Combining high-energy-density batteries and high-power-density ultracapacitors in fuel cell hybrid electric vehicles (FCHEVs) results in a high-performance, highly efficient, low-size, and light system. Often, the battery is rated with respect to its energy requirement to reduce its volume and mass. This does not prevent deep discharges of the battery, which are critical to the lifetime of the battery. In this paper, the ratings of the battery and ultracapacitors are investigated. Comparisons of the system volume, the system mass, and the lifetime of the battery due to the rating of the energy storage devices are presented. It is concluded that not only should the energy storage devices of a FCHEV be sized by their power and energy requirements, but the battery lifetime should also be considered. Two energy-management strategies, which sufficiently divide the load power between the fuel cell stack, the battery, and the ultracapacitors, are proposed. A charging strategy, which charges the energy-storage devices due to the conditions of the FCHEV, is also proposed. The analysis provides recommendations on the design of the battery and the ultracapacitor energy-storage systems for FCHEVs.

This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.

The implementation of an accurate but also low computational demanding state-of-health (SOH) estimation algorithm represents a key challenge for the battery management systems in electric vehicle (EV) applications. In this paper, we investigate the suitability of the incremental capacity analysis (ICA) technique for estimating the capacity fade and subsequently the SOH of LMO/NMC-based EV Lithium-ion batteries. Based on aging results collected during eleven months of testing, we were able to relate the capacity fade of the studied batteries to the evolution of four metric points, which were obtained using the ICA. Furthermore, the accuracy of the proposed models for capacity fade and SOH estimation was successfully verified (i.e., 0.367 capacity fade error and 2.99% normalized root-mean-square error) using results from the twelve months of aging tests.

In this paper, a novel current control approach called model predictive direct current control (MPDCC) is presented. The controller takes into account the discrete states of the voltage source inverter (VSI), and the current errors are predicted for each sampling period. Voltage vectors are selected by a graph algorithm, whereby the most appropriate vector is chosen based on an optimization criterion. However, this depends on whether the state of the system is transient or steady. In the first case, the current error should be minimized as fast as possible in order to obtain fast dynamics. In the latter one, the VSI switching behavior is optimized since the switching losses account for a large amount of the total converter losses in high-power drive systems. MPDCC has been developed for a general neutral-point isolated resistive-inductive load with an internal voltage source. For demonstration, the presented control strategy has been implemented on a small-scale permanent-magnet synchronous machine drive system with a two-level VSI. This new approach has several advantages. The most important one is that the switching frequency is reduced up to 70% compared to linear control combined with pulsewidth modulation. Second, MPDCC obtains fast dynamic responses, which are already known from, e.g., direct torque control.