A Rule-Based Strategy for a Series/Parallel Hybrid Electric Vehicle: An Approach Based on Dynamic Programming

2010 ◽  
Author(s):  
Domenico Bianchi ◽  
Luciano Rolando ◽  
Lorenzo Serrao ◽  
Simona Onori ◽  
Giorgio Rizzoni ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
A Priori ◽  
Optimal Solution ◽  
Management Problem ◽  
Rule Based ◽  
Hybrid Electric ◽  
Programming Solution ◽  
Powertrain Configuration

Dynamic programming (DP) provides the optimal global solution to the energy management problem for hybrid electric vehicles (HEVs), but needs complete a-priori knowledge of the driving cycle and has high computational requirements. This article presents a possible methodology to extract rules from the dynamic programming solution to design an implementable rule-based strategy. The case study considered is a series/parallel HEV, in which a clutch allows to switch from one configuration to another. The strategy works according to a two layer policy: the supervisory controller, which decides the powertrain configuration (either series or parallel), and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described. Then, the performance of the resulting rule-based strategy is studied and compared with the solution given by the dynamic programming, which functions as a benchmark.

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.

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

2021 ◽  
pp. 095440702110297
Author(s):  
Hui Liu ◽  
Rui Liu ◽  
Riming Xu ◽  
Lijin Han ◽  
Shumin Ruan
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Hierarchical Control ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

Optimization of Fuel Economy of Hybrid Electric Vehicles Using Set Based Dynamic Programming

2014 ◽  
Author(s):  
Nikhil Ramaswamy ◽  
Nader Sadegh
Keyword(s):  
Dynamic Programming ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Optimal Solution ◽  
The State ◽  
State Variables ◽  
Discrete Time System ◽  
Real Time Control ◽  
Time Step ◽  
Hybrid Electric

Dynamic Programming (DP) technique is an effective algorithm to find the global optimum. However when applying DP for finite state problems, if the state variables are discretized, it increases the cumulative errors and leads to suboptimal results. In this paper we develop and present a new DP algorithm that overcomes the above problem by eliminating the need to discretize the state space by the use of sets. We show that the proposed DP leads to a globally optimal solution for a discrete time system by minimizing a cost function at each time step. To show the efficacy of the proposed DP, we apply it to optimize the fuel economy of the series and parallel Hybrid Electric Vehicle (HEV) architectures and the case study of Chevrolet Volt 2012 and the Honda Civic 2012 for the series and parallel HEV’s respectively are considered. Simulations are performed over predefined drive cycles and the results of the proposed DP are compared to previous DP algorithm (DPdis). The proposed DP showed an average improvement of 2.45% and 21.29% over the DPdis algorithm for the series and the parallel HEV case respectively over the drive cycles considered. We also propose a real time control strategy (RTCS) for online implementation based on the concept of Preview Control. The RTCS proposed is applied for the series and parallel HEV’s over the drive cycles and the results obtained are discussed.

Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

2010 ◽  
Author(s):  
Simona Onori ◽  
Lorenzo Serrao ◽  
Giorgio Rizzoni
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Optimal Solution ◽  
State Of Charge ◽  
Management Problem ◽  
Equivalence Factor ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy ◽  
Adaptation Law

This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.

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