Vehicle electrification increases the fuel efficiency of the transportation sector while lowering emissions. Eventually, however, electric vehicle batteries will reach their end-of-life (EOL) point, when the capacity of the battery is insufficient for operating a motor vehicle. At this point, the battery is typically removed for recycling. This treatment of the electric vehicle battery is not efficient, as there is still a high enough storage capacity that they can be used in various non-vehicular uses. Unfortunately, there are numerous barriers limiting the adoption of re-used electric vehicle batteries. Herein, the authors analyze the limitations and current codes and standards that affect re-purposed battery pack designs. Utilizing these requirements, a bench test setup is designed and built, to determine feasibility of a repurposed electric vehicle (EV) battery for stationary energy storage in Canada.

An accurate and computationally efficient lithium-ion battery model is beneficial when developing state-of-charge (SOC) and state-of-health (SOH) algorithms for battery management systems (BMS). These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effects of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out many experiments, which can be costly and time consuming for BMS manufacturers. To address these issues, this paper makes two contributions to literature. First, a comprehensive battery model is developed, where the ECM parameter surface is generated using a design of experiments (DOE) approach. Second, replication runs are conducted to accurately estimate the measurement noise and determine which model parameters are significant. The technique is then compared with existing approaches from the literature, and it is shown that, by using the proposed method, the same degree of accuracy can be obtained while requiring significantly fewer experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out many experiments.

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