Battery (electricity)

Quantum mechanics

Algorithm

Control (management)

State of charge

Lithium (medication)

Working memory

Short-term memory

Extended Kalman filter

Cognition

Endocrinology

Psychiatry

Power (physics)

Computer science

Physics

Long short term memory

Artificial intelligence

Kalman filter

Charge (physics)

Medicine

Term (time)

Control theory (sociology)

Artificial neural network

Moving horizon estimation

Recurrent neural network

State (computer science)

Applied Energy

Because of the extensive applications of lithium-ion batteries (LIBs) in electric vehicles (EVs), the battery management system (BMS) used to monitor the state and guarantee the operating safety of LIBs has been widely researched. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, accurate SOC estimation is challenging because of erratic battery dynamics and SOC variation with current, temperature, operating conditions, etc. In this paper, a method combining a long short-term memory (LSTM) network with an adaptive cubature Kalman filter (ACKF) is proposed. The LSTM network is first utilized to learn the nonlinear relationship between the SOC and measurements, including current, voltage and temperature, and then, the ACKF is applied to smooth the outputs of the LSTM network, thus achieving accurate and stable SOC estimation. The proposed method can simplify the tedious procedure of tuning the parameters of the LSTM network, and it does not need to establish a battery model. Data collected from dynamic stress tests are used as training datasets, while data collected from US06 tests and federal urban driving schedules serve as test datasets to verify the generalization ability of the proposed method. Experimental results reveal that the proposed method can dramatically improve estimation accuracy compared with the solo LSTM method and the combined LSTM-CKF method, and it exhibits excellent generalization ability for different datasets and convergence ability to address initial errors. In particular, the root-mean-square error is less than 2.2%, and the maximum error is less than 4%.

A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter