To realize the optimal energy allocation between the engine-generator and battery of a hybrid tracked vehicle (HTV), a reinforcement learning-based real-time energy-management strategy was proposed. A systematic control-oriented model for the HTV was built and validated through the test bench, including the battery pack, the …
Participated in the construction of Zhangbei energy storage project – the largest wind and solar energy storage and transmission project in the world at the time. 1999 The founding team established ATL, which is the world''s leading company in the field of lithium-ion batteries for consumer electronics (CE).
Intelligent vehicle can make people get rid of the "driver–vehicle–road" closed-loop traffic control system, and form "vehicle–road" closed-loop traffic control system to eliminate the …
Taking a hybrid energy storage system (HESS) composed of a battery and an ultracapacitor as the study object, this paper studies the energy management …
This paper mainly introduces a design scheme of intelligent logistics cars based on the STM32F103ZET6 single-chip microcomputer and OpenMV programmable camera. Its main process is as follows ...
At EVESCO, we help businesses deploy scalable, fast electric vehicle charging solutions that free them from the constraints of the electric grid through innovative energy storage. The EVESCO mission is to accelerate the mass adoption of electric vehicles by delivering sustainable fast-charging solutions, which can be deployed anywhere.
The full-lifespan management concept provides a new pathway to seeking solutions from macro-application scenarios to micro-mechanism levels. This paper presents a cyber hierarchy multiscale optimal control method for multiple intelligent hybrid vehicles to fully release the potentials of vehicle components while guaranteeing driving safety …
The energy storage system incorporates a fail-safe redundancy feature, enabling the vehicle to function at reduced capacity and return to the depot for maintenance should one branch experience a malfunction. In a typical three-unit ART tram, the energy storage system boasts a 200 kWh capacity as standard.
In this paper, a bi-level control framework is proposed to improve the energy efficiency for a hybrid tracked vehicle. The higher-level discusses how to accurately predict power demand based on the Markov Chain. Specially, fuzzy encoding predictor is used for power demand prediction, and a real-time recursive algorithm is applied to fuse …
Optimal fuzzy logic based energy management strategy of battery/supercapacitor hybrid energy storage system for electric Vehicles.2016 12th world congress on intelligent control and automation (WCICA) june 12-15
This strategy effectively deals with the constraints of road adhesion conditions, driver intention, slip rate, and battery state of charge, and significantly reduces track slip and motor braking ...
As hybrid tracked vehicles use dual-energy sources, the engine-generator sets and power batteries. We applied the dynamic programming theory to optimize the distribution between the dual-energy sources, so we can make the fuel consumption minimize. Through the analysis of the optimization results, near-optimal rules are extracted.
To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet transform-fuzzy logic control energy management strategy based on driving pattern recognition (DPR) is proposed in view of the fact that driving cycle greatly affects the …
Lead-acid (LA) batteries. LA batteries are the most popular and oldest electrochemical energy storage device (invented in 1859). It is made up of two electrodes (a metallic sponge lead anode and a lead dioxide as a cathode, as shown in Fig. 34) immersed in an electrolyte made up of 37% sulphuric acid and 63% water.
The dynamic programming (DP) theory is applied in designing energy management control strategy of Hybrid Tracked Vehicle. As hybrid tracked vehicles use dual-energy sources, the engine-generator sets and power batteries. …
Renewable resources, including wind and solar energy, are investigated for their potential in powering these charging stations, with a simultaneous exploration of energy storage systems to ...
An online updating energy management strategy (EMS) based on deep reinforcement learning (DRL) with accelerated training is proposed to further reduce fuel consumption and improve the adaptability of the algorithm. The online frame continuously updates neural network parameters every predetermined time. First, the mathematical …
The high frequency magnetic field area in the radio energy transmission system (WPT) is an important medium for electric energy transmission. As an open structure, the high frequency magnetic field often inevitably falls into metal foreign matters, leading to a decline in the transmission efficiency of the system, and the heating of the metal itself will also cause …
DOI: 10.1016/J.APENERGY.2019.113388 Corpus ID: 191175936 Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning @article{Du2019IntelligentEM, title={Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning}, author={Guodong Du and Yuan …
This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) …
1. Introduction. The fact that current transportation highly depends on nonrenewable fuels raises more and more concern over sustainability development of the global environment [1].The air pollution caused by traditional vehicles and the depletion of oil resources has greatly accelerated the development of electric vehicles [2].Plug-in …
This paper proposes a data-driven energy management strategy (EMS) for a series hybrid electric tracked vehicle (SHETV). Firstly, according to the configuration characteristics of the SHETV powertrain, a simulation model for the development of EMSs is built. Secondly, combined with the design requirements, a global optimal EMS based on …
Ref. [11] employed SDP to address the energy management for a series hybrid tracked vehicle based on the Markov chain driver model. Xi etc. [12] co-optimizes the use of energy storage for multiple applications with SDP while accounting for …
Energy management strategy (EMS) is an important link during the HEV/PHEV design procedure, which can govern the energy flow between the fuel tank …
An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions …
The addition of battery and SC reduces the size of the FC and improves the overall performance and service life of the power system [13], [14]. Therefore, FCHEV is regarded as the solution for pure electric vehicles. However, various energy storage devices added to HEV increase the degree of freedom of system control [15], [16].
Figure 2. Principle block diagram of gun base integration. 2.2. Charging Gun Connected to Mobile Energy Storage Vehicle As shown in Figure 3, the charging pile can be directly connected to the ...
To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet …
This paper proposes an online updating energy management strategy to improve the fuel economy of hybrid electric tracked vehicles. As the basis of the research, the overall model for the hybrid electric tracked vehicle is built in detail and validated through the field experiment. ... "Investigation of novel intelligent energy management ...
For instance, energy management systems in the context of electric vehicles (Liu et al., 2020), IoT''s (Golpîra and Bahramara, 2020), intelligent transportation (Yang et al., 2020), photo-voltaic systems (Langer and Volling, 2020), and virtual power plants (Sheidaei and Ahmarinejad, 2020) are also emerging topic in the intelligent …
As the driving cycle of unmanned tracked vehicles is uncertain, conventional energy management strategies must deal with new challenges. To improve the prediction accuracy, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed for processing both planned and historical velocity series.
This paper proposes a data-driven energy management strategy (EMS) for a series hybrid electric tracked vehicle (SHETV). Firstly, according to the configuration characteristics of the SHETV powertrain, a simulation …
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To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet transform-fuzzy logic control...
However, developing an energy management strategy (EMS) is a time-consuming and labor-intensive task, which is challenging to generalize across different driving tasks. To solve this problem and shorten the development cycle of EMSs, this article proposes a novel transferable energy management framework for a series hybrid electric tracked vehicle …
These intelligent systems should predict energy generation from renewable sources and energy demand to generate the deficit energy demand near the demand location to minimize losses. Another implementation of AI is in energy storage. ML is very capable in data classification and regression, and other related tasks.