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This paper proposes a new fast chargingstrategy for lithium-ion (Li-ion) batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-Time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate the nonuniform Li-ion battery states. Then, a health-and safety-Aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-Triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost.

Lithium iron phosphate battery packs are widely employed for energy storage in electrified vehicles and power grids. However, their flat voltage curves rendering the weakly observable state of charge are a critical stumbling block for charge equalization management. This paper focuses on real-time active balancing of series-connected lithium iron phosphate batteries. In the absence of accurate in-situ state information in the voltage plateau, a balancing current ratio (BCR) based algorithm is proposed for battery balancing. Then, BCR-based and voltage-based algorithms are fused, responsible for the balancing task within and beyond the voltage plateau, respectively. The balancing process is formulated as a batch-based run-to-run control problem, as the first time in the research area of battery management. The control algorithm acts in two timescales, including time-wise control within each batch run and batch-wise control at the end of each batch. Hardware-in-the-loop experiments demonstrate that the proposed balancing algorithm is able to release 97.1% of the theoretical capacity and can improve the capacity utilization by 5.7% from its benchmarking algorithm. Furthermore, the proposed algorithm can be coded in C language with the binary code in 118,328 bytes only and thus is readily implementable in real-time.

Lithium iron phosphate battery packs are widely employed for energy storage in electrified vehicles and power grids. However, their flat voltage curves rendering the weakly observable state of charge are a critical stumbling block for charge equalization management. This paper focuses on real-time active balancing of series-connected lithium iron phosphate batteries. In the absence of accurate in-situ state information in the voltage plateau, a balancing current ratio (BCR) based algorithm is proposed for battery balancing. Then, BCR-based and voltage-based algorithms are fused, responsible for the balancing task within and beyond the voltage plateau, respectively. The balancing process is formulated as a batch-based run-to-run control problem, as the first time in the research area of battery management. The control algorithm acts in two timescales, including time-wise control within each batch run and batch-wise control at the end of each batch. Hardware-in-the-loop experiments demonstrate that the proposed balancing algorithm is able to release 97.1% of the theoretical capacity and can improve the capacity utilization by 5.7% from its benchmarking algorithm. Furthermore, the proposed algorithm can be coded in C language with the binary code in 118,328 bytes only and thus is readily implementable in real-time.

In this paper LQG control over unreliable communication links is examined. That is to say, the communication channels between the controller and the actuators and between the sensors and the controller are unreliable. This is of growing importance as networked control systems and use of wireless communication in control are becoming increasingly common. A proposed approach is to use tree codes to turn lossy channels into ones with a random delay. The problem of how to optimize LQG control in this case is examined, and it is found that to optimize LQG control previous control signals must also be used. Only the situation where communication between the components is done with acknowledgments is examined. An optimal solution is derived for finite horizon discrete hold-input LQG control for this case. The solution is compared with standard LQG control in simulations, which demonstrate that a significant improvement in the cost can be achieved when the probability of delay is high.

Two input-output pairing strategies based on linear quadratic Gaussian (LQG) control are suggested. In the first strategy, denoted linear quadratic interaction index (LQII), input-output pairing suggestions are found from a minimization of the output signal variance. This index not only guides to what pairing should be tried, it also gives a direct measure of how much better a full MIMO controller can perform. The second proposed interaction measure, denoted integrating linear quadratic index array (ILQIA), focuses more on the low frequency behaviour of the considered plant, such as load disturbances. The strategies are used to compare the expected performance of decentralized control structures in some illustrative examples. The pairing suggestions are compared with the recommendations previously obtained using other interaction measures such as the relative gain array (RGA), the Hankel interaction index array (HIIA) and the participation matrix (PM). The new strategies are easy to interpret and give suitable pairing recommendations where other methods may fail.

Recirculating aquaculture systems (RAS) in land based fish tanks, where the fish tank effluent is biologically treated and then recirculated back to the fish tanks, offers a possibility for large scale ecologically sustainable fish production. In order to fully exploit the advantages of RAS, however, the water exchange should be as small as possible. This implies strong demands on the water treatment, e.g. the maintenance of an efficient nitrification, denitrification and organic removal. Because of the RAS complexity, though, dynamic simulations are required to analyze and optimize a plant with respect to effluent water quality. production and robustness. Here, We present a framework for integrated dynamic aquaculture and wastewater treatment modelling, where fish growth and evacuation rate models can be linked to state of the art dynamic wastewater treatment models through massbalance based waste characterization. It provides means to analyze, predict and explain RAS performance. Using this framework we demonstrate how a new and improved RAS configurations is identified.