Adaptive Management of Large Energy Storage Systems for Vehicle Electrification

Abstract: 

Recent progress in battery technology has made it possible to use batteries to power various physical platforms, such as ground/air/water vehicles. These platforms require hundreds/thousands of battery cells to meet their power and energy needs. Of these, automobiles, locomotives, and unmanned air vehicles (UAVs) face the most stringent environmental challenges. In particular, and of special importance to the automotive industry, is the transition from conventional powertrains to (plug-in) hybrid and electric vehicles, all of which are subject to environmental and operational variations. Current state-of-the-art needs significant improvements in the architecture and algorithms of battery management before achieving the desired levels of efficiency and performance. To meet this need, we have been developing---independently of any specific platform---a new comprehensive battery management architecture, called Smart Battery Management System (SBMS). The architecture incorporates and enhances a BMS that includes battery state-of-charge (SoC) and state-of-health (SoH) algorithms as well as battery power management strategies on both pack and cell levels. SBMS enables efficient and intelligent control of each cell in a battery pack to enhance its lifetime and reliability. SBMS is fundamentally different from existing BMSs in several ways. First, it is a prototypical cyber-physical system (CPS), synergistically integrating a large number of battery cells and intelligent control software thereon. That is, the physical (P) part informs the cyber (C) part of its condition (e.g., a cell failure), which then reconfigures the P part (e.g., determining the type of coolant or re-arranging current path for battery cells) by adaptively setting appropriate system parameters based on the information it received from the P part. This reconfigurability enables the system to be adaptive and robust to physical changes and failures as well as environmental conditions. Second, SBMS is efficient in utilizing the battery cells in a pack via an effective charge and discharge scheduling mechanism. This mechanism adaptively selects energy storage elements (batteries or ultra-capacitors) to be discharged and voltage-balanced, thus enhancing capacity and recovery efficiency, and ultimately extending the pack's life and operation-time. Third, SBMS communicates with the external world to facilitate remote diagnoses/prognoses, thus not only improving robustness to cell failures, but also reducing maintenance cost.

The main research tasks of this project are to:

(i) design a dynamically reconfigurable energy storage system to tolerate harsh internal and external stresses;

(ii) develop cell-level thermal management algorithms;

(iii) develop efficient, dependable charge and discharge scheduling algorithms in hybrid energy storage systems;

(iv) develop a comprehensive, diagnostic/prognostic algorithm with system parameters adjusted for making optimal decisions; and

(vi) build a testbed and evaluate the proposed architecture and algorithms on the testbed.