Summary In order to meet the required power and energy demand of battery‐powered applications, battery packs are constructed from a multitude of battery cells. For safety and control purposes, an accurate estimate of the temperature of each battery cell is of vital importance. Using electrochemical impedance spectroscopy (EIS), the battery temperature can be inferred from the impedance. However, performing EIS measurements simultaneously at the same frequency on each cell in a battery pack introduces crosstalk interference in surrounding cells, which may cause EIS measurements in battery packs to be inaccurate. Also, currents flowing through the pack interfere with impedance measurements on the cell level. In this paper, we propose, analyse, and validate a method for estimating the battery temperature in a battery pack in the presence of these disturbances. First, we extend an existing and effective estimation framework for impedance‐based temperature estimation towards estimating the temperature of each cell in a pack in the presence of crosstalk and (dis)charge currents. Second, the proposed method is analysed and validated on a two‐cell battery pack, which is the first step towards development of this method for a full‐size battery pack. Monte Carlo simulations are used to find suitable measurement settings that yield small estimation errors and it is demonstrated experimentally that, over a range of temperatures, the method yields an accuracy of ±1°C in terms of bias, in the presence of both disturbances.

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Haptic feedback has two important sources of dynamics: the machine being controlled and the haptic device itself. This paper concentrates on the means of identifying the dynamics of a Phantom Omni haptic feedback device. Two models are compared: a dynamic model with parameters using results from sinusoidal steady state analysis and a data driven model that uses pseudo-random binary sequences (PRBS) for identification. The overall form of the frequency and phase response is welldefined for the dynamics model but for the data driven model a spectral estimate from PRBS response data is used to determine the model order.The results in this paper show that a dynamic equation based minimal model produces accuracy as good as the data driven model. While the data driven model has more fitting accuracy the increase in accuracy is not useful for modelling the physical response as the differences occur at high frequencies where the Phantom arm is not sensitive anyway. The dynamic model is particularly useful as it gives a physical basis for the observed output and the sinusoidal steady state behaviour is useful for exposing non-linearities. Future work includes development and verification an arm inertia model that allows system parameters to be identified from response data at arbitrary arm angles.