State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

Electronic battery tester with tailored compensation for low state-of charge

Abstract: Various embodiments of an improved electronic device for testing or monitoring storage batteries that may be only partially charged are disclosed. The device determines the battery's small-signal dynamic conductance in order to provide either a proportional numerical readout, displayed in appropriate battery measuring units, or a corresponding qualitative assessment of the battery's relative condition based upon its dynamic conductance and electrical rating. The device also determines the battery's terminal voltage in an essentially unloaded condition and utilizes this information to automatically correct the measured dynamic conductance. The automatic correction is performed by the electronic device using information or functions which are tailored for the particular type of battery being tested. By virtue of this automatic correction, the quantitative or qualitative information displayed to the user conforms with that of a fully-charged battery even though the battery may, in actual fact, be only partially charged. If the battery's state-of-charge is too low for an accurate assessment to be made, no information is displayed. Instead, an indication is made to the user that the battery must be recharged before testing.

Battery state of charge gauge

Abstract: A circuit for detecting and indicating the actual state of charge of a storage battery in response to signals sampled from various points in the circuit powered by the battery. Such circuit is adapted to calculate the open circuit voltage of the battery, it being realized that the open circuit voltage is directly proportional to the battery state of charge. A function generator is constructed to simulate the characteristic curve of the battery relating the internal resistance to the percent charge. Feedback is provided from the output of the indicating circuitry to the bias point of the function generator for causing the generator to produce a signal related to the actual internal resistance of the battery. The circuit is adapted to provide a reliable indication of the amount of usable energy remaining in the battery, irrespective of the conditions under which the battery was discharged.

Battery state of charge estimation using a load-classifying neural network

Abstract: Battery state-of-charge estimation is an important component in battery management system design. Many known issues with lithium ion batteries such as performance decay, accelerated aging and even hazardous incidents were associated with faulty state-of-charge estimation. Different estimation algorithms can be summarized in a nutshell as: 1) modeless approaches, i.e. columbic counting; 2). model based observers, i.e. extended Kalman filter; and 3). data driven nonlinear models, i.e. neural networks, and learning machines. This paper adopts the third approach, and proposes a new architecture for SoC estimation using a load-classifying neural network. This approach pre-processes battery inputs and categorizes battery operation modes as idle, charge and discharge, with three neural networks trained in parallel. Using a vehicle drive cycle load profile for model training and a pulse test duty cycle for validation, the proposed method yields a 3.8% average estimation error. This result demonstrates that data driven machine learning approach can deliver estimation performance comparable with other advanced observer designs. The neural network however has a simpler model training procedure, boarder choice of training data, and smaller computational cost. In addition, with simple filtering and output constraints, estimation error spikes associated with ‘uncharted’ inputs can be effectively suppressed.