Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries

Xiangbao Song,Kwok-Leung Tsui,Dong Wang,Fangfang Yang

doi:10.1109/access.2019.2926517

Abstract

State-of-charge (SOC), which indicates the remaining capacity at the current cycle, is the key to the driving range prediction of electric vehicles and optimal charge control of rechargeable batteries. In this paper, we propose a combined convolutional neural network (CNN) - long short-term memory (LSTM) network to infer battery SOC from measurable data, such as current, voltage, and temperature. The proposed network shares the merits of both CNN and LSTM networks and can extract both spatial and temporal features from input data. The proposed network is trained using data collected from different discharge profiles, including a dynamic stress test, federal urban driving schedule, and US06 test. The performance of the proposed network is evaluated using data collected from a new combined dynamic loading profile in terms of estimation accuracy and robustness against the unknown initial state. The experimental results show that the proposed CNN-LSTM network well captures the nonlinear relationships between SOC and measurable variables and presents better tracking performance than the LSTM and CNN networks. In case of unknown initial SOCs, the proposed network fast converges to true SOC and, then, presents smooth and accurate results, with maximum mean average error under 1% and maximum root mean square error under 2%. Moreover, the proposed network well learns the influence of ambient temperature and can estimate battery SOC under varying temperatures with maximum mean average error under 1.5% and maximum root mean square error under 2%.

Highlights

Lithium-ion batteries have gradually become the dominant power source of electric vehicles (EVs) due to their high energy density, high power density, long lifetime and environmental friendliness [1]
As the EV driving environment is usually complicated and the battery will degrade over repeated charge and discharge, a battery management system (BMS) is required to monitor the battery health status and protect the battery from over-charge and over-discharge to ensure the battery operating in a safe window [2]
The proposed convolutional neural network (CNN)-long short-term memory (LSTM) network in Section III is trained with data collected from the DST test, the FUDS test, and the US06 test, and the performance of online SOC estimation is evaluated with data collected from the DFU test

Summary

INTRODUCTION

Lithium-ion batteries have gradually become the dominant power source of electric vehicles (EVs) due to their high energy density, high power density, long lifetime and environmental friendliness [1]. The model-based filtering method is very fast and suitable for real-time applications, but its performance relies heavily on the quality of battery model [11] Many models, such as simple model, combined model, one-state hysteresis model, enhanced self-correcting model, and resistance-capacitance network based equivalent circuit model, have been proposed to estimate the SOC of lithium-ion batteries [12]. 1) A combined CNN-LSTM network is proposed to capture the nonlinear dynamics inside the lithium-ion battery and estimate battery SOC with voltage, current, and temperature measurements. STATE-OF-CHRAGE ESTIMATION BASED ON THE COMBINED CNN-LSTM NETWORK a combined CNN-LSTM network is proposed to model the highly nonlinear dynamics of lithium-ion batteries and estimate battery SOC from measurable voltage, current, and temperature variables. The input gate and the output gate function in the same way

PROPOSED CONVOLUTIONAL LSTM NETWORK

RESULTS

SOC ESTIMATION AT VARYING TEMPERATURES

CONCLUSION