An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation

Abstract

Accurate estimation of inner status is vital for safe reliable operation of lithium-ion batteries. In this study, a temperature compensation based adaptive algorithm is proposed to simultaneously estimate the multi-state of lithium-ion batteries including state of charge, state of health and state of power. In the proposed co-estimation algorithm, the state of health is identified by the open circuit voltage-based feature point method. On the basis of accurate capacity prediction, the state of charge is estimated by the adaptive extended Kalman filter with a forgetting factor considering temperature correction. The state of power is determined according to the multi constraints subject to state of charge, operating temperature and maximum current duration. The substantial experimental validations in terms of different current profiles, aging status and time-varying temperature operating conditions highlight that the proposed algorithm furnishes preferable estimation precision with certain robustness, compared with the traditional extended Kalman filter and the adaptive extended Kalman filter. Moreover, the battery pack validation is performed to further justify the feasibility of proposed algorithm when employed in a product battery management system.

Stage of Charge Estimation of Lithium-Ion Battery Packs Based on Improved Cubature Kalman Filter With Long Short-Term Memory Model

“…One representative grey-4 of 30 box model is the equivalent circuit model (ECM), which is constituted by a number of equivalent electric components (such as capacitance and resistance), together with OCV sources, to characterize the battery's external electrical performances. However, the model parameters need to be determined offline in advance or online by different algorithms, such as genetic algorithm (GA) [21], particle swarm optimization (PSO) [22], recursive least square (RLS) [23], and KF [24]. Moreover, the influences of temperature, current as well as SOC on the internal properties are difficult to be taken into account in a unified model, and imprecise parameters may lead to inaccurate modeling performance and also be adverse to SOC estimation.…”

“…To overcome the difficulty of calculating the Jacobian matrix of the LSTM model, an improved SRCKF algorithm is introduced to estimate the SOC in this study. For a traditional SRCKF, single information is utilized to update the state estimation, while the multi-innovation approach exploits historical information to readdress the current state, so as to improve the estimation accuracy [22]. The historical information vector of state estimation can be expressed as: Correspondingly, the Kalman gain matrix can be constructed, as:…”

Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter

“…The Ah integration method is simple and easy to implement. Nonetheless, it highly depends on the precise knowledge of initial SOC and rated capacity as well as the accuracy of current sensor/transducer [14], and the accumulation of sensor error may raise aggravated difference from actual values [15]. The OCV based method is widely accepted as an offline SOC estimation strategy, and the basic principle is to establish a OCV-SOC mapping table according to the nonlinear relationship between SOC and OCV [16].…”

State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

“…To ensure working safety and prolong service life, battery management system (BMS) is usually indispensable for monitoring and controlling their proper and safe operation [2]. State of charge (SOC), as one crucial parameter inside of batteries, indicates the percentage of remaining capacity over the nominal value [3].…”