Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.

Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends

This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.

State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF

Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility

An overview of new and current developments in state of charge (SOC) estimating methods for battery is given where the focus lies upon mathematical principles and practical implementations. As the battery SOC is an important parameter, which reflects the battery performance, so accurate estimation of SOC cannot only protect battery, prevent overcharge or discharge, and improve the battery life, but also let the application make rationally control strategies to achieve the purpose of saving energy. This paper gives a literature survey on the categories and mathematical methods of SOC estimation. Based on the assessment of SOC estimation methods, the future development direction of SOC estimation is proposed.

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The State of Charge Estimating Methods for Battery: A Review

State of charge Kalman filter estimator for automotive batteries

LTV-MPC for Yaw Rate Control and Side Slip Control with Dynamically Constrained Differential Braking

The control of power flow in a Hybrid Electric Vehicle (HEV) is challenging because of the hybrid structure of the driveline and conflicting performance objectives: fuel consumption minimization, state of charge (SOC) regulation, and drivability. The flexibility and dynamic reconfigurability of modern HEV driveline architectures enable the design of power management control strategies that are able to better address these issues. A decoupling control strategy based on such a driveline model is presented. The driveline has three power sources: an internal combustion engine, an integrated starter alternator, and an electric machine. The power management control strategy consists of a control based upon static minimization of the equivalent fuel cost combined with dynamic control of battery SOC and drivability. By exploiting the structure of the driveline’s dynamic model, decoupling is obtained in the sense that the battery SOC and drivability controls do not affect the power request constraint, nor do they affect each other.

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Power Management Decoupling Control for a Hybrid Electric Vehicle

A state of charge estimator for Nickel-Metal Hydride batteries, suitable for hybrid vehicles automotive applications, is proposed. The estimator is based on a statespace dynamic model of the battery which is obtained by modeling the kinetic of reactions and the diffusion phenomena. State of charge is estimated by computing the dynamic behavior of the Ni(OH)/sub 2/ concentrations in the active nickel-hydroxide film and using measurements of current and voltage. The estimator is tested off-line on experimental data and performs good results.

State of charge estimator for NiMH batteries

In this paper we present the integration of a Linear-Time-Varying Model-Predictive-Control (LTV-MPC), designed to stabilize a vehicle during sudden lane change or excessive speed-entry in curve, with a slip controller that converts the desired longitudinal tire force variation in pressure variation in the brake system. The lateral controller is designed using a three-degrees-of-freedom vehicle model taking into account both yaw rate and side slip angle of vehicle while the slip controller is a nonlinear gain scheduling P with feedforward action. The performances are validated with SIL technique, in particular, the authors use a proprietary simulator calibrated on an oversteering sport commercial car. Simulation results show the benefits of the control methodology used.

Osvaldo Barbarisi

Papers

State of charge Kalman filter estimator for automotive batteries

LTV-MPC for Yaw Rate Control and Side Slip Control with Dynamically Constrained Differential Braking

Power Management Decoupling Control for a Hybrid Electric Vehicle

State of charge estimator for NiMH batteries

A preliminary study to integrate LTV-MPC lateral vehicle dynamics control with a slip control