Journal ArticleDOI
State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering
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TLDR
A more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter supported by experimental data.Abstract:
In this paper, a more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter (EKF) supported by experimental data. A nonlinear battery model is constructed by separating the model into a nonlinear open circuit voltage and a two-order resistance-capacitance model. EKF is used to eliminate the measurement and process noise and remove the need of prior knowledge of initial SOC. A hardware-in-the-loop test bench was built to validate the method. The experimental results show that the proposed method can estimate the battery SOC with high accuracy.read more
Citations
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Journal ArticleDOI
A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
TL;DR: In this article, a comprehensive review of the battery state of charge estimation and its management system for the sustainable future electric vehicles (EVs) applications is presented, which can guarantee a reliable and safe operation and assess the battery SOC.
Journal ArticleDOI
Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
TL;DR: In this paper, the methods for monitoring the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications.
Journal ArticleDOI
A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
TL;DR: In this article, a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs is presented, including the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models.
Journal ArticleDOI
The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles
TL;DR: This paper proposes a co-estimation scheme of state of charge, state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles that is validated in a real battery management system with good real-time performance and convincible estimation accuracy.
Journal ArticleDOI
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
TL;DR: A nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation.
References
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Journal ArticleDOI
Impedance Observer for a Li-Ion Battery Using Kalman Filter
TL;DR: This paper deals with the use of an extended Kalman filter (EKF) for the observation of the parameters of a Li-ion battery lumped model and the electrical model that can be used to represent the main electrochemical phenomena in the battery.
Journal ArticleDOI
Development and Validation of a Battery Model Useful for Discharging and Charging Power Control and Lifetime Estimation
TL;DR: In this paper, a partially linearized (in battery power) input-output battery model was developed for lead-acid batteries in a hybrid electric vehicle, which can be extended to different battery types, such as lithium-ion, nickel-metal hydride, and alkaline.
Journal ArticleDOI
Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems
TL;DR: The proposed merged MISO FNN with RGA (FNNRGA) can achieve faster convergence and lower estimation error than neural networks with the back propagation method and the overfitting suppression features are significantly improved.
Journal ArticleDOI
Linear parameter varying battery model identification using subspace methods
Y. Hu,Stephen Yurkovich +1 more
TL;DR: In this article, a comprehensive identification algorithm that uses linear-algebra-based subspace methods to identify a parameter varying state variable model that can describe the input-to-output dynamics of a battery under various operating conditions is presented.
Journal ArticleDOI
Charge-Depleting Control Strategies and Fuel Optimization of Blended-Mode Plug-In Hybrid Electric Vehicles
TL;DR: Simulation results indicate that the proposed control strategy is more efficient than other strategies of interest and it is possible to implement the control strategy in real time if the total trip distance is known before the trip.