel saving rate compared to the rule-based strategy and a 5.30% higher rate compared to the ECMS strategy without prediction, further enhancing the fuel saving potential of PHEVs.
Article
Engineering, Electrical & Electronic
Mohammadreza Moghadari, Mohsen Kandidayeni, Loic Boulon, Hicham Chaoui
Summary: This paper compares the operating cost of a single-stack and a multi-stack fuel cell hybrid electric vehicle (FC-HEV), including hydrogen consumption and degradation of the fuel cell (FC). A hierarchical energy management strategy (EMS) is developed for the multi-stack system. Results show that the FC-HEV with a multi-stack structure has lower hydrogen and degradation costs compared to the single-stack structure.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Thermodynamics
Likang Fan, Yufei Wang, Hongqian Wei, Youtong Zhang, Pengyu Zheng, Tianyi Huang, Wei Li
Summary: The paper presents a real-time EMS for PHEVs based on adaptive regulation of multiple parameters, achieving optimized charge depletion stage and improving adaptability and efficiency. The use of ECMS replaces traditional rules, allowing for real-time optimal solutions and addressing torque distribution challenges.
Article
Automation & Control Systems
Yonggang Liu, Jun Xie, Datong Qin, Yuanjian Zhang, Zheng Chen, Guang Li, Yi Zhang
Summary: This study presents the design and control strategy of a clutchless two-speed automatic transmission for electric vehicles. The transmission features a motor-controlled shifting mechanism that enables fast and smooth shifting, and optimizes the torque trajectory during synchronization.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Thermodynamics
Junyan Niu, Weichao Zhuang, Jianwei Ye, Ziyou Song, Guodong Yin, Yuanjian Zhang
Summary: This paper proposes an offline sizing method and an online energy management strategy for electric vehicles with a semi-active hybrid battery system (HBS). The proposed method optimizes the energy management and battery size of the vehicle through modeling and optimization algorithms. The simulation results demonstrate the effectiveness of the proposed method.
Article
Thermodynamics
Zheng Chen, Simin Wu, Shiquan Shen, Yonggang Liu, Fengxiang Guo, Yuanjian Zhang
Summary: In this study, a real-time hierarchical effective and efficient co-optimization control strategy is designed for automated and connected PHEV to co-optimize vehicle velocity and energy management in urban driving scenarios. The feasibility and energy-saving effect of the proposed co-optimization strategy is verified through a traffic-in-the-loop simulator under various urban driving scenarios.
Article
Thermodynamics
Yonggang Liu, Yitao Wu, Xiangyu Wang, Liang Li, Yuanjian Zhang, Zheng Chen
Summary: This paper proposes an imitation reinforcement learning-based algorithm for energy control of hybrid vehicles, aiming to accelerate the solving process and achieve better control performance. The method provides preferable energy reduction for HEVs in arbitrary driving scenarios and suggests an efficient solution instruction for similar mechanical and electrical systems.
Article
Engineering, Electrical & Electronic
Xing Shu, Zheng Chen, Jiangwei Shen, Shiquan Shen, Fengxiang Guo, Yuanjian Zhang, Yonggang Liu
Summary: In this study, an ensemble learning and voltage reconstruction-based framework is proposed to accurately estimate the state of health (SOH) of lithium-ion batteries. By analyzing charging behaviors and using voltage shape fitting method, the SOH can be estimated with high accuracy.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Qiao Xue, Junqiu Li, Zheng Chen, Yuanjian Zhang, Yonggang Liu, Jiangwei Shen
Summary: This paper proposes a deep learning method for online capacity estimation of lithium-ion batteries. The proposed algorithm utilizes a predictive model that combines a convolutional neural network and a long short-term memory unit for automatic feature extraction and target estimation. Experimental results demonstrate that this method can accurately estimate battery capacity using only short voltage and current data.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Thermodynamics
Yuanjian Zhang, Bingzhao Gao, Jingjing Jiang, Chengyuan Liu, Dezong Zhao, Quan Zhou, Zheng Chen, Zhenzhen Lei
Summary: In this paper, a cooperative power management strategy is proposed for range extended electric vehicles (REEVs), utilizing vehicle-environment cooperation and abundant information exchanged in the internet of vehicles (IoVs). The strategy integrates the self-learning explicit equivalent minimization consumption strategy (SL-eECMS) and adaptive neuro-fuzzy inference system (ANFIS) based online charging management within on-board power sources in the REEV. Simulation results and hardware-in-the-loop (HIL) test demonstrate the effectiveness and efficiency of the proposed strategy in managing power flow within power sources in the REEV.
Article
Thermodynamics
Zhuoran Hou, Jianhua Guo, Jihao Li, Jinchen Hu, Wen Sun, Yuanjian Zhang
Summary: This study proposes a vehicle-environment cooperation energy management strategy (VEC-EMS) for cEV based on the explicitly framed cooperation mechanism in IoVs. The strategy achieves optimal energy management for cEVs by integrating the improved radial basis function neural network (iRBF-NN) based velocity prediction and extreme gradient boosting decision tree (XGBoost) based driving condition identification. Evaluation results show that the proposed EMS can manage power flow within the electric powertrain and increase performance by nearly 8% compared with a normal rule-based energy management strategy.
Article
Engineering, Electrical & Electronic
Jie Li, Yonggang Liu, Abbas Fotouhi, Xiangyu Wang, Zheng Chen, Yuanjian Zhang, Liang Li
Summary: In this research, a learning-based method is used to achieve satisfactory fuel economy for connected plug-in hybrid electric vehicles (PHEVs) in car-following scenarios. By leveraging a data-driven energy consumption model and considering the nonlinear efficiency characteristics, an advanced ADP scheme is designed for connected PHEVs. The cooperative information is also incorporated to improve fuel economy and driving safety.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Industrial
Hongqian Zhao, Zheng Chen, Xing Shu, Jiangwei Shen, Zhenzhen Lei, Yuanjian Zhang
Summary: This study presents a hybrid attention and deep learning method for the accurate state of health prediction of lithium-ion batteries. The method calculates temperature difference curves, extracts health features related to capacity degradation, and utilizes a hybrid model combining convolutional neural network, gated recurrent unit recurrent neural network, and attention mechanism for forecasting. The proposed method demonstrates superior prediction performance with estimation errors within 1.3% and satisfactory robustness to battery inconsistency.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Chemistry, Analytical
Wang Li, Mingming Zhang, Dan Han, Hong Yang, Qing Hong, Yanfeng Fang, Zhixin Zhou, Yanfei Shen, Songqin Liu, Chaofeng Huang, Haibin Zhu, Yuanjian Zhang
Summary: Photoelectrochemical (PEC) sensing allows fast, accurate, and highly sensitive detection of biologically important chemicals. However, achieving high selectivity without external biological elements remains a challenge due to the poor selectivity of PEC reactions. This study presents a strategy to address this issue by regulating charge-transfer pathways using polymeric carbon nitride (pCN)-based heterojunction photoelectrodes. By controlling the redox reactions at different semiconductor/electrolyte interfaces, each analyte showed a unique combination of photocurrent-change polarity, leading to the development of a highly selective PEC sensor for ascorbic acid in serum against common interferences.
ANALYTICAL CHEMISTRY
(2023)
Article
Thermodynamics
Jie Li, Abbas Fotouhi, Wenjun Pan, Yonggang Liu, Yuanjian Zhang, Zheng Chen
Summary: This study proposes an eco-driving approach with a hierarchical framework to be leveraged at signalized intersections considering the impact of traffic uncertainty. The proposed method utilizes a queue-based traffic model to estimate traffic uncertainty and generate dynamic modified traffic light information. Additionally, a deep reinforcement learning-based controller is constructed to optimize velocity, reducing energy consumption and ensuring driving safety. Simulation results demonstrate the effectiveness of the proposed control strategy in improving energy economy and preventing unnecessary idling in uncertain traffic scenarios, as compared to approaches that ignore traffic uncertainty. The proposed method is also adaptable to different traffic scenarios and exhibits energy efficiency.
Article
Thermodynamics
Yonggang Liu, Qianyou Chen, Jie Li, Yuanjian Zhang, Zheng Chen, Zhenzhen Lei
Summary: This study investigates the optimization problem of eco-routing on a road network for heterogeneous continuous vehicle flow, and proposes a collaborative heterogeneous multi-vehicle eco-routing optimization strategy to improve the overall economy in the road network.
Article
Computer Science, Information Systems
Shiquan Shen, Shun Gao, Yonggang Liu, Yuanjian Zhang, Jiangwei Shen, Zheng Chen, Zhenzhen Lei
Summary: This study proposes a real-time energy management strategy for plug-in hybrid electric vehicles (PHEVs) by incorporating double-delay Q-Learning and model predictive control (MPC). The results show that this strategy improves the adaptability of energy management to dynamic environments and achieves similar fuel consumption as the offline stochastic dynamic programming-based strategy. Furthermore, the proposed strategy has a computation time of less than 23 milliseconds, highlighting its potential for online implementation.
Proceedings Paper
Automation & Control Systems
Yang Lin, Liang Chu, Jincheng Hu, Yuanjian Zhang, Zhuoran Hou
Summary: This paper proposes an adaptive hierarchical management strategy that combines equivalent consumption minimization strategy (ECMS) with proximal policy optimization (PPO) through deep reinforcement learning algorithm, aiming to optimize the energy management strategy of plug-in hybrid electric vehicles (PHEVs). The results show that the proposed strategy outperforms traditional strategies, indicating the potential of deep reinforcement learning in real-time systems.
2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV)
(2022)
Article
Automation & Control Systems
Ran Jiao, Wenjie Liu, Ramy Rashad, Jianfeng Li, Mingjie Dong, Stefano Stramigioli
Summary: A novel end-effector bilateral rehabilitation robotic system (EBReRS) is developed for upper limb rehabilitation of patients with hemiplegia, providing simulations of multiple bimanual coordinated training modes, showing potential for application in home rehabilitation.
Article
Automation & Control Systems
Qiaosheng Pan, Yifang Zhang, Xiaozhu Chen, Quan Wang, Qiangxian Huang
Summary: A resonant piezoelectric rotary motor using parallel moving gears mechanism has been proposed and tested, showing high power output and efficiency.
