Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy

Kamal, Abdus Samad; Mukai, Masakazu; Murata, Junichi; Kawabe, Taketoshi

doi:10.1109/tcst.2012.2198478

Cited by 398 publications

(227 citation statements)

References 18 publications

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“…The MPC can often solve the optimization problem online using some prediction horizon. In Kamal et al (2013) this is done while taking a preceding vehicle into account, in Henzler et al (2015) the focus is on reducing calculation time and in Kirches et al (2013), the gear selection is also taken into account.…”

Section: Introductionmentioning

confidence: 99%

Optimal Speed Trajectories Under Variations in the Driving Corridor

2017

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The optimal speed trajectory for a heavy-duty truck is calculated using the Pontryagin's maximum principle. The truck motion depends on controllable tractive and braking forces and external forces such as air and rolling resistance and road slope. The velocity of the vehicle is restricted to be within a driving corridor which consists of an upper and a lower boundary. Simulations are performed on data from a test cycle commonly used for testing distribution driving. The data include road slope and a speed reference, from which the driving corridor is created automatically. The simulations include a sensitivity analysis on how changes in the parameters for the driving corridor influence the energy consumption and trip time.For the widest driving corridor tested, 15.8 % energy was saved compared to the most narrow corridor without increasing the trip time. Most energy was saved by reducing the losses due to braking and small amounts of energy were saved by reducing the losses due to air resistance. Finally, optimal trajectories with the same trip time derived from different settings on the driving corridor are compared in order to analyse energy efficient driving patterns.

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Section: Introductionmentioning

confidence: 99%

Optimal Speed Trajectories Under Variations in the Driving Corridor

2017

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“…A suitable approach to overcome this problem is to apply model-predictive control (MPC) in a receding horizon fashion, where the optimisation is carried out for a finite prediction horizon and is repeated at every time step. This control strategy has been considered as the tool of choice for the eco-cruise control of fuel-powered cars in several works [4], [5], [6]. Recently, eco-cruise control for purely electric vehicles has been considered in [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Previous works use analytical solutions of the nonlinear optimal control problem based on Pontryagin's Maximum Principle (indirect methods) [2], [3] or alternatively efficient discretisation techniques to solve the nonlinear optimisation problem directly [5], [10], [11]. Using analytical solutions however, the optimal controller cannot be designed in a flexible way since it is then difficult to consider constraints on the states, dynamically changing weightings or measured disturbances.…”

Section: Introductionmentioning

confidence: 99%

A novel model-predictive cruise controller for electric vehicles and energy-efficient driving

Schwickart

Voos

Hadji-Minaglou

et al. 2014

2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Abstract-This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. In this work, a coordinate transformation is used which leads to a linear differential motion equation without loss of information. The energy consumption is modeled by the maximum of a set of linear functions which is determined implicitly by the optimisation problem and thus leads to a piecewise linear model. The reformulations finally result in a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. The controller and the underlying dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified by measurements. Simulation results of the MPC controller and the simulation model in closedloop operation finally provide a proof of concept.

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“…A suitable approach to overcome this problem is to apply model-predictive control (MPC) in a receding horizon fashion, where the optimisation is carried out for a finite prediction horizon and is repeated at every time step. This control strategy has been considered as the tool of choice for the eco-cruise control of fuel-powered cars in several works [3], [4], [5], [6]. Recently, eco-cruise control for purely electric vehicles has been considered in [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…Previous works use analytical solutions of the nonlinear optimal control problem based on Pontryagin's Maximum Principle (indirect methods) [2], [3] or alternatively efficient discretisation techniques to solve the nonlinear optimisation problem directly [5], [9], [6]. Using analytical solutions however, the optimal controller cannot be designed in a flexible way since no constraints on the state variables, dynamically changing weightings or measured disturbances can be considered.…”

Section: Introductionmentioning

confidence: 99%

A real-time implementable model-predictive cruise controller for electric vehicles and energy-efficient driving

Schwickart

Voos²,

Darouach³

2014

2014 IEEE Conference on Control Applications (CCA)

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Abstract-This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. The controller and the underlying dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified by measurements. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. In this work, a coordinate transformation is used which leads to a linear differential motion equation without loss of information. The energy consumption map is approximated by the maximum of a set of linear functions which is implicitly determined in the optimisation problem. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the MPC controller in closed-loop operation finally show the effectiveness of the approach.

show abstract

Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy

Cited by 398 publications

References 18 publications

Optimal Speed Trajectories Under Variations in the Driving Corridor

Optimal Speed Trajectories Under Variations in the Driving Corridor

A novel model-predictive cruise controller for electric vehicles and energy-efficient driving

A real-time implementable model-predictive cruise controller for electric vehicles and energy-efficient driving

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