Battery health target tracking for HEVs: Closed-loop control approach, simulation framework, and reference trajectory optimization

In this paper, we address the trade-off between primary energy consumption and battery wear for hybrid electric vehicles in an optimal manner, for which we provide three contributions: First, we suggest a control structure to track a battery lifetime target in a closed control loop by incorporating periodic measurements of the state of health. This feedback enables the energy management system to reliably meet the target lifetime in the presence of disturbances and model mismatch. We validate the control scheme in a case study featuring a battery-assisted trolley bus. In this case study, we show that without the proposed measurement feedback and in the presence of disturbances and model mismatch, the sub-optimal use of the battery can either result in an increase in energy consumption of up to 9% over the vehicle's lifetime or in a prematurely required battery replacement. Second, to speed up the necessary calculations, we devise an algorithm that is able to perform simulations of a complete vehicle lifetime in less than a minute. A comparison to a standard simulation approach shows that our approach is able to accurately calculate both energy consumption and battery degradation with an error of less than 1% on average, while the execution time is reduced by a factor of about 70000. Third, we numerically optimize the battery health trajectory over the vehicle lifetime. We show that, while a quadratic health trajectory leads to improved energy efficiency, for the specific vehicle and cell technology considered in our case study, a linear trajectory results in only a small energy penalty of 0.05% over the vehicle lifetime.