Lithium ion (Li-ion) is the current leading battery technology. Because of their complex behavior, Li-ion batteries require advanced battery management systems (BMSs). One of the most critical tasks of a BMS is state of charge (SoC) estimation. In this paper, an efficient electrochemical model-based SoC estimation algorithm is presented. The use of electrochemical models enables an accurate estimation of the SoC as well during high current events. However, this often due to the cost of a high computational complexity. In this paper, it is shown that by writing the model as a linearly spatially interconnected system and by exploiting the resulting semi-separable structure an efficient extended Kalman filter (EKF) can be implemented. The proposed EKF is compared with another electrochemical-based estimation and shown to deliver an estimation error of less than 5% also during high current peak

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SUMMARYThis paper describes the application of linear-parameter-varying (LPV) control design techniques to the problem of slip control for two-wheeled vehicles. A nonlinear multi-body motorcycle simulator is employed to derive a control-oriented dynamic model. It is shown that, in order to devise a robust controller with good performance, it is necessary to take into account the dependence of the model on the velocity and on the wheel slip. This dependence is modeled via an LPV system constructed from Jacobian linearizations at different velocities and slip values. The control problem is formulated as a model-matching control problem within the LPV framework; a specific modification of the LPV control synthesis algorithm is proposed to alleviate controller interpolation problems. Linear and nonlinear simulations indicate that the synthesized controller achieves the required robustness and performance.

The anti-lock braking system (ABS) is the most important active safety system for passenger cars. Thanks to tire force measurement, provided for example by the new SKF load sensing hub bearing units, hybrid ABS algorithms can be made simpler and more robust than when only using wheel acceleration measurement. A two-phase algorithm is presented, where the wheel acceleration is controlled in closed-loop and the longitudinal force measurement is used to fire phase switching. Load transfer is accounted for using the vertical force measurement. Realistic simulations show that this simple algorithm can handle changes in vehicle velocity and tire-road friction without extra logic or adaptation of the controller parameters. Stability analysis provides tuning indications. Finally, the algorithm is validated on a tire-in-the-loop experimental facility

The spread of active braking controllers on vehicles\ud with significant mechanical differences and on low-cost products asks for control design approaches which offer easy and fast calibration and re-tuning capabilities. This task is made difficult by the use of model-based control approaches which heavily rely on specific vehicle dynamics descriptions. To address these issues, this brief paper proposes a data-driven approach to active braking\ud control design, grounded on the virtual reference feedback tuning (VRFT) approach complemented with a data-driven nonlinear compensator. The effectiveness of the proposed approach is assessed both on a full-fledged multibody simulator and on a tire-in-the-loop experimental facility