The problem considered in this paper is the design of a novel control strategy for regenerative suspension systems in road vehicles. It is based on Linear Quadratic Regulator(LQR) with a non standard cost function which optimizes energy harvesting. Knowing that regenerative suspension systems must satisfy many control requirements such as ride comfort and road handling, the energy harvesting objective is also added to the optimization problem. Moreover, a bound on the Ride Index(RI) is applied allowing one to tradeoff between energy harvesting and ride comfort. The proposed control law is compared to two control strategies usually considered in the literature for energy harvesting applications: the Maximum Induced Power Control (MIPC) \(H_2\) and the Regenerative Damper (RD) strategy. Simulative studies are done via MATLAB and Simulink on the same quarter-car model considering four riding cases: a case without any bound on the RI, a case with good ride comfort/small energy harvesting \(RI=0.25\) , a case with a tradeoff between comfort/energy harvesting \(RI=0.47\) and a case with perceivable discomfort/excellent energy harvesting \(RI=0.70\) . The findings from our simulations reveal that the novel LQR strategy allows to recover more energy than the MIPC \(H_2\) and the RD strategies. The condition on the Ride Index efficiently constraints the acceleration and provides ride comfort with the corresponding energy harvesting.

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Energy Harvesting Optimization in Vehicle Suspension Systems via a Non Standard LQ Control Strategy

  • Paul Christian Tesso Woafo,
  • Gianfranco Gagliardi,
  • Alessandro Casavola,
  • Francesco Tedesco

摘要

The problem considered in this paper is the design of a novel control strategy for regenerative suspension systems in road vehicles. It is based on Linear Quadratic Regulator(LQR) with a non standard cost function which optimizes energy harvesting. Knowing that regenerative suspension systems must satisfy many control requirements such as ride comfort and road handling, the energy harvesting objective is also added to the optimization problem. Moreover, a bound on the Ride Index(RI) is applied allowing one to tradeoff between energy harvesting and ride comfort. The proposed control law is compared to two control strategies usually considered in the literature for energy harvesting applications: the Maximum Induced Power Control (MIPC) \(H_2\) and the Regenerative Damper (RD) strategy. Simulative studies are done via MATLAB and Simulink on the same quarter-car model considering four riding cases: a case without any bound on the RI, a case with good ride comfort/small energy harvesting \(RI=0.25\) , a case with a tradeoff between comfort/energy harvesting \(RI=0.47\) and a case with perceivable discomfort/excellent energy harvesting \(RI=0.70\) . The findings from our simulations reveal that the novel LQR strategy allows to recover more energy than the MIPC \(H_2\) and the RD strategies. The condition on the Ride Index efficiently constraints the acceleration and provides ride comfort with the corresponding energy harvesting.