A real-time capable enhanced dynamic programming approach for predictive optimal cruise control in hybrid electric vehicles

Wahl, Hans-Georg; Bauer, Kai-Lukas; Gauterin, Frank; Holzäpfel, Marc

doi:10.1109/itsc.2013.6728468

Cited by 22 publications

(9 citation statements)

References 8 publications

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“…In order to compensate for the time lost because of this, the vehicle must drive faster at other parts of the trip. For this example, the values of β necessary to achieve the same trip correspond to velocities of around 70 km/h and 90 km/h in (14) for regenerative braking disabled and enabled respectively. Given a driving corridor or horizon, the total amount of converted energy is greater when enabling regenerative braking.…”

Section: Discussionmentioning

confidence: 99%

“…which is equivalent to (14). There is a unique solution to (28) denoted K = K s , where the constant K s is called the stabilization energy that will depend on how β and the parameters in the resistive force F w are chosen.…”

Section: Partial Powermentioning

confidence: 99%

“…By applying a Dynamic programming (DP) algorithm, the search space and thus the complexity of the problem is reduced [12]. DP is applied to a truck with combustion engine in [13] and a hybrid electric vehicle in [14]. The execution time can be reduced further by applying some heuristic in the search for the optimal trajectory [15] [16].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Speed Control of a Heavy-Duty Vehicle in Urban Driving

Held

Flärdh

Mårtensson

2019

IEEE Trans. Intell. Transport. Syst.

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Fuel efficient driving patterns are well investigated for highway driving, but less so for applications with varying speed requirements, such as urban driving. In this article, the driving mission of a heavy-duty vehicle in urban driving is formulated as an optimal control problem. The velocity of the vehicle is restricted to be within upper and lower constraints referred to as the driving corridor. The driving corridor is constructed from a test cycle with large variations in the speed profile, together with statistics from vehicles in real operation. The optimal control problem is first solved offline using Pontryagin's maximum principle. A sensitivity analysis is performed in order to investigate how variations in the driving corridor influence the energy consumption of the optimal solution. The same problem is also solved using a model predictive controller with a receding horizon approach. Simulations are performed in order to investigate how the length of the control horizon influences the potential energy savings. Simulations on a test cycle with varying speed profile show that 7 % energy can be saved without increasing the trip time nor deviating from a normal driving pattern. A horizon length of 1000 m is sufficient to realize these savings by the model predictive controller. The vehicle model used in these simulations is extended to include regenerative braking in order to investigate its influence on the optimal control policy and the results.

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Section: Discussionmentioning

confidence: 99%

Section: Partial Powermentioning

confidence: 99%

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Optimal Speed Control of a Heavy-Duty Vehicle in Urban Driving

Held

Flärdh

Mårtensson

2019

IEEE Trans. Intell. Transport. Syst.

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“…Another way to reduce the amount of computations performed in the vehicle is to solve the optimal control problem in the cloud [6]. In [7], an optimal cruise control algorithm is developed using DP for a hybrid electrical vehicle having its velocity limited by a driving corridor. The fact that an engine's efficiency depends on engine speed and torque is considered in the optimization problem in [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal powertrain control of a heavy-duty vehicle under varying speed requirements

Henriksson

Flärdh

Mårtensson

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Reducing the fuel consumption is a major issue in the vehicle industry. In this paper, it is done by formulating a driving mission of a heavy-duty truck as an optimal control problem and solving it using dynamic programming. The vehicle model includes an engine and a gearbox with parameters based on measurements in test cells. The dynamic programming algorithm is solved by considering four specific types of transitions: transitions between the same gear, coasting in neutral gear, coasting with a gear engaged with no fuel injection and transitions involving gear changes. Simulations are performed on a driving cycle commonly used for testing distribution type of driving. In order to make sure that the truck does not deviate too much from a normal way of driving, restrictions on maximum and minimum allowed velocities are imposed based on statistics from real traffic data. The simulations show that 12.7 % fuel can be saved without increasing the trip time by allowing the truck to engage neutral gear and make small deviations from the reference trajectory.

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“…As HGVs are limited to a maximum speed that is lower than a passenger vehicle and drive predominantly on free-ways, the optimization problem is less complex than that of a passenger vehicle. DP is applied to a hybrid electric passenger vehicle in [16] considering road sections with varying speed limits on both highway and free-way routes. Traffic conditions are included in the optimization system presented in [17] with different levels of traffic information provided to the optimization system, and the effects are simulated.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles

et al. 2016

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Energy use in internal combustion engine passenger vehicles contributes directly to CO 2 emissions and fuel consumption, as well as producing a number of air pollutants. Optimizing the vehicle velocity by utilising upcoming road information is an opportunity to minimize vehicle energy use without requiring mechanical design changes. Dynamic programming is capable of such an optimization task and is shown in simulation to produce fuel savings, on average 12%, compared to real driving data; however, in this paper it is also applied in real time on a Raspberry Pi, a low cost miniature computer, in situ in a vehicle. A test drive was undertaken with driver feedback being provided by a dynamic programming algorithm, and the results are compared to a simulated intelligent cruise control system that can follow the algorithm results precisely. An 8% reduction in fuel with no loss in time is reported compared to the test driver.

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A real-time capable enhanced dynamic programming approach for predictive optimal cruise control in hybrid electric vehicles

Cited by 22 publications

References 8 publications

Optimal Speed Control of a Heavy-Duty Vehicle in Urban Driving

Optimal Speed Control of a Heavy-Duty Vehicle in Urban Driving

Optimal powertrain control of a heavy-duty vehicle under varying speed requirements

Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles

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