Wheel force distribution for improved handling in a hybrid electric vehicle using nonlinear control

Fredriksson, Jonas; Andreasson, Johan; Laine, Leo

doi:10.1109/cdc.2004.1429391

Cited by 41 publications

(43 citation statements)

References 4 publications

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“…Control allocation can be used to coordinate the electric motors in individual combinations of drive/brake mode while optimizing energy efficiency. The method proposed in (Fredriksson, Andreasson & Laine 2004, Chen & Wang 2011) utilizes linear/quadratic approximations to formulate an approximate control allocation problem that can be solved numerically online with computational efficiency.…”

Section: Electrical Propulsionmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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Section: Electrical Propulsionmentioning

confidence: 99%

Control allocation—A survey

2013

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“…The case study demonstrated that the stability of the vehicle can be seriously degraded when the electrical fault occur. However, by using a modified variant of the path controller suggested in [3] with a simple method to allocate the forces on each wheel, vehicle stability could be maintained. This inherent capacity to handle an important class of electrical faults is attractive, especially since no additional fault-handling strategy or hardware is needed.…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, the path can also be described by , , . Closed-loop vehicle controllers for vehicles equipped with individual force actuation on each corner are elaborated in [3], [4]. A general flow chart of such a controller is shown in Fig.…”

Section: Closed-loop Vehicle Controlmentioning

confidence: 99%

Stability of an Electric Vehicle with Permanent-Magnet In-Wheel Motors during Electrical Faults

Jonasson

Wallmark

2007

WEVJ

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This paper presents an analysis of the stability of an electric vehicle equipped with in-wheel motors of permanent-magnet type during a class of electrical faults. Due to the constant excitation from the permanent magnets, the output torque from a faulted wheel cannot easily be removed if an inverter shuts down, which directly affects the vehicle stability. In this paper, the impact of an electrical fault during two driving scenarios is investigated by simulations; using parameters from a 30 kW in-wheel motor and experimentally obtained tire data. It is shown that the electrical fault risks to seriously degrading the vehicle stability if the correct counteraction is not taken quickly. However, it is also demonstrated that vehicle stability during an electrical fault can be maintained with only minor lateral displacements when a closed-loop path controller and a simple method to allocate the individual tire forces are used. This inherent capacity to handle an important class of electrical faults is attractive; especially since no additional fault-handling strategy or hardware is needed.

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“…Furthermore, load transfer is included and tyre forces are likewise sensitive to these vertical loads. Similar models are used and detailed in references [Longoria 2009], [Fredriksson 2004]. Vehicle axes are shown in Fig.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…One way to achieve this is via control allocation algorithms [Longoria 2009], [Fredriksson 2004]. Such algorithms typically use constrained nonlinear optimization methods to determine feasible (and in some sense optimal) inputs at the actuators to achieve the required vehiclelevel forces and moments [ , , ]…”

Section: Control Allocation In Limit Handling Via the Hamiltonian Funmentioning

confidence: 99%

Modified Hamiltonian Algorithm for Optimal Lane Change With Application to Collision Avoidance

Gao

Lidberg

Gordon

2015

MM SJ

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Abstract-This paper deals with collision avoidance for road vehicles when operating at the limits of available friction. For collision avoidance, a typical control approach is to: (a) define a reference geometric path that avoids collision; (b) apply low-level control to perform path following. However, there are a number of limitations in this approach, which are addressed in the current paper. First, it is typically unknown whether a predefined reference path is feasible or over-conservative. Secondly, the control scheme is not well suited to avoiding a moving object, e.g. another vehicle. Further: incorrect choice of reference path may degrade performance, fast adaptation to friction change is not easy to implement and the associated low-level control allocation may be computationally intensive. In this paper we use the general nonlinear optimal control formulation, include some simplifying assumptions and base optimal control on the minimization of an underlying Hamiltonian function. A particle model is used to define an initial reference in the form of a desired global mass-center acceleration vector. Beyond that, yaw moment is taken into account for the purpose of enhancing the stability of the vehicle. The Hamiltonian function is adapted as a linear function of tyre forces and can be minimized locally for individual wheels; this significantly reduces computational workload compared to the conventional approach of forcemoment allocation. Several combinations of actuators are studied to show the general applicability of the control algorithm based on a linear Hamiltonian function. The method has the potential to be used in future vehicle control systems across a wide range of safety applications and hence improve overall vehicle agility and improve safety.

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Wheel force distribution for improved handling in a hybrid electric vehicle using nonlinear control

Cited by 41 publications

References 4 publications

Control allocation—A survey

Control allocation—A survey

Stability of an Electric Vehicle with Permanent-Magnet In-Wheel Motors during Electrical Faults

Modified Hamiltonian Algorithm for Optimal Lane Change With Application to Collision Avoidance

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