Complete Vehicle Energy Management Research Articles

Complete vehicle energy management using adaptive primal–dual operator splitting

Complete vehicle energy management (CVEM) aims at minimizing the energy consumption of all subsystems inside a vehicle. When the subsystems consist of energy buffers with linear dynamics and/or power converters with quadratic power losses, the CVEM becomes a large-scale and ill-conditioned optimal control problem for which well-known solution approaches (e.g. Pontryagin’s maximum principle, dynamic programming, convex and derivative-free optimization methods) are not effective. In this paper, we design an efficient solution algorithm for this problem by leveraging the framework of operator splitting methods. To facilitate its implementation, we propose a spectral method that automatically tunes the step sizes at every iteration and, in practice, accelerates convergence. Finally, we consider a case study for a parallel-hybrid vehicle and we illustrate the numerical performance of our method by showing that we can efficiently solve a CVEM problem with very large horizons.

Traffic-Aware Vehicle Energy Management Strategies via Scenario-Based Optimization

This paper explores the development of traffic-aware energy management strategies by means of scenario-based optimization. This is motivated by that fact that real driving conditions are subject to uncertainty, thereby making the real-time optimization of the energy consumption of a vehicle to be a challenging problem. In order to deal with this situation, we employ the current framework of complete vehicle energy management in a receding horizon fashion, in which we consider random constraints representing realizations of exogenous signals, i.e., the uncertain driving conditions. Additionally, we study three methods for velocity prediction in energy management strategies, i.e., a method based on (average) traffic flow information, a method based on Gaussian process regression, and a method that combines both. The proposed strategy is tested with real traffic data using a case study of the power split in a series-hybrid electric vehicle. The behavior of the battery, control inputs and fuel consumption generated with the resulting strategies are compared against the optimal solution from an offline benchmark and a situation with perfect prediction of the future, For the considered case, the use of a Gaussian process regression and the traffic speed achieves near optimal fuel consumption.

A Distributed Optimization Approach for Complete Vehicle Energy Management

In this paper, a distributed optimization approach is proposed to solve the complete vehicle energy management (CVEM) problem of a hybrid truck with several controllable auxiliaries. The first part of the approach is a dual decomposition, which allows the underlying optimal control problem to be solved for every subsystem separately. For the second part of the approach, the optimal control problem for every subsystem is further decomposed by splitting the control horizon into several smaller horizons. Two methods for splitting the control horizon are used; the first method uses alternating direction method of multipliers and divides the horizon a priori , while the second method divides the horizon iteratively by solving unconstrained optimization problems analytically. We demonstrate the approach by solving the CVEM problem of a hybrid truck with a refrigerated semitrailer, an air supply system, an alternator, a dc–dc converter, a low-voltage battery, and a climate control system. Simulation results show that the fuel consumption can be reduced up to 0.52% by including smart auxiliaries in the energy management problem. More interestingly, the computation time is reduced by a factor of 64 up to 1825, compared with solving a centralized convex optimization problem.

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

Effects of Battery Charge Acceptance and Battery Aging in Complete Vehicle Energy Management

In this paper, we propose a solution to the complete vehicle energy management problem with battery charge acceptance limitations and battery aging limitations. The problem is solved using distributed optimization for a case study of a hybrid heavy-duty vehicle, equipped with a refrigerated semi-trailer for two different battery models. The first battery model takes charge acceptance into account by adding an additional energy state to the optimization problem. The second battery model includes battery aging for which a novel iterative algorithm is proposed. Simulation results show that charge acceptance limitations only have a minor effect on the solution to the energy management problem, while battery aging leads to a trade-off between battery capacity loss and fuel consumption reduction. In particular, a decrease in capacity loss by 260%, leads to a drop in fuel consumption reduction from 9.40% (without battery aging) to 8.61% (with battery aging), both when compared to a conventional vehicle. This is caused by the fact that aging limitations cause less energy to be stored in the high-voltage battery. Still, because energy can also be stored in the refrigerated semi-trailer, smart control of this refrigerated semi-trailer leads to an additional fuel reduction 0.53% in the case where battery aging is incorporated, while it was only 0.09% when battery aging was not considered. In other words, the drop in the fuel consumption reduction caused by battery aging constraints can be partly compensated by smart control of other energy buffers, which shows the true benefit of complete vehicle energy management.

Receding Horizon Control for Distributed Energy Management of a Hybrid Heavy-Duty Vehicle with Auxiliaries

In this paper, a real-time and distributed solution to Complete Vehicle Energy Management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints. The receding horizon control problem is solved for a case study of a hybrid heavy-duty vehicle, equipped with a high-voltage battery system and a refrigerated semi-trailer. Simulations demonstrate that close to optimal performance in terms of fuel consumption is obtained. The average execution time is 11.4 ms demonstrating that the proposed solution method is indeed real-time implementable.

Prelude to Eight Essays on Control Science in the XX-th Century

Complete vehicle energy management (CVEM) aims at minimizing the energy consumption of all subsystems inside a vehicle. When the subsystems consist of energy buffers with linear dynamics and/or power converters with quadratic power losses, the CVEM becomes a large-scale and ill-conditioned optimal control problem for which well-known solution approaches (e.g. Pontryagin’s maximum principle, dynamic programming, convex and derivative-free optimization methods) are not effective. In this paper, we design an efficient solution algorithm for this problem by leveraging the framework of operator splitting methods. To facilitate its implementation, we propose a spectral method that automatically tunes the step sizes at every iteration and, in practice, accelerates convergence. Finally, we consider a case study for a parallel-hybrid vehicle and we illustrate the numerical performance of our method by showing that we can efficiently solve a CVEM problem with very large horizons.