We consider the problem of optimally designing an excitation input for parameter identification of an electrochemical Li-ion battery model. The conventional approach to performing parameter identification uses standard test cycles. In contrast, we optimally design the input trajectory to maximize parameter identifiability in the sense of Fisher information. Specifically, we derive sensitivity equations for the electrochemical model. This approach enables parameter sensitivity analysis and optimal parameter fitting via gradient-based algorithms. This paper presents a general systematic approach to identify the electrochemical parameters in a non-invasive way. First, we group parameters into two sets: (i) equilibrium parameters, and (ii) dynamical parameters. We also divide the dynamical parameters into subsets by calculating orthogonalized sensitivity, which mitigates linear dependence between parameters. A large number of input profiles have been devised to constitute an input library. Then, the optimal inputs are selected from the input library to maximize the Fisher information, via convex programming. Using this framework a number of relevant experiments are obtained to parameterize. To validate our approach experimentally, we consider a 18650 Lithium nickel cobalt aluminum oxide battery. Compared to the conventional approach, our proposal achieves lower voltage RMSE across all experimental testing cycles.

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In light of recent events, there has been a surge in discussions of defunding police. On one hand, policy that reduces police presence aims to reduce frequency of police violence. On the other hand, downsizing the police force triggers concerns of public safety and police response time. In this work, we use spatial analysis to examine the impact a reduced police force may have on response time. Modeling the transportation system of Chicago as a network, we simulate the response of police officers from stations to incidents. We then use this simulation to calculate the impacts of resource re-allocation from police to alternate responders. Using Chicago's large, open-source police incident response database, we use our simulation to predict how the response time changes subject to various crime and policing scenarios. Our model suggests that the current response time distribution can be maintained with a 30-60% reduction in police staffing levels if some incidents are re-allocated to alternate responders.

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