Journal articles: 'Energy management strategies for hybrid electric vehicles'

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PIELECHA, Ireneusz, Wojciech CIEŚLIK, and Kinga FLUDER. "Analysis of energy management strategies for hybrid electric vehicles in urban driving conditions." Combustion Engines 173, no. 2 (April 1, 2018): 14–18. http://dx.doi.org/10.19206/ce-2018-203.

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The pursuit of fuel consumption reduction by vehicles leads to a sudden increase in the share of hybrid and electric drives in the vehicle market. Replacing hybrid vehicles with electric vehicles requires long-term technological solutions, both for the infrastructure and the vehicles themselves. Therefore, one of the leading types of passenger car drives is currently the hybrid drive. The generated work share of electric drives used to power hybrid vehicles is a determinant of the viability of using electric drives. The article estimates the operating time share of electric and hybrid modes operation in real driving conditions (RDC) based on the latest Toyota hybrid model. The research object was a vehicle from the crossover group equipped with a fourth generation hybrid drive. Analysis of the drives operation allowed to determine the conditions of energy flow and determine the work share of the electric drive in the total driving time.

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Panday, Aishwarya, and Hari Om Bansal. "A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle." International Journal of Vehicular Technology 2014 (November 18, 2014): 1–19. http://dx.doi.org/10.1155/2014/160510.

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Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

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Franceschi, Alessandro, Nicolò Cavina, Riccardo Parenti, Maurizio Reggiani, and Enrico Corti. "Energy Management Optimization of a Dual Motor Lithium Ion Capacitors-Based Hybrid Super Sport Car." Applied Sciences 11, no. 2 (January 19, 2021): 885. http://dx.doi.org/10.3390/app11020885.

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Nowadays, hybrid electric vehicles represent one of the main solutions for the reduction of greenhouse gases in the automotive sector. Alongside the reduction of CO2, hybrid electric vehicles serve as a strong alternative on drivability and performance to conventional internal combustion engine-based vehicles. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid electric vehicles can differ in the powertrain configuration and the choice of the energy storage system. Manufacturers have recently started to work on Lithium-Ion Capacitors (LiC) -based hybrid vehicles. This paper discusses the usage of a control-oriented vehicle and powertrain model to analyze the performance of a dual motor LiC-based hybrid V12 vehicle by Automobili Lamborghini. P3–P4 and P2–P4 parallel hybrid configurations have been selected and compared since they allow to fully exploit the potential of the LiC storage system characterized by high power. The validated model has been used to develop control strategies aimed at fuel economy and CO2 reduction, and in particular, both Rule Based Strategies (RBS) and Equivalent Consumption Minimization Strategies (ECMS) are presented in the paper. A critical comparison between the various powertrain configurations is carried out, keeping into account the peculiarities of the LiC technology and evaluating the performance of the different control approaches.

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Song, Ziyou, Heath Hofmann, Jianqiu Li, Jun Hou, Xuebing Han, and Minggao Ouyang. "Energy management strategies comparison for electric vehicles with hybrid energy storage system." Applied Energy 134 (December 2014): 321–31. http://dx.doi.org/10.1016/j.apenergy.2014.08.035.

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Shen, Caiying, Peng Shan, and Tao Gao. "A Comprehensive Overview of Hybrid Electric Vehicles." International Journal of Vehicular Technology 2011 (November 24, 2011): 1–7. http://dx.doi.org/10.1155/2011/571683.

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As the environmental pollution and energy crises are getting more and more remarkable, hybrid electric vehicles (HEVs) have taken on an accelerated pace in the world. A comprehensive overview of HEVs is presented in this paper, with the emphasis on configurations, main issues, and energy management strategies. Conclusions are discussed finally.

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Obu Showers, Samson, and Atanda Kamoru Raji. "State-of-the-art review of fuel cell hybrid electric vehicle energy management systems." AIMS Energy 10, no. 3 (2022): 458–85. http://dx.doi.org/10.3934/energy.2022023.

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<abstract> <p>The primary purpose of fuel cell hybrid electric vehicles (FCHEVs) is to tackle the challenge of environmental pollution associated with road transport. However, to benefit from the enormous advantages presented by FCHEVs, an appropriate energy management system (EMS) is necessary for effective power distribution between the fuel cell and the energy storage systems (ESSs). The past decade has brought a significant increase in the number of FCHEVs, with different EMSs having been implemented due to technology advancement and government policies. These methods are broadly categorised into rule-based EMS methods, machine learning methods and optimisation-based control methods. Therefore, this paper presents a systematic literature review on the different EMSs and strategies used in FCHEVs, with special focus on fuel cell/lithium-ion battery hybrid electric vehicles. The contribution of this study is that it presents a quantitative evaluation of the different EMSs selected by comparing and categorising them according to principles, technology maturity, advantages and disadvantages. In addition, considering the drawbacks of some EMSs, gaps were highlighted for future research to create the pathway for comprehensive emerging solutions. Therefore, the results of this paper will be beneficial to researchers and electric vehicle designers saddled with the responsibility of implementing an efficient EMS for vehicular applications.</p> </abstract>

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Dwivedi, Shailendra, and Yash Dave. "Energy Management And Control Strategies For Parallel Hybrid Eleсtriс Vehiсle." Journal of Futuristic Sciences and Applications 5, no. 1 (2022): 65–70. http://dx.doi.org/10.51976/jfsa.512209.

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For the parallel hybrid electric vehicle, the various control strategies for energy management are illustrated with the implementation of fuzzy logic. The controller is designed and simulated in two modes for the economy and fuel optimisation. In order to manage the energy in HEV with three separate energy sources—batteries, Fuel cell and a supercapacitor system, —this article intends to create a fuzzy logic controller. By considering a complete system, the operating efficiency of the components need to be optimized. the control strategy implementation will be performed by the forward-facing approach. The fuel economy is optimised by maximising the operating efficiency in this strategy while other strategies does not have this extra aspect. The ability controller for parallel hybrid vehicles is mentioned in this research to enhance fuel economy. Although the earlier installed power controllers optimise operation, they do not fully utilise the capabilities. Hybrid vehicles can be equipped with a variety of power and energy sources such as batteries, internal combustion engines , fuel cell systems, supercapacitor systems or flywheel systems.

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Sorlei, Ioan-Sorin, Nicu Bizon, Phatiphat Thounthong, Mihai Varlam, Elena Carcadea, Mihai Culcer, Mariana Iliescu, and Mircea Raceanu. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies." Energies 14, no. 1 (January 5, 2021): 252. http://dx.doi.org/10.3390/en14010252.

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With the development of technologies in recent decades and the imposition of international standards to reduce greenhouse gas emissions, car manufacturers have turned their attention to new technologies related to electric/hybrid vehicles and electric fuel cell vehicles. This paper focuses on electric fuel cell vehicles, which optimally combine the fuel cell system with hybrid energy storage systems, represented by batteries and ultracapacitors, to meet the dynamic power demand required by the electric motor and auxiliary systems. This paper compares the latest proposed topologies for fuel cell electric vehicles and reveals the new technologies and DC/DC converters involved to generate up-to-date information for researchers and developers interested in this specialized field. From a software point of view, the latest energy management strategies are analyzed and compared with the reference strategies, taking into account performance indicators such as energy efficiency, hydrogen consumption and degradation of the subsystems involved, which is the main challenge for car developers. The advantages and disadvantages of three types of strategies (rule-based strategies, optimization-based strategies and learning-based strategies) are discussed. Thus, future software developers can focus on new control algorithms in the area of artificial intelligence developed to meet the challenges posed by new technologies for autonomous vehicles.

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Ma, Yunfei, Di Shi, Jieni Song, and Sishi Zuo. "Review on Energy Management Strategies of PHEV." Highlights in Science, Engineering and Technology 3 (July 8, 2022): 144–57. http://dx.doi.org/10.54097/hset.v3i.703.

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The problems of air pollution and the shortage of energy sources are increasingly significant, and the traditional automobile industry plays a vital role in it. Consequently, Energy management strategy can adjust the energy allocation between the engine and the motor, achieving the purpose of improving fuel economy. Meanwhile, it can optimize the battery balance so that the comprehensive cost of the hybrid vehicle can be reduced, apart from the limited emissions and conserved energy. This paper first introduces the machinery configuration of the parallel hybrid electric vehicles, and then classifies their working patterns under different working conditions. After analysing the existing energy management strategies, we introduce and analyse the rule-based ones by two frequently used methods: logic threshold method and fuzzy logic control. Additionally, strategies based on optimization, including instantaneous and global optimization strategy, are also introduced and analysed, with each advantages, limitations and potential innovations. Finally, the contradiction between practicability and control effect is identified, which is one of the crucial problems to be resolved.

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Millo, Federico, Luciano Rolando, Rocco Fuso, Erik Bergshoeff, and Farshid Shafiabady. "Analysis of Different Energy Management Strategies for Complex Hybrid Electric Vehicles." Computer-Aided Design and Applications 11, sup1 (May 30, 2014): S1—S10. http://dx.doi.org/10.1080/16864360.2014.914399.

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M. Sabri, M. F., K. A. Danapalasingam, and M. F. Rahmat. "A review on hybrid electric vehicles architecture and energy management strategies." Renewable and Sustainable Energy Reviews 53 (January 2016): 1433–42. http://dx.doi.org/10.1016/j.rser.2015.09.036.

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van Reeven, Vital, and Theo Hofman. "Multi-Level Energy Management—Part II: Implementation and Validation." Vehicles 1, no. 1 (February 15, 2019): 41–56. http://dx.doi.org/10.3390/vehicles1010003.

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In hybrid electric vehicles, energy management systems (EMS) using optimization show superior fuel efficiency compared to rule-based strategies. However, little research shows its real-life applicability. In Part II of this work, the multi-level, model-predictive EMS from Part I is implemented on a heavy-duty parallel hybrid electric vehicle, using GPS and map data as preview. The power split, hybrid mode, and gear selection, including switching costs, are optimized in real time, thereby proving the feasibility of optimal control techniques for hybrid driveline control. Functional validation of the EMS on a test track confirm the fuel-saving mechanism as simulated in Part I. In addition to a fuel saving of 36%, the EMS also improves the drivability, by reducing the amount of open driveline events.

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Grondin, Olivier, Laurent Thibault, and Carole Quérel. "Energy Management Strategies for Diesel Hybrid Electric Vehicle." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 70, no. 1 (April 11, 2014): 125–41. http://dx.doi.org/10.2516/ogst/2013215.

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Zhang, Fengqi, Lihua Wang, Serdar Coskun, Hui Pang, Yahui Cui, and Junqiang Xi. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook." Energies 13, no. 13 (June 30, 2020): 3352. http://dx.doi.org/10.3390/en13133352.

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Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

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Liu, Teng, Wenhao Tan, Xiaolin Tang, Jinwei Zhang, Yang Xing, and Dongpu Cao. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review." Renewable and Sustainable Energy Reviews 151 (November 2021): 111521. http://dx.doi.org/10.1016/j.rser.2021.111521.

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Zhu, Yuzheng, Xueyuan Li, Qi Liu, Songhao Li, and Yao Xu. "Review article: A comprehensive review of energy management strategies for hybrid electric vehicles." Mechanical Sciences 13, no. 1 (March 11, 2022): 147–88. http://dx.doi.org/10.5194/ms-13-147-2022.

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Abstract. In order to prevent the aggravation of global environmental problems, all industries are facing the challenge of green development. In the automotive field, the development of “new-energy vehicles” (plug-in electric vehicles) is particularly necessary. Hybrid electric vehicles (HEVs) have been proven to be an efficient way of solving environmental and energy problems. As the core of HEVs, the energy management strategy (EMS) plays an important role in fuel economy, power performance, and drivability. However, considering the randomness of actual driving conditions, there are great challenges involved in the establishment of an EMS. Therefore, it is critical to develop an efficient and adaptable EMS. This paper presents a systematic review of EMSs for HEVs. First, different issues that can affect the performance of EMSs are summarized. Second, recent studies on EMSs for HEVs are reviewed. Third, the advantages and disadvantages of different categories of EMSs are compared in detail. Finally, promising EMS research topics for future study are put forward.

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Hordiienko, Mykola, Oleksandr Parkhomenko, and Vladyslav Podpisnov. "WLTC measuring driving cycle (power reserve measurement procedure for hybrids and electric vehicles)." Vehicle and electronics. Innovative technologies, no. 22 (December 27, 2022): 37–46. http://dx.doi.org/10.30977/veit.2022.22.0.9.

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Problem. The most effective energy management strategies for hybrid vehicles and electric vehicles are optimization-based strategies. These strategies require prior knowledge of the driving cycle, which is not easy to predict. Goal. The goal is to combine the Worldwide harmonized light vehicles test cycle (WLTC) with short trips on small sections with real traffic levels to predict the energy and fuel consumption of hybrid vehicles and electric vehicles. Methodology. Research methods are experimental and mathematical. First of all, eight characteristic parameters are extracted from real speed profiles used on urban road sections in the city of Kharkiv under various road conditions, as well as on short WLTC trips. The minimum distance algorithm is used to compare parameters and determine three traffic levels (heavy, medium, and low traffic) for short WLTC trips. Thus, for each route determined using Google Maps, the energy and fuel consumption of hybrid vehicles and electric vehicles are estimated using short trips by the WLTC, adjusted for distances and traffic levels. In addition, a numerical model of the vehicle was implemented. It was used to test the accuracy of predicting fuel and energy consumption in accordance with the proposed methodology. Originality. For the methodology using only GM information is required as input data; no other device or software is required. This aspect makes the methodology extremely economical. Then, the algorithm regulating traffic levels shown by GM is unique and valid in all urban centers. This aspect makes the methodology universal. WLTC takes into account the driving styles of drivers around the world, so the methodology can be applied to any car driver. Prediction accuracy can be increased by taking into account other input information, such as the distribution of traffic light signals or the driver's typical gear shifting style. Results. The results are promising, as the average absolute percentage error between experimental driving cycles and projected ones is 3.89 % for fuel consumption, increasing to 6.80 % for energy consumption. Practical value. The possibility of energy forecasting and fuel consumption for a hybrid vehicle and an electric vehicle makes it possible to develop energy consumption management systems for HEVs that can manage the energy reserve to ensure full travel by electric traction in limited traffic zone (LTZ) or minimize local air pollution; increase the service life of energy reserves (usually batteries) by maintenance costs and disposal problems reducing; optimize the transmission-use efficiency due to fuel consumption and pollutants emissions reduction.

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Al-Saadi, Ziad, Duong Phan Van, Ali Moradi Amani, Mojgan Fayyazi, Samaneh Sadat Sajjadi, Dinh Ba Pham, Reza Jazar, and Hamid Khayyam. "Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles." Sustainability 14, no. 15 (July 31, 2022): 9378. http://dx.doi.org/10.3390/su14159378.

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Automotive companies continue to develop integrated safety, sustainability, and reliability features that can help mitigate some of the most common driving risks associated with autonomous vehicles (AVs). Hybrid electric vehicles (HEVs) offer practical solutions to use control strategies to cut down fuel usage and emissions. AVs and HEVs are combined to take the advantages of each kind to solve the problem of wasting energy. This paper presents an intelligent driver assistance system, including adaptive cruise control (ACC) and an energy management system (EMS), for HEVs. Our proposed ACC determines the desired acceleration and safe distance with the lead car through a switched model predictive control (MPC) and a neuro-fuzzy (NF) system. The performance criteria of the switched MPC toggles between speed and distance control appropriately and its stability is mathematically proven. The EMS intelligently control the energy consumption based on ACC commands. The results show that the driving risk is extremely reduced by using ACC-MPC and ACC-NF, and the vehicle energy consumption by driver assistance system based on ACC-NF is improved by 2.6%.

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Yu, Pengli, Mince Li, Yujie Wang, and Zonghai Chen. "Fuel Cell Hybrid Electric Vehicles: A Review of Topologies and Energy Management Strategies." World Electric Vehicle Journal 13, no. 9 (September 16, 2022): 172. http://dx.doi.org/10.3390/wevj13090172.

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With the development of the global economy, the automobile industry is also developing constantly. In recent years, due to the shortage of environmental energy and other problems, seeking clean energy as the power source of vehicles to replace traditional fossil energy could be one of the measures to reduce environmental pollution. Among them, fuel cell hybrid electric vehicles (FCHEVs) have been widely studied by researchers for their advantages of high energy efficiency, environmental protection, and long driving range. This paper first introduces the topology of common FCHEVs and then classifies and introduces the latest energy management strategies (EMSs) for FCHEVs. Finally, the future trends of EMSs for FCHEVs are discussed. This paper can be useful in helping researchers better understand the recent research progress of EMSs for FCHEVs.

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Lu, languang. "ENERGY MANAGEMENT STRATEGIES FOR FUEL CELL HYBRID ELECTRIC VEHICLE." Chinese Journal of Mechanical Engineering 41, no. 12 (2005): 8. http://dx.doi.org/10.3901/jme.2005.12.008.

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Sidharthan, Vishnu P., Yashwant Kashyap, and Panagiotis Kosmopoulos. "Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles." Energies 16, no. 3 (January 22, 2023): 1214. http://dx.doi.org/10.3390/en16031214.

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The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery performance and lifespan of battery electric vehicles (BEVs). Hence, as a singular power source, batteries have difficulty in tackling these issues in BEVs, highlighting the significance of hybrid-source electric vehicles (HSEVs). The supercapacitor (SC) and photovoltaic panels (PVs) are the auxiliary power sources coupled with the battery in the proposed hybrid electric three-wheeler (3W). However, energy management strategies (EMS) are critical to ensure optimal and safe power allocation in HSEVs. A novel adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) is proposed to perform energy management in hybrid sources. The IHSEMS optimizes the power sources using an absolute energy-sharing algorithm to meet the required motor power demand using the fuzzy logic controller. Techno-economic assessment wass conducted to analyze the effectiveness of the IHSEMS. Based on the comprehensive discussion, the proposed strategy reduces peak battery power by 50.20% compared to BEVs. It also reduces the battery capacity loss by 48.1 %, 44%, and 24%, and reduces total operation cost by 60%, 43.9%, and 23.68% compared with standard BEVs, state machine control (SMC), and frequency decoupling strategy (FDS), respectively.

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Shi, Qing Sheng, Xiao Ping Zhang, and Fuan Chen. "Multi-Lookup Table Based Regenerative Braking Strategy of Plug-in Hybrid Electric Vehicle." Applied Mechanics and Materials 44-47 (December 2010): 1509–13. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1509.

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. In order to improve the energy efficiency of plug-in hybrid electric vehicles, it is important to design a suitable regenerative braking strategy. There are many control strategies that have been developed and presented for plug-in hybrid electric vehicles. Most of them are aimed to energy flow management, and seldom involves regenerative braking control. In this paper, a regenerative braking strategy based on multi-lookup table method is proposed for plug-in hybrid electric vehicles. Decelerations are introduced as the index of Table Selector, so braking force distribution coefficients can be flexibly adjusted using the proposed strategy. Finally, the simulation results show the validity of the novel strategy.

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Alleyne, Andrew, Timothy Deppen, Jonathan Meyer, and Kim Stelson. "Enery management in Mobile Hydraulics." Mechanical Engineering 135, no. 06 (June 1, 2013): S4—S6. http://dx.doi.org/10.1115/1.2013-jun-5.

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This paper explores research into hydraulic hybrids that span a wide range of applications from heavy-duty vehicles, such as city buses, to small passenger vehicles. This case study also highlights the importance of having a well-designed energy management strategy if one is to maximize benefit of the hybrid powertrain. There is potential for hydraulic hybrid vehicles to offer a cost-effective solution to the need for increased efficiency in transportation systems. The high-power density of fluid power makes it a natural choice for energy storage in urban driving environments where there are frequent starts/stops and large acceleration/braking power demands. Because the opportunities and challenges of fluid power are different than those of electrical power, unique control strategies are needed and a summary of common energy management strategies (EMS) design methods for hydraulic hybrids has been presented.

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Du, Changqing, Shiyang Huang, Yuyao Jiang, Dongmei Wu, and Yang Li. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming." Energies 15, no. 12 (June 13, 2022): 4325. http://dx.doi.org/10.3390/en15124325.

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Fuel cell hybrid electric vehicles have attracted a large amount of attention in recent years owing to their advantages of zero emissions, high efficiency and low noise. To improve the fuel economy and system durability of vehicles, this paper proposes an energy management strategy optimization method for fuel cell hybrid electric vehicles based on dynamic programming. Rule-based and dynamic-programming-based strategies are developed based on building a fuel cell/battery hybrid system model. The rule-based strategy is improved with a power distribution scheme of dynamic programming strategy to improve the fuel economy of the vehicle. Furthermore, a limit on the rate of change of the output power of the fuel cell system is added to the rule-based strategy to avoid large load changes to improve the durability of the fuel cell. The simulation results show that the equivalent 100 km hydrogen consumption of the strategy based on the dynamic programming optimization rules is reduced by 6.46% compared with that before the improvement, and by limiting the rate of change of the output power of the fuel cell system, the times of large load changes are reduced. Therefore, the strategy based on the dynamic programming optimization rules effectively improves the fuel economy and system durability of vehicles.

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Zhao, Xiuliang, Lei Wang, Yinglong Zhou, Bangxiong Pan, Ruochen Wang, Limei Wang, and Xueqing Yan. "Energy management strategies for fuel cell hybrid electric vehicles: Classification, comparison, and outlook." Energy Conversion and Management 270 (October 2022): 116179. http://dx.doi.org/10.1016/j.enconman.2022.116179.

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Wang, Ximing, Hongwen He, Fengchun Sun, Xiaokun Sun, and Henglu Tang. "Comparative Study on Different Energy Management Strategies for Plug-In Hybrid Electric Vehicles." Energies 6, no. 11 (October 29, 2013): 5656–75. http://dx.doi.org/10.3390/en6115656.

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Qiu, Shaolin, Lihong Qiu, Lijun Qian, and Pierluigi Pisu. "Hierarchical energy management control strategies for connected hybrid electric vehicles considering efficiencies feedback." Simulation Modelling Practice and Theory 90 (January 2019): 1–15. http://dx.doi.org/10.1016/j.simpat.2018.10.008.

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Debata, Seshadev, Chandan Kumar Samanta, and Siba Prasada Panigrahi. "Efficient energy management strategies for hybrid electric vehicles using shuffled frog-leaping algorithm." International Journal of Sustainable Engineering 8, no. 2 (May 16, 2014): 138–44. http://dx.doi.org/10.1080/19397038.2014.919363.

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Sun, Binbin, Tianqi Gu, Mengxue Xie, Pengwei Wang, Song Gao, and Xi Zhang. "Strategy Design and Performance Analysis of an Electromechanical Flywheel Hybrid Scheme for Electric Vehicles." Sustainability 14, no. 17 (September 3, 2022): 11017. http://dx.doi.org/10.3390/su141711017.

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Energy management strategies are one of the key factors affecting the working efficiency of electric vehicle energy power systems. At present, electric vehicles will develop real-time and efficient energy management strategies according to the topology of on-board energy power system to improve the driving performance of vehicles. In this paper, a new electromechanical flywheel hybrid system is studied. Firstly, the characteristics of the topological scheme of the electromechanical flywheel hybrid system are analyzed, and the working modes are designed. Secondly, in order to improve the efficiency of vehicles’ energy utilization and ensure the real-time performance of the management strategy, an energy management strategy based on fuzzy rules is designed with the flywheel’s state of energy (SOE) as the key reference parameter. Then, considering the directional stability in the braking process, the braking force distribution strategy between the front axle and the rear axle is designed. In order to improve the braking energy recovery efficiency, the secondary distribution strategy consisting of a mechanical braking force and regenerative braking force on the front and rear axles is designed. Finally, the bench test of a electromechanical flywheel hybrid system is carried out. Experiments show that compared with the original dual-motor four-wheel drive scheme, the electromechanical flywheel hybrid four-wheel drive system scheme developed in this paper can reduce the current variation range of lithium batteries by 43.16%, increase the average efficiency by 1.04%, and increase the braking energy recovery rate by 40.61% under the Japan urban cycle conditions. In addition, taking advantage of the energy and power regulation advantages of the electromechanical flywheel device, the power consumption of the lithium battery is reduced by 1.82% under cycling conditions.

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Previti, Umberto, Sebastian Brusca, Antonio Galvagno, and Fabio Famoso. "Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles." Sustainability 14, no. 19 (September 29, 2022): 12411. http://dx.doi.org/10.3390/su141912411.

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Nowadays, the automotive market has showed great interest in the diffusion of Hybrid Electric Vehicles (HEVs). Despite their low emissions and energy consumptions, if compared with traditional fossil fuel vehicles, their architecture is much more complex and presents critical issues in relation to the combined use of the internal combustion engine (ICE), the electric machine and the battery pack. The aim of this paper is to investigate lithium-ion battery usage when coupled with an optimization-based strategy in terms of the overall energy management for a specific hybrid vehicle. A mathematical model for the power train of a Peugeot 508 RXH was implemented. A rule-based energy management system (EMS) was developed and optimized using real data from the driving cycles of two different paths located in Messina. A mathematical model of the battery was implemented to evaluate the variation of its voltage and state of charge (SOC) during the execution of driving cycles. Similarly, a mathematical model was implemented to analyze the state of health (SOH) of the battery after the application of electrical loads. It was thus possible to consider the impact of the energy management system not only on fuel consumption but also on the battery pack aging. Three different scenarios, in terms of battery usage at the starting SOC values (low, medium, and maximum level) were simulated. The results of these simulations highlight the degradation and aging of the studied battery in terms of the chosen parameters of the rule-based optimized EMS.

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Guo, Zhiqi, Jianhua Guo, Liang Chu, Chong Guo, Jincheng Hu, and Zhuoran Hou. "A Hierarchical Energy Management Strategy for 4WD Plug-In Hybrid Electric Vehicles." Machines 10, no. 10 (October 18, 2022): 947. http://dx.doi.org/10.3390/machines10100947.

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In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies.

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Geetha, A., and C. Subramani. "A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles." International Journal of Energy Research 41, no. 13 (March 6, 2017): 1817–34. http://dx.doi.org/10.1002/er.3730.

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Zhang, Qian, Shaopeng Tian, and Xinyan Lin. "Recent Advances and Applications of AI-Based Mathematical Modeling in Predictive Control of Hybrid Electric Vehicle Energy Management in China." Electronics 12, no. 2 (January 14, 2023): 445. http://dx.doi.org/10.3390/electronics12020445.

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Artificial intelligence is widely used in mathematical modeling. The technical means in mathematical modeling are more and more diversified, especially the application of artificial intelligence algorithm greatly promotes the development of mathematical modeling. In recent years, because of its great influence on the fuel consumption, output power and exhaust performance of automobiles, the control strategy has become a research hotspot and focus in automobile R&D industry. Therefore, based on the relevant research results in recent years, after studying and analyzing the typical control strategies of hybrid vehicles, this paper finally puts forward the energy management strategy of hybrid vehicles based on model predictive control (MPC), and strives to contribute to the academic research of energy management strategies of hybrid vehicles.

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Galvagno, Antonio, Umberto Previti, Fabio Famoso, and Sebastian Brusca. "An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles." Energies 14, no. 6 (March 11, 2021): 1548. http://dx.doi.org/10.3390/en14061548.

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The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.

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Zhu, Di, Ewan Pritchard, Sumanth Dadam, Vivek Kumar, and Yang Xu. "Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming." Combustion Engines 184, no. 1 (March 30, 2021): 3–10. http://dx.doi.org/10.19206/ce-131967.

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Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

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Mallon, Kevin, and Francis Assadian. "A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems." Energies 15, no. 2 (January 14, 2022): 600. http://dx.doi.org/10.3390/en15020600.

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Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy.

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Kandidayeni, M., J. P. Trovão, M. Soleymani, and L. Boulon. "Towards health-aware energy management strategies in fuel cell hybrid electric vehicles: A review." International Journal of Hydrogen Energy 47, no. 17 (February 2022): 10021–43. http://dx.doi.org/10.1016/j.ijhydene.2022.01.064.

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Zhang, Pei, Fuwu Yan, and Changqing Du. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics." Renewable and Sustainable Energy Reviews 48 (August 2015): 88–104. http://dx.doi.org/10.1016/j.rser.2015.03.093.

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HomChaudhuri, Baisravan, Runing Lin, and Pierluigi Pisu. "Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads." Transportation Research Part C: Emerging Technologies 62 (January 2016): 70–86. http://dx.doi.org/10.1016/j.trc.2015.11.013.

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Qiu, Lihong, Lijun Qian, Hesam Zomorodi, and Pierluigi Pisu. "Global optimal energy management control strategies for connected four-wheel-drive hybrid electric vehicles." IET Intelligent Transport Systems 11, no. 5 (June 1, 2017): 264–72. http://dx.doi.org/10.1049/iet-its.2016.0197.

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Schmid, Roland, Johannes Bürger, and Naim Bajcinca. "A comparison of PMP-based Energy Management Strategies for Plug-in-Hybrid Electric Vehicles." IFAC-PapersOnLine 52, no. 5 (2019): 592–97. http://dx.doi.org/10.1016/j.ifacol.2019.09.094.

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Lü, Xueqin, Siwei Li, XiangHuan He, Chengzhi Xie, Songjie He, Yuzhe Xu, Jian Fang, Min Zhang, and Xingwu Yang. "Hybrid electric vehicles: A review of energy management strategies based on model predictive control." Journal of Energy Storage 56 (December 2022): 106112. http://dx.doi.org/10.1016/j.est.2022.106112.

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Nassar, Mohamed Y., Mohamed L. Shaltout, and Hesham A. Hegazi. "Multi-objective optimum energy management strategies for parallel hybrid electric vehicles: A comparative study." Energy Conversion and Management 277 (February 2023): 116683. http://dx.doi.org/10.1016/j.enconman.2023.116683.

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Wang, Yaqian, and Xiaohong Jiao. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles." Energies 15, no. 9 (April 28, 2022): 3235. http://dx.doi.org/10.3390/en15093235.

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This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL)-based strategy, simulation results verify the robustness of fuel economy and the adaptability of the power-split optimization of the proposed EMS to different driving conditions.

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Zanelli, Alessandro, Emanuele Servetto, Philippe De Araujo, Sujeet Nagaraj Vankayala, and Adam Vondrak. "Numerical Assessment of Auto-Adaptive Energy Management Strategies Based on SOC Feedback, Driving Pattern Recognition and Prediction Techniques." Energies 15, no. 11 (May 25, 2022): 3896. http://dx.doi.org/10.3390/en15113896.

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The Equivalent Consumption Minimization Strategy (ECMS) is a well-known control strategy for the definition of optimal power-split in hybrid-electric vehicles, because of its effectiveness and reduced calibration effort. In this kind of Energy Management Systems (EMS), the correct identification of an equivalence factor (K), which translates electric power in equivalent fuel consumption, is of paramount importance. To guarantee charge sustaining operation, the K factor must be adjusted to different mission profiles. Adaptive ECMS (A-ECMS) techniques have thus been introduced, which automatically determine the optimal equivalence factor based on the vehicle mission. The aim of this research activity is to assess the potential in terms of fuel consumption and charge sustainability of different A-ECMS techniques on a gasoline hybrid-electric passenger car. First, the 0D vehicle and powertrain model was developed in the commercial CAE software GT-SUITE. An ECMS-based EMS was used to control the baseline powertrain and three alternative versions of an auto-adaptive algorithm were implemented on top of that. The first A-ECMS under study was based on feedback from the battery State of Charge, while the second and third on a Driving Pattern Recognition/Prediction algorithm. Fuel consumption was assessed using the New European Driving Cycle (NEDC), the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Real Driving Emissions (RDE) driving cycles by means of numerical simulation. A potential improvement of up to 4% Fuel Economy was ultimately achieved on an RDE driving cycle with respect to the baseline ECMS.

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Chen, Tzu-Chia, Fouad Jameel Ibrahim Alazzawi, John William Grimaldo Guerrero, Paitoon Chetthamrongchai, Aleksei Dorofeev, Aras masood Ismael, Alim Al Ayub Ahmed, Ravil Akhmadeev, Asslia Johar Latipah, and Hussein Mohammed Esmail Abu Al-Rejal. "Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles." Mathematical Problems in Engineering 2022 (January 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/3693263.

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The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

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Bagwe, Rishikesh Mahesh, Andy Byerly, Euzeli Cipriano dos Santos, and Ben-Miled. "Adaptive Rule-Based Energy Management Strategy for a Parallel HEV." Energies 12, no. 23 (November 24, 2019): 4472. http://dx.doi.org/10.3390/en12234472.

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This paper proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (HEV). The aim of the strategy is to facilitate the aftermarket hybridization of medium- and heavy-duty vehicles. ARBS can be deployed online to optimize fuel consumption without any detailed knowledge of the engine efficiency map of the vehicle or the entire duty cycle. The proposed strategy improves upon the established Preliminary Rule-Based Strategy (PRBS), which has been adopted in commercial vehicles, by dynamically adjusting the regions of operations of the engine and the motor. It prevents the engine from operating in highly inefficient regions while reducing the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink®, both the proposed ARBS and the established PRBS strategies are compared over an extended duty cycle consisting of both urban and highway segments. The results show that ARBS can achieve high MPGe with different thresholds for the boundary between the motor region and the engine region. In contrast, PRBS can achieve high MPGe only if this boundary is carefully established from the engine efficiency map. This difference between the two strategies makes the ARBS particularly suitable for aftermarket hybridization where full knowledge of the engine efficiency map may not be available.

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Rabinowitz, Aaron, Farhang Motallebi Araghi, Tushar Gaikwad, Zachary D. Asher, and Thomas H. Bradley. "Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles." Energies 14, no. 18 (September 10, 2021): 5713. http://dx.doi.org/10.3390/en14185713.

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In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

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Maroto Estrada, Pedro, Daniela de Lima, Peter H. Bauer, Marco Mammetti, and Joan Carles Bruno. "Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach." Applied Energy 329 (January 2023): 120231. http://dx.doi.org/10.1016/j.apenergy.2022.120231.

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Yue, Meiling, Samir Jemei, Rafael Gouriveau, and Noureddine Zerhouni. "Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: Degradation models and strategies." International Journal of Hydrogen Energy 44, no. 13 (March 2019): 6844–61. http://dx.doi.org/10.1016/j.ijhydene.2019.01.190.

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Journal articles on the topic 'Energy management strategies for hybrid electric vehicles'

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