Research on energy management strategies for fuel cell commercial vehicles based on model predict control strategy

Model predictive control (MPC) based energy management strategies (EMS) are promising to achieve high-efficiency power conversion for different hybrid electric vehicles. In this work, we investigate the impact of velocity prediction on the performance of EMS. For this, MPC controllers are designed respectively for fuel cell hybrid electric vehicles (FCHEV) using four prediction settings: Prescient MPC, Frozen time MPC, exponentially decreasing MPC, and MPC with Markov chain model. The comparison of the results using different driving cycles is performed to study the effects of prediction horizon and prediction accuracy on the performance of EMS, in terms of hydrogen consumption and battery charge sustainability. Simulation results show that the performance of MPC-based EMS is highly dependent on the prediction accuracy and the control horizon length. With proper velocity prediction methods and horizon length configurations, low hydrogen consumption and sustainable battery charge can be achieved. Moreover, the necessity of co-designing the prediction model and the horizon length by specifying the driving condition is highlighted.

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.

A two-term energy management strategy of hybrid electric vehicles for power distribution and gear selection with intelligent state-of-charge reference

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.

Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles

Model predictive sampled-data control of constrained, linear, time-invariant, continuous-time plants is considered. The time-discretization of the prediction horizon may be non-linear, in order to reduce the computational complexity of online MPC methods by lowering the number of optimization variables for a given prediction horizon length. The main contribution of this paper is to propose two closed-loop performance measures in order to evaluate the salient performance properties of non-linearly time-discretized prediction horizons. A numerical motivating example comparing two prediction horizon time-discretizations with an order of magnitude difference in the number of optimization variables is discussed, and subsequently the results of a sensitivity analysis of the two proposed performance measures with respect to the prediction horizon time-discretization are presented. The use of non-linearly time-discretized prediction horizons is also shown to be relevant for complexity reduction in offline MPC strategies.

Model predictive sampled-data control of constrained, linear, time-invariant, continuous-time plants is considered. The time-discretization of the prediction horizon may be non-linear, in order to reduce the computational complexity of online MPC methods by lowering the number of optimization variables for a given prediction horizon length. The main contribution of this paper is to propose two closed-loop performance measures in order to evaluate the salient performance properties of non-linearly time-discretized prediction horizons. A numerical motivating example comparing two prediction horizon time-discretizations with an order of magnitude difference in the number of optimization variables is discussed, and subsequently the results of a sensitivity analysis of the two proposed performance measures with respect to the prediction horizon time-discretization are presented. The use of non-linearly time-discretized prediction horizons is also shown to be relevant for complexity reduction in offline MPC strategies.

Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle

In this brief, a model predictive control (MPC) is developed for the first time to solve the optimal energy management problem in tracked bulldozers equipped with advanced series hybrid powertrains. Hybrid bulldozers use two distinct power sources for propulsion, and their complex powertrain architecture requires the coordination of all subsystems to achieve target performances in terms of fuel economy, exhaust emissions. This 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 (QP), to find a feasible suboptimal solution. The MPC solution is then compared with the dynamic programming algorithm, which requires the entire driving profile to be known priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon) is also investigated. The results from comparing the MPC solution and the rule-based control strategy indicate that there is an approximately 5.2%improvement in fuel economy.

Model predictive sampled-data control of linear continuous-time plants is considered. The time-discretization of the prediction horizon may be non-linear, in order to reduce the number of optimization variables for a given prediction horizon length. This is done for the purpose of allowing faster implementation. While the method is aimed at constrained systems, this paper focuses on the achievable performance of such control strategies for unconstrained systems. A general solution to the finite-horizon optimal control problem is derived for a prediction horizon of arbitrary time-discretization. The model predictive control strategy is consequently derived, and the optimal control input shown to be given by a time-invariant state feedback expression. Three non-linear prediction horizon time-discretization schemes are proposed, and their relative merits discussed. The benefit of employing the presented control strategy is demonstrated by a satellite attitude control case study. The same case study is further used to highlight limitations of and performance differences between the three proposed prediction horizon time-discretization schemes.

This study introduces a multi-level Energy Management Strategy (EMS) designed for Hybrid Electric Vehicles (HEV) that considers multiple uncertainties. In the upper-level strategy, an extended Long Short-Term Memory (xLSTM) neural network algorithm is utilized for short-term vehicle speed prediction. Simultaneously, within the prediction horizon, the optimal control sequence for the engine is then determined using the Dynamic Programming (DP) algorithm, which also generates a State of Charge (SOC) trajectory. In the lower-level strategy, the SOC trajectory from the upper-level strategy serves as a reference, and a tube-based Model Predictive Control (tube-MPC) approach is utilized to address the reference trajectory tracking problem under multiple uncertainties. Simulation results demonstrate that the xLSTM-based speed prediction model improves accuracy and reduces compute time compared to the Long Short-Term Memory (LSTM) and transformer speed prediction model; the proposed strategy improves fuel economy by 11.65% over the rule-based strategy and improves fuel economy by 5.25% over the latest Model Predictive Control (MPC) strategy, with a fuel consumption of 4.641 L/100 km, achieving 92.67% fuel economy of the DP strategy. Furthermore, it maintains over 90% of the DP strategy’s fuel efficiency across various driving conditions, confirming its robustness and adaptability.

In this paper, a fast constrained model predictive control algorithm was designed for the active suspension of a half-car model to increase the controller bandwidth so that high frequency displacement disturbance coming from the road can be rejected. To this end, a quasi-LTI model of a semi-active suspension model was controlled by a model predictive controller with orthogonal Laguerre polynomials. With the use of Laguerre polynomials, it has been shown that the optimization parameter set could be made minimal, and thereby it has been shown that on-line optimization takes less time. With numerical simulations, it has been shown that the time complexity of a model predictive control having Laguerre polynomials is linear in the length of prediction horizon, whereas time complexity of a regular model predictive control is quadratic in the length of prediction horizon. Since it has been shown that time complexity of the constrained model predictive controller with orthogonal Laguerre polynomial is reduced, it is possible to extend the prediction horizon to large values. Further, constraints on the input signal and the state vector were also discussed within this context.

Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework

The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin’s minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin’s minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.

The energy management strategy plays an important role in the control of a hybrid electric vehicle (HEV), which can obviously improve the energy efficiency without significant loss of driving performance. This paper focuses on the energy management of a parallel hybrid electric vehicle with a continuously variable transmission (CVT) using model predictive control (MPC), including torque split and gear ratio optimization. Gauss pseudo-spectral (GPM) method is employed to discretize the optimal problem and reduce computational time in the framework of MPC. Simulation results show that the proposed energy management strategy can effectively improve energy efficiency with less computational load.