scholarly journals Energy Management Strategy Based on a Novel Speed Prediction Method

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8273
Author(s):  
Jiaming Xing ◽  
Liang Chu ◽  
Zhuoran Hou ◽  
Wen Sun ◽  
Yuanjian Zhang
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Management Strategy ◽  
Prediction Method ◽  
Parallel Structure ◽  
Vehicle Speed ◽  
Energy Management Strategy ◽  
Highly Nonlinear ◽  
The Future ◽  
Speed Prediction

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy.

scholarly journals Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7767
Author(s):  
Jiaming Xing ◽  
Liang Chu ◽  
Chong Guo ◽  
Shilin Pu ◽  
Zhuoran Hou
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Network Architecture ◽  
Prediction Method ◽  
Support Vector ◽  
Vehicle Speed ◽  
Energy Management Strategy ◽  
Equivalent Fuel ◽  
Speed Prediction ◽  
Equivalent Fuel Consumption

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.

Receding Horizon Real-Time Energy Management Strategy for Hybrid Vehicles

2013 ◽  
Author(s):  
Bram de Jager ◽  
Thijs van Keulen
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Vehicles ◽  
State Control ◽  
Energy Management Strategy ◽  
Receding Horizon ◽  
Fuel Use ◽  
Commodity Hardware ◽  
The Future

Indirect optimal control and dynamic programming are combined in a receding horizon controller to obtain an energy management strategy for hybrid vehicles. This combination permits the use of inaccurate predictions of the future, instead of requiring exact knowledge, and allows the use of mixed state-control constraints, like voltage constraints for batteries. The controller can run in real-time on commodity hardware and, using a prediction of the future based on geographic information only, obtains a fuel use within 0.2% of the optimal fuel use computed with the exact speed and power trajectory of the vehicle known in advance. All this for a planned distance of more than 500 [km].

Energy management strategy for HEV based on KFCM and neural network

2018 ◽  
Vol 31 (10) ◽  
pp. e4838 ◽  
Author(s):  
Yeqin Wang ◽  
Zhen Wu ◽  
Aoyun Xia ◽  
Chang Guo ◽  
Yuyan Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Management Strategy ◽  
Energy Management Strategy

scholarly journals A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2212 ◽  
Author(s):  
Qiwei Xu ◽  
Yunqi Mao ◽  
Meng Zhao ◽  
Shumei Cui
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Energy Management ◽  
Dynamic Optimization ◽  
Power Loss ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Permanent Magnet Motor ◽  
Engine Power

A dynamic optimization energy management strategy called Hybrid Electric Vehicle Based on Compound Structured Permanent-Magnet Motor (CSPM-HEV) is investigated in this paper. CSPM-HEV has obvious advantages in power density, heat dissipation efficiency, torque performance and energy transmission efficiency. This paper describes the topology and working principle of the CSPM-HEV, and analyzes its operating mode and corresponding energy flow laws. On this basis, the relationship about the power loss of the vehicle, the CSPM transmission ratio iCSPM and the CSPM-HEV power distribution coefficient f1 were derived. According to the optimal combination of (iCSPM, f1), the engine power and speed which minimize the power loss of the vehicle, were calculated, thus realizing the instantaneous optimal control of the vehicle. In addition, in order to improve the instantaneously optimized control processing speed, a neural network controller was established. The drive axle demand power, speed and battery State of Charge (SOC), were taken as input variables. Then, the engine power and speed were taken as output variables. The simulation results show that the average speed of the instantaneous optimization strategy after BP neural network optimization is increased by 98.1%, the control effect is significant, and it has high application value.

scholarly journals A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 696
Author(s):  
Qi Zhang ◽  
Xiaoling Fu
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Membership Function ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Fuzzy Energy

Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.

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