Search Results

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

The chemical storage battery is currently the primary choice of automotive powertrain designers for hybrid-electric vehicles. This design suffers from complexity, manufacturing, cost, durability, poor performance predictability and other problems. Additionally, the trend in hybrid powertrain design is to move from high energy density to high power density. A proposed alternative to chemical batteries for some hybrid vehicle applications is an electro-mechanical battery (EMB) that combines an electric machine with hydro-pneumatics to provide energy capture, storage, and propulsion assistance. An initial multi-domain physical system model of an EMB-based hybrid powertrain has been developed in the Simulink environment to show the behavior of the EMB design in a midsize hybrid passenger vehicle application.

The lithium iron phosphate (LFP) cell chemistry is finding wide acceptance for energy storage on-board hybrid electric vehicles (HEVs) and electric vehicles (EVs), due to its high intrinsic safety, fast charging, and long cycle life. However, three main challenges need to be addressed for the accurate estimation of their state of charge (SOC) at runtime: Long voltage relaxation time to reach its open circuit voltage (OCV) after a current pulse Time-, temperature- and SOC-dependent hysteresis Very flat OCV-SOC curve for most of the SOC range In view of these problems, traditional SOC estimation techniques such as coulomb counting with error correction using the SOC-OCV correlation curve are not suitable for this chemistry. This work addressed these challenges with a novel combination of the extended Kalman filter (EKF) algorithm, a two-RC-block equivalent circuit and the traditional coulomb counting method.