ADMM applied to energy management of ancillary systems in trucks

Nilsson, Magnus; Johannesson, Lars; Askerdal, Mikael

doi:10.1109/acc.2015.7171866

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…al [1]. Some other control levers are also discussed as direct, indirect methods using spectral collocation, shooting methods etc, [2], [3], [4]. The paper [5] assesses the impact of an eco-driving training program on fuel savings and reduction of CO2 emissions in a well-designed field trial.…”

Section: Introductionmentioning

confidence: 99%

Predictive Optimal Control of Hybrid Line Haul Trucks

Pramanik¹,

Anwar²

2022

Preprint

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Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that the vehicle operates in its true optimal operating region given a variety of constraints such as road grade, load, gear shifts, Battery State of charge (for hybrid vehicles), etc. In this paper a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line haul truck. The problem is solved using a combination of dynamic programming with back tracking and model predictive control. The specific fuel saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features are a function of road grade. The result and analysis shows significant improvement in fuel savings along with NOx benefits. Out of the control features dynamic cruise (predictive) control and dynamic coasting showed the most benefits while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency.

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Section: Introductionmentioning

confidence: 99%

Predictive Optimal Control of Hybrid Line Haul Trucks

Pramanik¹,

Anwar²

2022

Preprint

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“…Nilsson et al [14] consider the ADMM-based distributed energy management of heavy vehicle ancillary systems that include the cooling system, electrical system, and engine accessory loads. The control goal is to minimize the fuel use of the ancillaries over a drive cycle while respecting electrical bus bounds and available energy.…”

Section: Introductionmentioning

confidence: 99%

Distributed Switched Optimal Control of an Electric Vehicle

Meyer

2020

Energies

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Distributed control is investigated to solve an electric vehicle switched optimal control problem faster than centralized control without significant performance change. The powertrain includes a cooling system, supercapacitor, and two switched mode components: a battery with discharging and charging modes and an electric drive with motoring and generating modes. Control-oriented component power flow models are developed with mode and temperature dependence. Component specific power and thermal management optimization problems, subject to these models, require solution for overall powertrain management. The power management problem is switched, having discrete-valued mode selection variables. Both problems are solved in a distributed manner using the alternating direction method of multipliers (ADMM). An ADMM-based algorithm to solve the switched powertrain management problem is proposed; it (i) solves the embedded version of the switched problem that relaxes discrete mode switch values to continuous values and (ii) projects embedded mode selection values onto discrete values and then solves the problem with now known mode selections. The distributed solution approach is demonstrated using a trapezoidal drive profile and three regulatory profiles. The regulatory results are compared to centralized control and the proposed algorithm achieved at least a 3.3 times improvement in solution time with comparable drive performance.

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“…Rule-based strategies require a new set of rules for every subsystem that is added to the CVEM problem; ECMS requires tuning of an equivalence factor for each additional state in the CVEM problem; and MPC requires reformulating the optimal control problem, the cost function and tuning of the penalizing parameters each time a subsystem is added. To enhance the flexibility of vehicle energy management, a distributed offline solution strategy is proposed in [26], and online solutions are proposed in [27][28][29]. In [27], an online game-theoretic approach to CVEM is presented, but prediction information is not taken into account.…”

Section: Introductionmentioning

confidence: 99%

“…In [28], an online game-theoretic approach in combination with MPC is presented, which does utilize prediction information, but requires solving a nonlinear program. In [29], flexibility is obtained by using the Alternating Direction Method of Multipliers (ADMM), while ideas based on ECMS are used to provide the equivalent costs at a supervisory level. While each of these solution strategies is interesting and provides a certain degree of flexibility, they require still a significant amount of tuning, while real-time implementability is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Abstract: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.

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ADMM applied to energy management of ancillary systems in trucks

Cited by 7 publications

References 12 publications

Predictive Optimal Control of Hybrid Line Haul Trucks

Predictive Optimal Control of Hybrid Line Haul Trucks

Distributed Switched Optimal Control of an Electric Vehicle

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

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