In this paper, we propose an adaptive control approach with fuzzy logic parameter tuning (AFLPT) for the energy management of electric vehicles that are using fuel cell battery hybrid systems. The controller is adaptive to different driving conditions including normal, regenerative, and overload conditions. Specifically, the power flow between the fuel cell (FC) and the Li-ion battery is controlled in real time to maintain the battery state of charge (SOC) at a desirable level while satisfying the FC dynamic constraints. For guaranteeing performance in different driving conditions, the FLPT is integrated with the adaptive controller. Moreover, theoretical properties of the designed controller are analyzed. Simulation and experiment results illustrate the effectiveness of the proposed strategy for FC-battery hybrid systems in electric vehicles.

A new method for the state of charge (SOC) estimation of lithium-ion batteries is proposed based on an inclusive equivalent circuit model in this brief. In particular, we propose to utilize the neural network to estimate the uncertainties in the battery model online. A radial basis function neural network-based nonlinear observer is then designed to estimate the battery's SOC. Following Lyapunov stability analysis, it is proved that the SOC estimation error is ultimately bounded and the error bound can be arbitrarily small. Experimental and simulation results illustrate the performance of the proposed approach. Furthermore, we compare the SOC estimation results of the extended Kalman filter with the proposed radial basis function neural network-based nonlinear observer. The proposed approach has faster convergence speed and higher precision.

In this paper, a feedback linearization controller is proposed for the van compressor in the air supply system of a proton exchange membrane fuel cell. The control goal is to avoid oxygen starvation and reduce power consumption by tracking an optimal reference oxygen excess ratio. Specifically, an improved control-oriented third-order model of the air supply system is proposed with the model identification of the air compressor. The optimal reference oxygen excess ratio is obtained from experiments to maintain a maximum net power. Based on the air supply system model, a nonlinear controller is designed to track the optimal oxygen excess ratio using feedback linearization. Lyapunov-based technique is utilized to analyze the stability of the closed-loop system. Effectiveness of the proposed approach is illustrated by experimental results.

The successful and safe operation of a serially connected battery pack necessitates dynamic energy equalizing to adjust each cell's state of charge (SOC) to the same level, since there exists energy imbalance among its cells. Bidirectional Modified Ćuk converters are utilized as the cell equalizers due to their advantages of integrated infrastructure and modular design. Distinguished from the literature, the maximum allowed cell equalizing current is designed to vary with the change of the battery pack's external current rather than a constant to avoid the cell's current exceeding its limitation. With adaptive quasi-sliding mode observers designed for the cells' SOC estimation, a discrete-time quasi-sliding-mode-based strategy with saturated equalizing current constraints is proposed to have the converters work together efficiently to achieve the cells' SOC equalization. As analyzed in mathematical proofs and shown in experimental results, the cells' actual SOC differences can converge to a tolerant range around the origin in a relatively short time under the designed SOC estimation-based cell balancing method.

A battery pack can see energy imbalance among its cells resulting from cell-to-cell variation in capacity, internal resistance, and other parameters. Its successful and safe operation thus necessitates dynamic energy equalizing to adjust each cell's state-of-charge to the same level. The cell equalizing system for a serially connected battery pack is modeled as a multiagent system here. A consensus-based state-of-charge equalizing algorithm is proposed with its convergence proved through theoretical analysis. Following this development, an interesting and important problem is investigated: how to add extra individual cell equalizers or edges to the original cell equalizing system's topology to maximally accelerate the equalizing process. It is found that the balancing time depends on the algebraic connectivity of the topology graph, which is measured by the second smallest eigenvalue of the graph's Laplacian matrix. Then, a combinatorial 0-1 optimization problem is formulated and addressed to optimally choose added edges that lead to the maximum increase in the algebraic connectivity. The proposed results are validated through simulation and experiments, which demonstrate that the balancing time can be significantly reduced by just adding one optimal individual cell equalizer to the original cell equalizing system.