Economy-oriented vehicle adaptive cruise control with coordinating multiple objectives function

(2013): Economy-oriented vehicle adaptive cruise control with coordinating multiple objectives function, Vehicle System This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. A recent design issue of adaptive cruise control systems is how to reduce fuel consumption when following a preceding vehicle. High fuel economy is achievable through reducing acceleration level, however, it is also significantly restrained by two other functional demands, track capability and driver desired response. In the framework of multi-objective coordination, this paper develops and experimentally validates an economy-oriented headway control algorithm for a passenger car with internal combustion engine. The control algorithm is synthesised in a hierarchical structure. The upper controller, undertaking a major coordinating task, is designed based on the model predictive control theory. Fuel economy, tracking capability, and the driver desired response are formulated as its cost function and constraints in a finite prediction horizon. As further analysis indicated, such a design inevitably results in infeasible control inputs in some extreme cases, e.g. urgent situations involving rapid acceleration/deceleration. A constraint softening method is adopted to enlarge the feasible region in the cost of somewhat sacrificing the optimality of the original cost function. Finally, a prototyping controller is developed based on xPC toolbox and equipped in a passenger car. The followed field tests show that, compared to a linear quadratic controller, such an algorithm improves both fuel economy and tracking capability while also being more responsive to driver car-following behaviours.