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Synthetical Efficiency-based Optimization for the Power Distribution of Power-split hybrid electric vehicles

Chinese Journal of Mechanical Engineering 2014年第1期27卷 58-68页

作者： WANG Weida HAN Lijin XIANG Changle MA Yue LIU Hui National Key Laboratory of Vehicle Transmission Beijing Institute of Technology

Now the optimization strategies for power distribution are researched widely, and most of them are aiming to the optimal fuel economy and the driving cycle must be preknown. Thus if the actual driving condition deviates from the scheduled driving cycle, the effect of optimal results will be declined greatly. Therefore, the instantaneous optimization strategy carried out on-line is studied in this paper. The power split path and the transmission efficiency are analyzed based on a special power-split scheme and the efficiency models of the power transmitting components are established. The synthetical efficiency optimization model is established for enhancing the transmission efficiency and the fuel economy. The identification of the synthetical efficiency as the optimization objective and the constrain group are discussed emphatically. The optimization is calculated by the adaptive simulated annealing （ASA） algorithm and realized on-line by the radial basis function （RBF）-based similar models. The optimization for power distribution of the hybrid vehicle in an actual driving condition is carried out and the road test results are presented. The test results indicate that the synthetical efficiency optimization method can enhance the transmission efficiency and the fuel economy of the power-split hybrid electric vehicle （HEV） observably. Compared to the rules-based strategy the optimization strategy is optimal and achieves the approximate global optimization solution for the power distribution. The synthetical efficiency optimization solved by ASA algorithm can give attentions to both optimization quality and calculation efficiency, thus it has good application foreground for the power distribution of power-split HEV.

关键词： hybrid electric vehicles power-split synthetical efficiency-based optimization power distribution road test

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Driving-Cycle-Aware Energy Management of hybrid electric vehicles Using a Three-Dimensional Markov Chain Model

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Automotive Innovation 2019年第2期2卷 146-156页

作者： Bolin Zhao Chen Lv Theo Hofman Department of Mechanical Engineering Eindhoven University of TechnologyEindhovenThe Netherlands School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy management strategy.The impacts of different prediction time lengths on driving cycle generation were explored.The results indicate that the original driving cycle is compressed by 50%,which significantly reduces the computational burden while having only a slight effect on the prediction performance.The developed driving cycle prediction method was implemented in a real-time energy management algorithm with a hybrid electric vehicle powertrain model,and the model was verified by simulation using two different testing scenarios.The testing results demonstrate that the developed driving cycle prediction method is able to efficiently predict future driving tasks,and it can be successfully used for the energy management of hybrid electric vehicles.

关键词： Driving cycle prediction Markov chain model hybrid electric vehicles Energy managemen

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A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions

能源与人工智能(英文)

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能源与人工智能(英文) 2021年第3期5卷 1-17页

作者： Claudio Maino Daniela Misul Alessandro Di Mauro Ezio Spessa IC Engines Advanced Laboratory Dipartimento Energia“Galileo Ferraris”Politecnico di Torinoc.so Duca degli Abruzzi 2410129 TorinoItaly

hybrid electric vehicles(HEV)are nowadays proving to be one of the most promising technologies for the im-provement of the fuel economy of several transportation segments.As far as the on-road category is concerned,a wise selection of the powertrain design is needed to exploit the best energetic performance achievable by a HEV.Amongst the methodologies developed for comparing different hybrid architectures,global optimizers have demonstrated the capability of leading to optimal design solutions at the expense of a relevant compu-tational burden.In the present paper,an innovative deep neural networks-based model for the prediction of tank-to-wheel carbon dioxide emissions as estimated by a Dynamic Programming(DP)algorithm is presented.The model consists of a pipeline of neural networks aimed at catching the correlations lying between the de-sign parameters of a HEV architecture and the main outcomes of the DP,namely powertrain feasibility and tail pipe CO_(2) emissions.Moreover,an automatic search tool(AST)has been developed for tuning the main hyper-parameters of the networks.Interesting results have been registered by applying the pipeline to three databases related to three different HEV parallel architectures.The capability of the pipeline has been proved through an extensive testing campaign made up by multiple experiments.Classification performances above 91%as well as average regression errors below 1%have been achieved during an extensive set of simulations.The presented model could hence be considered as an effective tool for supporting HEV design optimization phases.

关键词： Deep neural networks Carbon dioxide emissions hybrid electric vehicles Dynamic programming

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Development of Novel Bipolar Nickel／Metal Hydride Batteries for hybrid electric vehicles

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Chinese Journal of Chemistry 2005年第2期23卷 221-224页

作者：邓超史鹏飞张森 DepartmentofAppliedChemistry HarbinInstituteofTechnologyHarbinHeilongjiang150001China ColledgeofChemicalEngineering HarbinEngineeringUniversityHarbinHeilongjiang150001China

This paper deals with the design and development of bipolar Ni/MH batteries. After optimizing the parameters of bipolar plates by adjusting electrode thickness and modifying the capacity ratio of two adjacent electrodes of a single cell, some bipolar Ni/MH stacks with a voltage of 6 V were assembled and examined. Electrochemical testing results showed that the bipolar battery has excellent high rate discharge and recharge characteristics, satisfying pulse discharge performance even in a low state of charge (SOC). Moreover, the battery showed good stability during pulse cycles as simulating hybrid electric vehicle working conditions. It would be a promising alternative for power storage system in hybrid electric vehicles.

关键词： bipolar Ni/MH battery electrochemical testing hybrid electric vehicles

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Closed-form solution to the dynamic programming for a heavy-duty parallel hybrid vehicle energy management

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Journal of Control and Decision 2024年第1期11卷 107-116页

作者： Tao Zhang Zhongjun Yu Huangda Lin National Key Laboratory for Vessel Integrated Power System Technology Naval University of EngineeringWuhanPeople’s Republic of China

Dynamic programming(DP)is frequently used to obtain the optimal solution to the hybrid electric vehicle(HEV)energy management.The trade-off between the accuracy and the computational effort is the biggest problem for the DP method.The closed-form solution to the DP is proposed to solve this problem.Firstly,the affine linear model of the engine fuel rate is obtained based on engine test data.The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery.Then,the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split.The results show that this method can greatly reduce the computing burden by 93%while ensuring near-optimal fuel economy compared with the conventional DP method.

关键词： Dynamic programming energy management closed-form solutions fuel economy hybrid electric vehicles

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Maximum-Torque-per-Ampere Control of Interior Permanent Magnet Synchronous Machine Applied for hybrid electric vehicles 2011

Maximum-Torque-per-Ampere Control of Interior Permanent Magn...

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2011 International Conference on Control, Automation and Systems Engineering(CASE)

作者： HUANG Peng MIAO Chang-yun LI Hong-qiang ZHANG Cheng School of electrical engineering and automation Tianjin Polytechnic University School of Information and Communication Engineering Tianjin Polytechnic University

Permanent magnet synchronous machines are known as a good candidate for hybrid electric vehicles(HEV) due to their unique merits. According to the characteristic that d,q-axis inductances are not equa

ISBN: (纸本)9781457708596

关键词： IPMSM Maximum-torque-per-ampere control Saturation effect Vector control hybrid electric vehicle Permanent magnet synchronous machines hybrid electric vehicles output torque peak torque maximum principle Engineering interior dynamic characteristics

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Energy management strategy for hybrid electric vehicle integrated with waste heat recovery system based on deep reinforcement learning

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Science China(Technological Sciences) 2022年第3期65卷 713-725页

作者： WANG Xuan WANG Rui SHU GeQun TIAN Hua ZHANG XuanAng State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

hybrid electric vehicles(HEVs)are acknowledged to be an effective way to improve the efficiency of internal combustion engines(ICEs)and reduce fuel consumption.Although the ICE in an HEV can maintain high efficiency during driving,its thermal efficiency is approximately 40%,and the rest of the fuel energy is discharged through different kinds of waste heat.Therefore,it is important to recover the engine waste heat.Because of the great waste heat recovery performance of the organic Rankine cycle(ORC),an HEV integrated with an ORC(HEV-ORC)has been proposed.However,the addition of ORC creates a stiff and multi-energy problem,greatly increasing the complexity of the energy management system(EMS).Considering the great potential of deep reinforcement learning(DRL)for solving complex control problems,this work proposes a DRL-based EMS for an HEV-ORC.The simulation results demonstrate that the DRL-based EMS can save 2%more fuel energy than the rule-based EMS because the former provides higher average efficiencies for both engine and motor,as well as more stable ORC power and battery state.Furthermore,the battery always has sufficient capacity to store the ORC power.Consequently,DRL showed great potential for solving complex energy management problems.

关键词： hybrid electric vehicles organic Rankine cycle waste heat recovery deep reinforcement learning energy management system

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Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

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Control Theory and Technology 2022年第2期20卷 145-160页

作者： Fuguo Xu Hiroki Tsunogawa Junichi Kako Xiaosong Hu Shengbo Eben Li Tielong Shen Lars Eriksson Carlos Guardiola Department of Engineering and Applied Sciences Sophia UniversityTokyo 102-8554Japan Higashi-Fuji Research Center Toyota Motor Corporation.Shizuoka 410-1107Japan The State Key Laboratory of Mechanical Transmissions Chongqing UniversityChongqing 400044China The State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijing 100084China Vehicular Systems Department Electrical EngineeringLinkoping UniversitySE 58183Linkoping.Sweden Departamento de Maquinas y Motores Termicos Universitat Politecnica de Valencia.Camino de Vera s/n46022 ValenciaSpain

In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.

关键词： Powertrain control Connected and automated vehicles hybrid electric vehicles Vehicle-to-everything

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Increment Dynamic Adjustment Method for hybrid Energy Source System in electric vehicles

Increment Dynamic Adjustment Method for Hybrid Energy Source...

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第37届中国控制会议

作者： Kong Zhiguo Huang Xin He Penglin Wang Bin Li Wenshuai Ma Kai China Automotive Technology and Research Center

To solve the power flow management problem of two energy sources in a composite energy source system, a incremental dynamic optimization method is proposed in this paper. The incremental adjustment criteria are studied and the penalty coefficients is introduced to optimize the increment. Further, the realization of the energy management strategy of an electric bus equipped with CHS hybrid power system is presented. The bus was tested on the bench with CCBC condition and operates on bus lines. And the test results and operating data demonstrate the effectiveness and robustness of the proposed method.

关键词： increment penalty coefficients composite energy source hybrid electric vehicles

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Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life

能源与人工智能(英文)

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能源与人工智能(英文) 2023年第1期11卷 118-128页

作者： Saeid Ahmadian Mohammad Tahmasbi Reza Abedi Department of Mechanical Engineering Sharif University of TechnologyTehranIran Department of electrical and Computer Engineering Concordia UniversityMontrealQCCanada Department of Mechanical Engineering K.N.Toosi University of TechnologyTehranIran

The present study investigates an energy management strategy based on reinforcement learning for seriesparallel hybrid vehicles. hybrid electric vehicles allow using more advanced power management policies because of their complexity of power management. Towards this feature, a Q-Learning algorithm is proposed to design an energy management strategy. Compared to previous studies, an online reward function is defined to optimize fuel consumption and battery life cycle. Moreover, in the provided method, prior knowledge of the cycle and exact modeling of the vehicle are not required. The introduced strategy is simulated for four driving cycles in MATLAB software linked with ADVISOR. The simulation results show that in the HWFET cycle, the fuel consumption decreases by 1.25 %, and battery life increases by 65% compared to the rule-based method implemented in ADVISOR. Also, the results for the other driving cycles confirm the self-improvement property. In addition, it has been depicted that in the case of change in the driving cycle, the method performance has been maintained and gained better performance than the rule-based controller.

关键词： hybrid electric vehicles Series-parallel configuration Power management policy Reinforcement learning Q-Learning

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