Energy Management Strategy for Dual-Motor Two-Speed Transmission Electric Vehicles Based on Dynamic Programming Algorithm Optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 19-31
Author(s):  
Bin Wu ◽  
Suxu Zhang
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Algorithm Optimization ◽  
Energy Management Strategy

Model Predictive Control as an Energy Management Strategy for Hybrid Electric Vehicles

2009 ◽  
Author(s):  
Balaji Sampathnarayanan ◽  
Lorenzo Serrao ◽  
Simona Onori ◽  
Giorgio Rizzoni ◽  
Steve Yurkovich
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
A Priori ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Energy Management Strategy ◽  
Hybrid Electric

The energy management strategy in a hybrid electric vehicle is viewed as an optimal control problem and is solved using Model Predictve Control (MPC). The method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on quadratic programming, to find a feasible suboptimal solution. The significance of the results lies in obtaining a real-time implementable control law. The MPC algorithm is applied using a quasi-static simulator developed in the MATLAB environment. The MPC solution is compared with the dynamic programming solution (offline optimization). The dynamic programming algorithm, which requires the entire driving cycle to be known a-priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon, type of prediction) is also investigated.

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

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.

An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles

Energy ◽  
2021 ◽  
pp. 122120
Author(s):  
Chao Yang ◽  
Kaijia Liu ◽  
Xiaohong Jiao ◽  
Weida Wang ◽  
Ruihu Chen ◽  
...  
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Algorithm Optimization ◽  
Energy Management Strategy ◽  
Hybrid Electric

scholarly journals Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles

Energies ◽  
2015 ◽  
Vol 8 (4) ◽  
pp. 3225-3244 ◽  
Author(s):  
Ximing Wang ◽  
Hongwen He ◽  
Fengchun Sun ◽  
Jieli Zhang
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Application Study ◽  
Hybrid Electric

scholarly journals Sizing of the Propulsion System for a Heavy-Duty Fuel Cell Commercial Vehicle

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Weiguang Zheng ◽  
Weiwei Xin ◽  
Enyong Xu ◽  
Shuilong He ◽  
Jirong Qin ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Management Strategy ◽  
Commercial Vehicle ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Heavy Duty ◽  
Energy Management Strategy ◽  
Price Performance

This paper presents a methodology for the sizing of a heavy-duty fuel cell commercial vehicle. The parameters scanning model and the long-term stochastic drive cycle are adopted for this proposed sizing framework. The dynamic programming algorithm is employed as the energy management strategy to assess the performance of sizing. The efficacy of this framework is evaluated, and a detailed analysis for the hydrogen consumption is given in the results. In addition, a prediction analysis based on the price performance of the next decade is also given in this work.

Modeling of engine thermal dynamics and its application in energy management of HEVs considering engine warming-up

2021 ◽  
pp. 146808742110445
Author(s):  
Hongqing Chu ◽  
Haoyun Shi ◽  
Yuyao Jiang ◽  
Tielong Shen
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
Degree Of Approximation ◽  
Programming Algorithm ◽  
Energy Management Strategy ◽  
Thermal Dynamics ◽  
Behavior Simulation

The process of engine warming-up leads to additional fuel consumption. Energy management strategy considering engine warming-up is expected to further improve the energy economy of hybrid electric vehicles. This study provides a simple yet practical model for engine thermal dynamics. Then, the optimization problem of energy management considering engine warming-up is formulated on the basis of the control-oriented engine thermal dynamics. Thereafter, the optimal solution is derived by using the dynamic programming algorithm. Finally, the proposed engine thermal dynamics and energy management strategy are evaluated through simulation and experiments. Results show that the established engine thermal model effectively captures the main thermal behavior, simulation results reveal a high degree of approximation to experimental results for the engine temperature and fuel consumption, and the energy management strategy with engine temperature can further improve the energy efficiency.

scholarly journals Energy management strategy for hybrid electric vehicles based on adaptive equivalent consumption minimization strategy and mode switching with variable thresholds

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987499 ◽  
Author(s):  
Yang Li ◽  
Xiaohong Jiao
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Mode Switching ◽  
Energy Management Strategy ◽  
Engine Torque ◽  
Equivalence Factor ◽  
Equivalent Consumption Minimization Strategy

To improve the real-time capability, adaptivity, and efficiency of the energy management strategy in the actual driving cycle, a real-time energy management strategy is investigated for commute hybrid electric vehicles, which integrates mode switching with variable threshold and adaptive equivalent consumption minimization strategy. The proposed strategy includes offline and online parts. In the offline part based on the historical traffic data on the route of the commute vehicle, particle swarm optimization is applied to optimize all the thresholds of mode switching, equivalence factor of the equivalent consumption minimization strategy, and the engine torque and speed at the engine-alone propelling mode so as to establish their mappings on the battery state of charge and power demand. In the online part, the established mappings are involved in the energy management supervisor to generate timely appropriate mode switching signals, and an adaptive equivalence factor for instantaneous optimization equivalent consumption minimization strategy and the optimal engine torque and speed at engine-alone propelling mode. To fully demonstrate the effectiveness of the proposed strategy, the simulation results and comparison with some other strategies and the benchmark dynamic programming strategy are presented by implementing the strategies on the GT-SUITE test platform. The comparison result indicates that the control effect of the proposed energy management strategy is much nearer to that of the benchmark dynamic programming than those of other strategies (the rule-based control, the conventional equivalent consumption minimization strategy, the adaptive equivalent consumption minimization strategy, the rule-based-equivalent consumption minimization strategy, and the stochastic dynamic programming strategy) with the respective improvement in fuel efficiency by 25.9%, 13.25%, 4.6%, 1.32%, and 1.13%.

scholarly journals Online Energy Management of Plug-In Hybrid Electric Vehicles for Prolongation of All-Electric Range Based on Dynamic Programming

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zeyu Chen ◽  
Weiguo Liu ◽  
Ying Yang ◽  
Weiqiang Chen
Keyword(s):  
Dynamic Programming ◽  
Energy Saving ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Energy Management Strategy ◽  
Driving Range ◽  
Hybrid Electric

The employed energy management strategy plays an important role in energy saving performance and exhausted emission reduction of plug-in hybrid electric vehicles (HEVs). An application of dynamic programming for optimization of power allocation is implemented in this paper with certain driving cycle and a limited driving range. Considering the DP algorithm can barely be used in real-time control because of its huge computational task and the dependence ona prioridriving cycle, several online useful control rules are established based on the offline optimization results of DP. With the above efforts, an online energy management strategy is proposed finally. The presented energy management strategy concerns the prolongation of all-electric driving range as well as the energy saving performance. A simulation study is deployed to evaluate the control performance of the proposed energy management approach. All-electric range of the plug-in HEV can be prolonged by up to 2.86% for a certain driving condition. The energy saving performance is relative to the driving distance. The presented energy management strategy brings a little higher energy cost when driving distance is short, but for a long driving distance, it can reduce the energy consumption by up to 5.77% compared to the traditional CD-CS strategy.

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