Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flows inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC) based algorithm. Inside a MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC algorithms. The contribution of this manuscript consists of developing and evaluating a high-fidelity battery model of a plug-in hybrid electric vehicle (PHEV) that has been used for MPC. Via empirical work and simulation, the impact of a high-fidelity battery model has been evaluated and compared to a simpler model in the context of MPC. It is proven that the new battery model reduces the absolute voltage, state of charge (SoC), and battery power loss error by a factor of 3.2, 1.9 and 2.1 on average respectively, compared to the simpler battery model.

Estimation of a lithium battery electrical impedance can provide relevant information regarding its characteristics. Currently, electrochemical impedance spectroscopy (EIS) constitutes the most recognized and accepted method. Although highly precise and robust, EIS is usually performed during laboratory testing and is not suitable for any on-board application, such as in battery electric vehicles (BEVs) because it is an instrumentally and computationally heavy method. To address this issue and on-line system applications, this manuscript describes, as a main contribution, a passive method for battery impedance estimation in the time domain that involves the voltage and current profile induced by the battery through its ordinary operation without injecting a small excitation signal. This method has been tested on the same battery with different passive voltage and current profile and has been validated by achieving similar results. Compared to the original idea presented in the published conference paper, this manuscript explains, in detail, the previously developed method of transforming the battery impedance from the frequency domain to time domain. Moreover, this impedance measurement is used to estimate more robustly the battery state of charge (SoC) through Kalman filters. In the original published conference paper, only an extended Kalman filter (EKF) was applied. However, in this manuscript, an EKF and an unscented Kalman filter (UKF) are used and their performances are compared.

Model predictive control strategy (MPC) has been identified as an efficient path for reducing fuel consumption, greenhouse gasses (GHG) emission, or degradation of power-train components for electrified vehicles. MPC is an optimization-based control strategy that aims at finding the optimal control actions of a system by predicting its future behaviors. As the main contribution, this paper provides a Pareto-front analysis of the objective function taking into account the equivalent fuel consumption and the battery aging when the PHEV is in the charge sustaining (CS) mode. The results show that the MPC controller can decrease the battery capacity fade by 45% for only increasing the equivalent vehicle fuel consumption of 0.1% compared to an engine on-off thermostat control strategy.

In Electrified Vehicles, the cost, efficiency, and durability of electrified vehicles are dependent on the energy storage system (ESS) components, configuration and its performance. This paper, pursuing a minimal size tactic, describes a methodology for quantitatively and qualitatively investigating the impacts of a full bandwidth load on the ESS in the HEV. However, the methodology can be extended to other electrified vehicles. The full bandwidth load, up to the operating frequency of the electric motor drive (20 kHz), is empirically measured which includes a frequency range beyond the usually covered frequency range by published standard drive cycles (up to 0.5 Hz). The higher frequency band is shown to be more efficiently covered by a Hybrid Energy Storage System (HESS) which in this paper is defined as combination of a high energy density battery, an Ultra-Capacitor (UC), an electrolytic capacitor, and a film capacitor. In this paper, the harmonic and dc currents and voltages are measured through two precision methods and then the results are used to discuss about overall HEV efficiency and durability. More importantly, the impact of the addition of high-band energy storage devices in reduction of power loss during transient events is disclosed through precision measurement based methodology.