A Predictive Energy Management Strategy for Multi-Energy Source Vehicles Based on Full-Factor Trip Information

To achieve the real-time application of a dynamic programming (DP) control strategy, we propose a predictive energy management strategy (PEMS) based on full-factor trip information, including vehicle speed, slip ratio and slope. Firstly, the prediction model of the full-factor trip information is proposed, which provides an information basis for global optimization energy management. To improve the prediction’s accuracy, the vehicle speed is predicted based on the state transition probability matrix generated in the same driving scene. The characteristic parameters are extracted by a feature selection method taken as the basis for the driving condition’s identification. Similar to speed prediction, regarding the uncertain route at an intersection, the slope prediction is modelled as a Markov model. On the basis of the predicted speed and the identified maximum adhesion coefficient, the slip ratio is predicted based on a neural network. Then, a predictive energy management strategy is developed based on the predictive full-factor trip information. According to the statistical rules of DP results under multiple standard driving cycles, the reference SOC trajectory is generated to ensure global sub-optimality, which determines the feasible state domain at each prediction horizon. Simulations are performed under different types of driving conditions (Urban Dynamometer Driving Schedule, UDDS and World Light Vehicle Test Cycle, WLTC) to verify the effectiveness of the proposed strategy.

Download Full-text

Energy Management of Fuel Cell Vehicles Based on Model Prediction Control Using Radial Basis Functions

Journal of Sensors ◽

10.1155/2021/9985063 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Weiwei Xin ◽

Weiguang Zheng ◽

Jirong Qin ◽

Shangjun Wei ◽

Chunyu Ji

Keyword(s):

Fuel Cell ◽

Real Time ◽

Energy Management ◽

Model Prediction ◽

Management Strategy ◽

Energy Management Strategy ◽

Time Performance ◽

Prediction Horizon ◽

Model Prediction Control ◽

Prediction Control

Energy management strategies can improve fuel cell hybrid electric vehicles’ dynamic and fuel economy, and the strategies based on model prediction control show great advantages in optimizing the power split effect and in real time. In this paper, the influence of prediction horizon on prediction error, fuel consumption, and real time was studied in detail. The framework of energy management strategy was proposed in terms of the model prediction control theory. The radial basis function neural network was presented as the predictor to obtain the short-term velocity in the future. A dynamic programming algorithm was applied to obtain optimized control laws in the prediction horizon. Considering the onboard controller’s real-time performance, we established a simple fuel cell vehicle mathematical model for simulation. Different prediction horizons were adopted on UDDS and HWFET to test the influence on prediction and energy management strategy. Simulation results showed the strategy performed well in fuel economy and real-time performance, and the prediction horizon of around 20 s was appropriate for this strategy.

Download Full-text

Design of Optimized Energy Management Strategy for All-Wheel-Drive Electric Vehicles

Applied Sciences ◽

10.3390/app11178218 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8218

Author(s):

Haishi Dou ◽

Youtong Zhang ◽

Likang Fan

Keyword(s):

Dynamic Programming ◽

Energy Management ◽

Management Strategy ◽

Dynamic Programming Algorithm ◽

Poor Performance ◽

Driving Cycle ◽

Programming Algorithm ◽

Vehicle Speed ◽

Energy Management Strategy ◽

Wheel Drive

The performance of the all-wheel-drive electric vehicle is inseparable from the energy management strategy (EMS). An outstanding EMS could extend the cycling mileage, coordinating the power output of the battery and exerts the advantage of the motor comprehensively. However, the current EMS has poor performance in real-time, and this paper proposes the dynamic programming coordination strategy (DPCS) to solve the problem. Firstly, the EMS based on a rule-based control strategy (RBCS) is applied in a different driving cycle. Secondly, the dynamic programming algorithm (DP) is proposed in the process. The DPCS cooperated the advantage of RBCS and DP, extracting the boundary parameters along with the demand power and vehicle speed. Finally, the number of motors joined in the driving condition is elucidated and the method obtains the optimal torque split ratio through a partly-known driving cycle. By incorporating the thought of a basis of rules, the DPCS determines the torque of each motor that confirm the motor working in an efficient range that incorporates the mind of dynamic programming. The method is validated through the simulation. The results show that the strategy can significantly improve the mileage of the driving cycle, with comprehensive performance in energy distribution and utilization.

Download Full-text

Energy Management Strategy Based on a Novel Speed Prediction Method

Sensors ◽

10.3390/s21248273 ◽

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.

Download Full-text