Battery- and Aging-Aware Embedded Control Systems for Electric Vehicles

In this paper, for the first time, we propose a battery- and aging-aware optimization framework for embedded control systems design in electric vehicles (EVs). Performance and reliability of an EV are influenced by feedback control loops implemented into in-vehicle electrical/electronic (E/E) architecture. In this context, we consider the following design aspects of an EV: (i) battery usage; (ii) processor aging of the in-vehicle embedded platform. In this work, we propose a design optimization framework for embedded controllers with gradient-based and stochastic methods taking into account quality of control (QoC), battery usage and processor aging. First, we obtain a Pareto front between QoC and battery usage utilizing the optimization framework. Well-distributed non-dominated solutions are achieved by solving a constrained bi-objective optimization problem. In general, QoC of a control loop highly depends on the sampling period. When the processor ages, on-chip monitors could be used to measure the delay of the critical path, based on which, the processor operating frequency is reduced to ensure correct functioning. As a result, the sampling period gets longer opening up the possibility of QoC deterioration, which is highly undesirable for safety-critical applications in EVs. Utilizing the proposed framework, we take into account the effect of processor aging by re-optimizing the controller design with the prolonged sampling period resulting from processor aging. We illustrate the approach considering electric motor control in EVs. Our experimental results show that the effect of processor aging on QoC deterioration can be mitigated by controller reoptimization with a slight compromise on battery usage.