A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles

Zhang, Tao; Guo, Ningyuan; Sun, Xiaoxia; Fan, Jie; Yang, Naifeng; Song, Junjie; Zou, Yuan

doi:10.3390/su13095166

Cited by 36 publications

(8 citation statements)

References 38 publications

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“…Here, the first-order RC equivalent circuit model of the battery cell will be firstly established [43]. Then the model of battery pack will be derived according to the pack topology, which connects the characteristic parameters between the pack and the cell.…”

Section: A Power Battery Modelmentioning

confidence: 99%

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

Zhang¹,

Zhao²,

Sun³

et al. 2022

IEEE Access

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Energy management strategy (EMS) is important to ensure energy-saving performance of hybrid electric vehicle (HEV). However, the power coupling property between different power sources, together with stochastic power demand fluctuation poses great challenges for EMS to achieve desirable performance in real-world scenario. This paper presents an uncertainty-aware energy management strategy for HEV. A speed predictor combining convolutional neural network and long short-term memory neural network is proposed to extract temporal features that could reveal speed change mechanism. Then an online self-adaptive transition probability matrix is constructed to estimate the speed prediction uncertainty. Tube model predictive control (tube-MPC) is finally used to solve the optimization control problem in a receding horizon manner. The robust set introduced in the tube-MPC greatly enhances the optimality and robust-ness of the control sequence under the scenario with speed prediction uncertainty. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the speed prediction accuracy is 47.4% and 23.1% higher than exponential decay rate prediction model and autoregressive integrated moving average model respectively. Compared with traditional rule-based and MPC method, the proposed tube-MPC method could achieve 10.7% and 3.0% energy-saving performance improvement in average.

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Section: A Power Battery Modelmentioning

confidence: 99%

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

Zhang¹,

Zhao²,

Sun³

et al. 2022

IEEE Access

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“…Further, the employment of two ECMs with different structure and parameters reduces the applicability in practice. Another multi-state estimation framework of SOC, SOH, and SOP is proposed in [66], where SOH will be updated quarterly or semi-annually, based on the charge accumulation between two separate SOCs. Thus, the degradation trend of available battery capacity can be captured, yielding the improved performance of SOP estimation at the CCCV mode.…”

Section: Sop-related Multi-state Co-estimationmentioning

confidence: 99%

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

Guo

Shen

2021

Vehicles

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With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future.

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“…The electrochemical reaction is simulated accurately with a complex structure, as too many parameters should be identified using the results of experimental tests 30 . Simplified electrochemical models are used in real‐time applications so that the internal mechanisms are described by electrochemical reactions by constructing the empirical Equations 31 . Voltage and current are used to describe the battery's characteristics and mathematical notations.…”

Section: Introductionmentioning

confidence: 99%

“…30 Simplified electrochemical models are used in real-time applications so that the internal mechanisms are described by electrochemical reactions by constructing the empirical Equations. 31 Voltage and current are used to describe the battery's characteristics and mathematical notations. The computational complexity is reduced accordingly, so that the accuracy is improved by the simplification method.…”

Section: Introductionmentioning

confidence: 99%

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

Wang

Fan

et al. 2022

Intl J of Energy Research

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Summary Accurate state of charge (SOC) estimation is essential for the whole‐life‐cycle safety guarantee and protection of lithium‐ion batteries, which is quite difficult to realize. In this study, a novel weighting factor‐adaptive Kalman filtering (WF‐AKF) method is proposed for the accurate estimation of SOC with a collaborative model for parameter identification. An improved bipartite electrical equivalent circuit (BEEC) model is constructed to describe the dynamic characteristics combined with the mathematical correction of the time‐varying factors. The model parameters are identified online, corresponding to various SOC levels and temperature conditions. Considering the internal resistances, ambient temperature, and complex current rate variations, an adaptive multi‐time scale iterative calculation model is constructed and combined with the real‐time estimation and correction strategies. The maximum closed‐circuit voltage (CCV) traction error is 0.36% and 0.24% for the main pulse‐current charging and discharging processes, respectively. The proposed WF‐AKF algorithm stabilizes the large initial SOC estimation error by tracking the actual value with a maximum error of 0.46% under the complex working condition. The SOC estimation is accurate and robust to the time‐varying characteristics and working conditions even when the initial error is large, providing a safety protection theory for lithium‐ion batteries.

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A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles

Cited by 36 publications

References 38 publications

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

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