The prognosis and health management of lithium-ion batteries are extremely important issues for operating performance as well as the cost of energy storage systems in vehicular applications. This is achieved through the estimation of the state-of-health and the prediction of remaining useful life (RUL). This paper presents a lithium battery prognosis model considering the battery-aging conditions. The proposed model is developed based on the Rao-Blackwellization particle filter, which is able to estimate the posterior values of the aging indicators, i.e., capacity and resistance, and to predict the RUL. The particularity of the proposed model is that it considers the aging conditions of batteries as inputs of the prognosis model. In order to validate the proposed method, experiments have been carried out under different aging conditions for three types of lithium-ion batteries. The proposed model performances have been evaluated. A comparison against the particle filter prognosis model is presented. Results highlight the effectiveness of the proposed technique to predict the RUL for different cases: initial conditions, types of lithium-ion batteries, and aging conditions. The RUL prediction using the proposed prognosis model presents a maximum relative error of 6.64%, which is low compared to 14.3% when a simple particle filter prognosis model is used.

This paper proposes a new higher-order sliding mode-based approach to deal with the problem of online state of charge (SOC) and state of health (SOH) estimation for Lithium-ion batteries. The proposed approach is based on the conjunction of a Higher-Order Sliding Mode (HOSM) observer with two Generalized Super-Twisting (GST) observer-based identification algorithms. The HOSM observer provides an exact estimation of SOC, while the two identification algorithms guarantee the finite time estimation of the battery capacity and the battery inner resistance. Thanks to the relationship between the battery capacity and the SOH, an accurate estimation of this latter is obtained. The proposed approach has two main advantages: it ensures SOC estimation using only one observer and estimates the battery parameters without requiring any assumption on the system states. Experimental results using the Worldwide Harmonized Light Vehicles Test procedures (WLTP) are used to illustrate the high efficiency of the proposed approach.

The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. A road, health, and price-conscious optimal cost minimization strategy based on decision making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of vehicles including the hydrogen cost and degradation costs of fuel cell and battery. Second, a decision making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate health states of power sources and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach optimal results on the overall cost and charge sustaining of battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed.

The low durability and high cost of fuel cells and batteries are the main barriers of widely commercial usage of fuel cell hybrid electric vehicles. Energy management is one of the effective methods to increase the durability of power sources and the overall economy of the vehicle. The majority of existing studies focus only on fuel consumption. Therefore, an online applicable cost minimization strategy cooperating fuel cell and battery degradation is proposed to decrease the overall operating cost including hydrogen consumption cost and lifetime costs of fuel cell and battery. Two kinds of degradation models are both designed for fuel cell and battery respectively. Empirical degradation model and prognostic model are used to calculate the degradation cost and estimate the degradation performances of power sources along with aging. Creatively cooperating two kinds of models to precisely quantify the degradation rates of power sources under real automobile operating conditions is firstly studied. Validation under a typical driving cycle shows the effectiveness of the developed cost minimization strategy in increasing the economy of the vehicle. The effects of degradation and failure states of power sources, driving cycles and price evolution of hydrogen and power sources on the optimal cost results are also analyzed.

In this paper, a simplified control scheme is introduced for permanent magnet synchronous motors (PMSMs). The control strategy consists of a direct voltage controller that capitalizes on the motor's model to achieve accurate speed tracking. As such, no explicit currents loop regulation is needed which simplifies the control structure and unlike other control strategies, no motor's parameter knowledge, voltage or current transducer is required. But, the absence of current regulation loops yields higher energy consumption which limits the motor's range of operation. Therefore, a genetic algorithm is presented to determine control gains with optimal current consumption ensuring operation at the full range of the machine. Simulation and experimental results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions. Furthermore, the simplicity of the control scheme makes it a good candidate for a low-cost implementation of real-time PMSM drives.

This paper presents a real-time parameter estimation of a proton exchange membrane fuel cell (PEMFC). The proposed strategy estimates online the PEMFC's resistance since it is directly correlated to its remaining useful life (RUL) assessment. The estimation of the PEMFC's parameters is a difficult task to undertake due to various uncertainties, like temperature and aging, that lead to a drift in parameters and limit the performance of the overall energy system. Therefore, online system identification is essential to track online the PEMFC's time-varying parameters. Unlike other identification techniques, the proposed strategy is based on a simple yet accurate PEMFC's model and adjusts its parameters in real-time using a Lyapunov-based adaptation law, which yields guaranteed stability. Experiments are conducted on a 500-W Horizon PEMFC and results along with a comparison against the well-known Kalman filter highlight the effectiveness of the proposed approach which is instrumental for its numerous applications, such as the energy management of hybrid fuel cell vehicles.