Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition

Hu, Jie; Liu, Di; Du, Changqing; Yan, Fuwu; Lv, Chen

doi:10.1016/j.energy.2020.117298

Cited by 129 publications

(54 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another representative rule‐based control strategy is the frequency‐based power decomposition strategy employed in References 40,53‐60 for battery‐UC HESS power split problem. In this method, the power demand of the HESS is handled using high and low frequency signals.…”

Section: Ems and Its Review On Em Strategies Applied For Hess Powered Evmentioning

confidence: 99%

“…Furthermore, this EM technique extends the battery life time and reduce stress on the battery. In addition to WT‐fuzzy at supervisory level an adaptive WT‐fuzzy logic‐based EM has been proposed in Reference 58 by integrating driving pattern recognition (DPR) for UC in EV applications. Cluster analysis is utilized by DPR and adaptive wavelet transform extracts and allocate the respective frequency components to UC and battery during power demand conditions.…”

Section: Performance Evaluation Of Em Strategies Applied For Em In Hementioning

confidence: 99%

See 1 more Smart Citation

Energy management techniques and topologies suitable for hybrid energy storage system powered electric vehicles: An overview

Sankarkumar

Rajasekar

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Energy management system (EMS) in an electric vehicle (EV) is the system involved for smooth energy transfer from power drive to the wheels of a vehicle. During acceleration and deceleration periods, batteries in EV undergo high peak power consumption. Therefore, battery lifecycle degrades and subsequently reduces the drive range of an EV. Thus, hybridization of different energy resources becomes essential and seen as one of the alternative solutions for afore said issues. Further, hybridization along with efficient EM strategies helps to: (i) optimally utilize the energy storage systems during discharging and charging, (ii) improve the performance which in turn improves efficiency, and (iii) extend the drive range (iv) reduce the battery size. Though, many articles have been reported so far in literature for hybrid energy storage system (HESS) related to EM techniques; comprehensive review on: the configurations related to HESS, various EM strategies used in EV, performance evaluation of EM strategies for HESS configurations is not yet published. Therefore, this paper intends to provide a comparative assessment on different types of HESS topologies, types of EM techniques. The performance indices based on battery peak current reduction; amount of power stored back during regenerative braking has been compared and discussed. Further, a comparative analysis has been made on the system design and voltage variations. Hence, this review may be a nonstop solution for researchers and engineers working in the field of EVs.

show abstract

Section: Ems and Its Review On Em Strategies Applied For Hess Powered Evmentioning

confidence: 99%

Section: Performance Evaluation Of Em Strategies Applied For Em In Hementioning

confidence: 99%

Energy management techniques and topologies suitable for hybrid energy storage system powered electric vehicles: An overview

Sankarkumar

Rajasekar

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…The system was evaluated over four drive cycles in an EV. A new control strategy was proposed in [27] based on driving pattern recognition. The driving cycle was classified into different patterns based on the historical driving data.…”

Section: Figure 1 Common Hess Architectures [11]mentioning

confidence: 99%

Optimal Control Strategy to Maximize the Performance of Hybrid Energy Storage System for Electric Vehicle Considering Topography Information

et al. 2020

View full text Add to dashboard Cite

This research designed an energy management system involving a battery-supercapacitor Hybrid Energy Storage System (HESS) for electric vehicles (EV). The objective is to improve the performance of the HESS by combining battery and supercapacitor features, accounting for topographical information to guarantee continuous hybridization during the drive cycle. Contour Positioning System (CPS) was used to determine the slope of the rode travelled by the vehicle. Two adaptive algorithms were designed for a rule-based controller to control the energy shared between the battery and the supercapacitor; an optimal adaptive controller and fuzzy adaptive controller. The HESS model, electric vehicle and controllers were tested using MATLAB/Simulink with three real drive cycles, namely, uphill, downhill and city tour, in three different speeds 50Km/h, 60Km/h and 70 Km/h. The results proved the controllers managed to extend battery life-cycle by reducing the stress on the battery for the drive cycles. The results were compared in terms of energy consumption for the optimal adaptive rule-based controller and fuzzy adaptive rule-based controller. The optimal adaptive rule-based controller guaranteed the HESS was able to operate continuously and extend the number of drive cycles in a wide range of speeds and road slopes. INDEX TERMS Electric vehicles; Energy storage; Energy management; Navigation.

show abstract

“…The driving pattern recognition (DPR) uses cluster analysis to classify driving cycles into different patterns according to the features extracted from the historical driving data sampling window and utilizes pattern recognition to identify real-time driving patterns. [29]. Yan et al [30] proposed a k-MPSO clustering algorithm for the construction of typical driving cycles.…”

Section: Introductionmentioning

confidence: 99%

Research on a Novel Vehicle Rollover Risk Warning Algorithm Based on Support Vector Machine Model

Zhu

Yin

et al. 2020

IEEE Access

View full text Add to dashboard Cite

A novel vehicle rollover warning algorithm based on support vector machine (SVM) empirical model is proposed to improve the real-time of un-tripped rollover warning algorithm and accuracy of dynamic rollover warning. Considering the nonlinear characteristic of driver-vehicle-road interaction and the uncertainty of modeling, the traditional deterministic methods cannot meet the requirements of accurate vehicle rollover warning modeling. The probability method considering issues of uncertainty is applied to design vehicle dynamic rollover warning algorithm. The SVM empirical model considers the uncertainties of the driver-vehicle-road system and the real variability of the parameters, provides an explicit function of vehicle rollover safety limit and its gradient, and utilizes the hypersurface visualization boundary to define the rollover safety area and the unsafe area. Targeting on sport utility vehicle under the condition of high-speed emergency obstacle avoidance, simulations are carried out to verify the proposed vehicle rollover warning algorithm based on SVM empirical model and of the simulation results show that the proposed algorithm has accurate warning and good real-time performance. It can effectively improve the warning accuracy of vehicle dynamic rollover, reduce the interference of nonlinear and uncertainty, and significantly improve the active safety performance with vehicle rollover prevention. INDEX TERMS rollover warning algorithm, support vector machine, empirical model TIANJUN ZHU was born in Xingtai City, Hebei province, China in 1977. He received the B.S. degree in vehicle engineering from Hebei Agricultural University, Baoding, in 2000. and M.S. degree in mechanical engineering from

show abstract

Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition

Cited by 129 publications

References 35 publications

Energy management techniques and topologies suitable for hybrid energy storage system powered electric vehicles: An overview

Energy management techniques and topologies suitable for hybrid energy storage system powered electric vehicles: An overview

Optimal Control Strategy to Maximize the Performance of Hybrid Energy Storage System for Electric Vehicle Considering Topography Information

Research on a Novel Vehicle Rollover Risk Warning Algorithm Based on Support Vector Machine Model

Contact Info

Product

Resources

About