Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles

Electromobility is a new approach to the reduction of CO2 emissions and the deceleration of global warming. Its environmental impacts are often compared to traditional mobility solutions based on gasoline or diesel engines. The comparison pertains mostly to the single life cycle of a battery. The impact of multiple life cycles remains an important, and yet unanswered, question. The aim of this paper is to demonstrate advances of 2nd life applications for lithium ion batteries from electric vehicles based on their energy demand. Therefore, it highlights the limitations of a conventional life cycle analysis (LCA) and presents a supplementary method of analysis by providing the design and results of a meta study on the environmental impact of lithium ion batteries. The study focuses on energy demand, and investigates its total impact for different cases considering 2nd life applications such as (C1) material recycling, (C2) repurposing and (C3) reuse. Required reprocessing methods such as remanufacturing of batteries lie at the basis of these 2nd life applications. Batteries are used in their 2nd lives for stationary energy storage (C2, repurpose) and electric vehicles (C3, reuse). The study results confirm that both of these 2nd life applications require less energy than the recycling of batteries at the end of their first life and the production of new batteries. The paper concludes by identifying future research areas in order to generate precise forecasts for 2nd life applications and their industrial dissemination.

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State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211018038 ◽

2021 ◽

pp. 095440702110180

Author(s):

Meng Wei ◽

Min Ye ◽

Jia Bo Li ◽

Qiao Wang ◽

Xin Xin Xu

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Unscented Kalman Filter ◽

Lithium Ion ◽

State Of Charge ◽

Support Vector ◽

Battery Management System ◽

Dynamic Neural Network ◽

Soc Estimation ◽

Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

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