Research on the energy management strategy of extended range electric vehicles based on a hybrid energy storage system

Yang, Yifan; Xu, Yonghong; Zhang, Honggang; Yang, Fubin; Ren, Jing; Wang, Xu; Jin, Peng; Huang, Denggao

doi:10.1016/j.egyr.2022.05.013

Cited by 25 publications

(3 citation statements)

References 53 publications

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“…A complex energy regulator may assist with range anxiety in EVs. A range expansion procedure for EVs is proposed in [14]. To keep the cycle working, a shrewd regulator should screen information and input boundaries.…”

Section: Electric Vehicle Range Extensionmentioning

confidence: 99%

A Hybrid Intelligent Controller for Extended-Range Electric Vehicles

Jayaraj,

Obulesu,

Govindaraj

et al. 2024

Eng. Technol. Appl. Sci. Res.

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A smart battery electric vehicle control framework is proposed in this paper. The specific controller empowers ceaseless observation and management of the battery's state with the scope of extending the vehicle's driving range under varying temperature and driving pattern conditions. The proposed method utilizes an incorporated scheme for dealing with a crossover energy stockpiling framework to expand a battery's lifespan while further ensuring its smooth activity.

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Section: Electric Vehicle Range Extensionmentioning

confidence: 99%

A Hybrid Intelligent Controller for Extended-Range Electric Vehicles

Jayaraj,

Obulesu,

Govindaraj

et al. 2024

Eng. Technol. Appl. Sci. Res.

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“…It is well known that one of the disadvantages is the cruising ability. [1] To solve this problem, the concept of the extended-range electric vehicle was introduced to scientists. The peak of the electric vehicle development period is in the 19th and early 20th centuries.…”

Section: Introductionmentioning

confidence: 99%

Research on Range Extended Electric Vehicle Based on Cruise

2023

J. Phys.: Conf. Ser.

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Recently, extended-range electric vehicles are a new solution to energy shortage and environmental pollution, which effectively increases the range of electric vehicles, extends the battery life, and reduces fuel consumption. Therefore, this paper mainly focuses on the design of the EREV powertrain, building a vehicle model by using AVL CRUISE to simulate and verify the calculated parameters of the design. Analyzing the structural characteristics of the extended-range electric vehicle and determining the vehicle’s basic parameters and performance indexes using the Aratou Free 4WD hybrid as the prototype were the first part. In this part, the drive motor on the power system, battery, and range extender were selected based on the calculated parameters. The AC asynchronous motor, ternary lithium battery, and four-stroke gasoline engine were chosen as the components of the power system in the EREV. Building the vehicle model by using the AVL Cruise and developing the calculation tasks such as cyclic driving conditions, steady-state driving performance analysis, full-load acceleration performance calculation, and hill-climbing performance analysis were the second part. After the above steps, the simulation results analysis report showed that the powertrain design meets the target and potential of electric vehicle development. This paper provides a reference for comparing the dynamic property and economy of the extended-range electric vehicle with traditional fuel vehicles.

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“…Thus, hybrid electric vehicles (HEV), which combine batteries and SCs have been studied in recent years. SCs have high power density but low energy density hence these contrasting properties of battery and SC make them good complements [2]. The SC and battery hybrid energy storage system (HESS) of HEVs is such that combines the merits of both the battery and SC.…”

Section: Introductionmentioning

confidence: 99%

Improved Deep Learning-Based Energy Management Strategy for Battery-Supercapacitor Hybrid Electric Vehicle With Adaptive Velocity Prediction

Udeogu

Lim

2022

IEEE Access

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The uncertainties and disturbances in the actual driving conditions of hybrid electric vehicles (HEVs) complicate the design of energy management strategy (EMS). To achieve better EMS performance for a battery-supercapacitor HEV, this paper proposes an improved and adaptive deep learning-based velocity prediction control EMS that can prolong the battery lifetime through efficient utilization of both the battery and supercapacitor. First, feature engineering techniques are used to extract and increase the key features from the historical driving cycle data of known driving conditions. With the extracted features, an improved long short-term memory (LSTM) velocity predictor was developed to predict future driving cycles for a realtime EMS under an unknown driving condition. Second, a real-time EMS based on the rule-based framework optimized with a neural network is proposed to optimize the power allocation online. Simulation results show that the proposed strategy smoothens battery peak power (i.e. prolongs battery life span) by approximately 26.85% on average and increases supercapacitor participation in the EMS, as evidenced by its increased energy throughput. Furthermore, compared with other EMS approaches, the proposed strategy improved the efficiency by significantly reducing total energy losses by approximately 22.25%. These results validate the reliability and robustness of the proposed strategy.

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Research on the energy management strategy of extended range electric vehicles based on a hybrid energy storage system

Cited by 25 publications

References 53 publications

A Hybrid Intelligent Controller for Extended-Range Electric Vehicles

A Hybrid Intelligent Controller for Extended-Range Electric Vehicles

Research on Range Extended Electric Vehicle Based on Cruise

Improved Deep Learning-Based Energy Management Strategy for Battery-Supercapacitor Hybrid Electric Vehicle With Adaptive Velocity Prediction

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