Predictive energy management of hybrid long-haul trucks

Johannesson, Lars; Murgovski, Nikolce; Jonasson, Erik; Hellgren, Jonas; Egardt, Bo

doi:10.1016/j.conengprac.2015.04.014

Cited by 84 publications

(29 citation statements)

References 19 publications

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“…For problems comprising two or more continuous states, online control strategies include stochastic DP [15] that predicts future driving cycles and model predictive control where the velocity, state of charge (SOC), and engine on/off and gear profiles are simultaneously optimized, either by combining direct methods, i.e., convex optimization and DP [16], [17], or by combining DP and PMP [18]. Overviews of different methods can be found in [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

Ghandriz

Jacobson

Murgovski

et al. 2021

IEEE Trans. Veh. Technol.

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With the objective of reducing fuel consumption, this paper presents real-time predictive energy management of hybrid electric heavy vehicles. We propose an optimal control strategy that determines the power split between different vehicle power sources and brakes. Based on model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons with a length of 5-20 km. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge, which is verified using a high-fidelity vehicle plant model. The main contribution of this paper is the development of a sequential linear program for predictive energy management that is faster and simpler than sequential quadratic programming in tested solvers and provides trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to that of two different sequential quadratic programs.

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Section: Introductionmentioning

confidence: 99%

Real-Time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

Ghandriz

Jacobson

Murgovski

et al. 2021

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

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“…2 Therefore, most of the currently available predictive systems only use digital maps as source of information, and predictive operating strategies are developed based on environmental data of a map, which help to reduce the total demand of energy. 3–6 Moreover, comfort 7 and security applications, 8,9 that take information from vehicle communication into account, have been examined for a long time by researchers. Nowadays, also functionalities focusing on energy consumption reduction are under development.…”

Section: Introductionmentioning

confidence: 99%

Smart rule-based diesel engine control strategies by means of predictive driving information

Vagnoni

Eisenbarth

Andert

et al. 2019

International Journal of Engine Research

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The increasing connectivity of future vehicles allows the prediction of the powertrain operational profiles. This technology will improve the transient control of the engine and its exhaust gas aftertreatment systems. This article describes the development of a rule-based algorithm for the air path control, which uses the knowledge of upcoming driving events to reduce especially [Formula: see text] and particulate (soot) emissions. In the first section of this article, the boosting and the lean [Formula: see text] trap systems of a diesel powertrain are investigated as relevant sub-systems for shorter prediction horizons, suitable for Car-to-X communication range. Reference control strategies, based on state-of-the-art engine control unit algorithms and suitable predictive control logics, are compared for the two sub-systems in a model in the loop simulation environment. The simulation driving cycles are based on Worldwide harmonized Light-duty Test Cycle and Real Driving Emissions regulations. Due to the shorter, and consequently more probable, prediction horizon and the demonstrated emission improvements, a dedicated rule-based algorithm for the air path control is developed and benchmarked in the Worldwide harmonized Light-duty Test Cycle as described in the second part of this article. Worldwide harmonized Light-duty Test Cycle test results show an improvement potential for engine-out soot and [Formula: see text] emissions of up to 5.2% and 1.2%, respectively, for the air path case and a reduction of the average fuel consumption in Real Driving Emissions of up to 1% for the lean NOx trap case. In addition, the developed rule-based algorithm allows the adjustment of the desired NOx–soot trade-off, while keeping the fuel consumption constant. The study concludes with brief recommendations for future research directions, as for example, the introduction of a prediction module for the estimation of the vehicle operational profile in the prediction horizon.

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“…A second area of research has emerged more recently, with the introduction of adaptive cruise control systems. Under the assumption that the power unit torque, and therefore the speed of the vehicle, can be controlled, eco-driving strategies were assessed offline using dynamic programming [21], [22], nonlinear optimization [23], [24] and Pontryagin's minimum principle [25], [26], whereby the implementation in real-time was mostly based on MPC [27], [28]. Nevertheless, such approaches aim at minimizing the fuel consumption and sometimes also pollutant emissions as well as battery wearing, but never solve minimum time control problems.…”

Section: Introductionmentioning

confidence: 99%

Equivalent Lap Time Minimization Strategies for a Hybrid Electric Race Car

Salazar

Balerna

Chisari

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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The powertrain of the Formula 1 car is composed of an electrically turbocharged internal combustion engine and an electric motor used for boosting and regenerative braking. The energy management system that controls this hybrid electric power unit strongly influences the achievable lap time, as well as the fuel and battery consumption. Therefore, it is important to design robust feedback control algorithms that can run on the ECU in compliance with the sporting regulations, and are able to follow lap time optimal strategies while properly reacting to external disturbances. In this paper, we design feedback control algorithms inspired by equivalent consumption minimization strategies (ECMS) that adapt the optimal control policy implemented on the car in real-time. This way, we are able to track energy management strategies computed offline in a lap time optimal way using three PID controllers. We validate the presented control structure with numerical simulations and compare it to a previously designed model predictive control scheme.

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Predictive energy management of hybrid long-haul trucks

Cited by 84 publications

References 19 publications

Real-Time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

Real-Time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

Smart rule-based diesel engine control strategies by means of predictive driving information

Equivalent Lap Time Minimization Strategies for a Hybrid Electric Race Car

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