Abstract: Methods and systems for applications and/or virtual machines (“VM”) are provided. As an example, one method includes registering a storage system configured to store data for a VM; creating a policy for the VM for generating a VM backup by a storage management system interfacing with the storage system and a cloud-based microservice; storing, by the cloud-based micro-service, a copy of the VM backup at a cloud-based storage; receiving, by the cloud-based micro-service, an indication from the storage management system of a successful execution of a pre-restore operation upon unregistering the VM from a VM management system; and copying, by the cloud-based micro-service, VM data from the VM backup stored at the cloud-based storage to the storage system for executing a restore operation to restore the VM backup at the storage system.

Abstract: In some embodiments, a battery management system is provided. The battery management system comprises a connector for electrically coupling a battery to the battery management system, at least one sensor configured to detect a battery state, a programmable chip configured to control at least one of charging and discharging of the battery, and a controller device. The controller device is configured to receive at least one battery state from the at least one sensor; provide the at least one battery state as input to a tanks-in-series model that represents the battery; and provide at least one output of the tanks-in-series model to the programmable chip for controlling at least one of charging and discharging of the battery.

Abstract: Electrochemical models for the lithium-ion battery are useful in predicting and controlling its performance. The values of the parameters in these models are vital to their accuracy. However, not all parameters can be measured precisely, especially when destructive methods are prohibited. In some embodiments of the present disclosure, a parameter estimation approach is used to estimate the open circuit potential of the positive electrode (Up) using piecewise linear approximation together with all the other parameters of a single particle model. Up and 10 more parameters may be estimated from a single discharge curve without knowledge of the electrode chemistry using a technique such as a genetic algorithm. Different case studies were presented for estimating Up with different types of parameters of the battery model. The estimated parameters were then validated by comparing simulations at different discharge rates with experimental data.

Abstract: In some embodiments, a battery management system is provided. The battery management system comprises a connector for electrically coupling a battery to the battery management system, at least one sensor configured to detect a battery state, a programmable chip configured to control at least one of charging and discharging of the battery, and a controller device. The controller device is configured to receive at least one battery state from the at least one sensor; provide the at least one battery state as input to a tanks-in-series model that represents the battery; and provide at least one output of the tanks-in-series model to the programmable chip for controlling at least one of charging and discharging of the battery.

Abstract: Electrochemical models for the lithium-ion battery are useful in predicting and controlling its performance. The values of the parameters in these models are vital to their accuracy. However, not all parameters can be measured precisely, especially when destructive methods are prohibited. In some embodiments of the present disclosure, a parameter estimation approach is used to estimate the open circuit potential of the positive electrode (Up) using piecewise linear approximation together with all the other parameters of a single particle model. Up and 10 more parameters may be estimated from a single discharge curve without knowledge of the electrode chemistry using a technique such as a genetic algorithm. Different case studies were presented for estimating Up with different types of parameters of the battery model. The estimated parameters were then validated by comparing simulations at different discharge rates with experimental data.