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. • An adaptive co-estimator is proposed for battery inner state estimation. • The algorithm offers high accuracy at different temperature and aging status. • The state of charge, state of health and state of power are cooperatively estimated. • Comparisons of the proposed algorithm with traditional ones are conducted. • The co-estimator is validated effective in a product battery management system.
Highlights
Lithium-ion batteries have been progressively deployed in electric vehicles (EVs) and energy storage systems because of their long cycle life and high energy density [1]
The proposed AEKF-FF estimator can raise more accurate estimation, compared to the AEKF and extended KF (EKF) algorithms; and the mean error (ME) and root mean square error (RMSE) based on the proposed AEKF-FF are respectively reduced to 0.45% and 0.23% when the process and measurement noise is not imposed
ALGORITHM VALIDATION IN A PRODUCTION BATTERY PACK To further validate the practicability of proposed multi-state co-estimator, a real BMS testbed is set up, as shown in Fig. 8 (a). It consists of a battery pack with six cells connected in parallel and fourteen cells connected in series, a BMS circuit, a direct current (DC) electronic load, a programmable temperature chamber, a laptop computer with controller area network (CAN) test software to save data, and a laptop computer with host computer software to display battery pack parameters
Summary
Lithium-ion batteries have been progressively deployed in electric vehicles (EVs) and energy storage systems because of their long cycle life and high energy density [1]. Motivated by the discussed challenges, a multi-state co-estimation framework for SOC, SOH and SOP is constructed to satisfy demand of real-time implementation and cope with wide operating temperature range To this end, an effective electrical model is built with the consideration of influences arisen by temperature variation. The main contributions of this study can be attributed to the following two aspects: 1) A comprehensive battery model accounting for the operating temperature range of -20 °C to 60 °C is established to efficaciously find the available capacity and OCVSOC relationship with respect to different temperature; 2) An improved AEKF algorithm with a forgetting factor is developed to achieve accurate and reliable SOC estimation by fully considering the temperature variation, and on this basis, accurate estimation of inner states of batteries at high temperature and timevarying temperature are achieved.
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