Generic Model Control for Lithium-Ion Batteries

Abstract

Battery Management Systems (BMS) are critical to safe and efficient operation of lithium-ion batteries and accurate prediction of the internal states. Smarter BMS that can estimate and implement optimal charging profiles in real-time are important for advancement of the Li-ion battery technology. Estimating optimal profiles using physics-based models is computationally expensive because of the non-linear and stiff nature of the model equations, involving the need for constrained nonlinear optimization. In this work, we present an alternative approach to control batteries called as Generic Model Control, or Reference System Synthesis. This work enables robust stabilization and control of battery models to set-point as an alternative approach, eliminating the need to perform optimization of nonlinear models. As compared to the generic model control approaches implemented by previous researchers, we implement the same concept using direct DAE numerical solvers. The results are presented for single input single objective problems, and for constrained problems for various battery models. Lithium-ion batteries are used in a wide range of applications ranging from cell phones to electric vehicles (EVs) to electric grids. With the expected decline in the price of batteries over the next few years, the market for EVs and grids is growing rapidly. Long recharging times, capacity fade and thermal runaway are some of the main issues that need to be addressed in order to use batteries safely and efficiently for a longer time. This necessitates the use of smarter battery management systems that can derive optimal use strategies by exploiting the dynamics of the battery.Battery models based on transport, physical, electrochemical and thermodynamics principles can be used to monitor the internal states of the battery and to obtain optimal control strategies. Such models are computationally expensive which limits their use in control applications in real-time. Various issues, such as the onset of dynamics of some internal states only during use of batteries at high charge/discharge rates, and several other states which are active only while charging or discharging, adds to the challenges of the observability of these physics-based electrochemical models. Hence, various approximated and reduced order models have been proposed in the past, to make the models strongly observable in local intervals. 1,[2][3][4][5] These models are also highly non-linear in nature, which adds significant challenges to implement control strategies along with the abovementioned issues. Past researchers have shown multivariable control techniques like Dynamic Matrix Control (DMC), 6 Internal Model Control (IMC), 7 and Model Predictive Heuristic Control (MPHC) 8 for a variety of systems. However, these models suffer from their reliability on the linear approximations of the experimentally obtained step-response data and do not directly consider the full nonlinear model explicitly. Several researchers have been working to design optimal charging profiles ...

Fingerprinting Redox Heterogeneity in Electrodes during Extreme Fast Charging

“…Implication II: Battery management systems (BMS) employing a physics-based control 114,115 make decisions for homogeneous electrodes. Such an approach underpredicts the plating onset, thus considerably delaying execution during XFC.…”

Battery Energy Storage Models for Optimal Control

“…However, there are methods available to estimate some or all of these parameters empirically [55], [58]. SPMs represent each electrode as a single particle [54], [81]- [83] which is useful for modeling the effects of transport phenomena but loses some accuracy at high current, or wherever variations across the electrodes are significant [11], [84]. Figure 12 shows an simple generic SPM.…”

Application of artificial neural network‐based generic model control to multivariable processes

“…Generic model control (GMC) [1] has been a popular nonlinear model-based control technique owing to its simple structure. The original version of Lee and Sullivan [1] and its variants [2][3][4][5][6][7][8] has been evaluated for different systems using exact dynamic process model. The difficulties associated with GMC are requirement of phenomenological process model and online state variable information, which are both time consuming.…”