Lithium-Ion Batteries For Hybrid Electric Vehicles Research Articles

A hybrid model predictive and fuzzy logic based control method for state of power estimation of series-connected Lithium-ion batteries in HEVs

Abstract Accurate estimation of the State of Power (SoP) can ensure safe and efficient operation of Lithium-ion batteries in Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs). The cell-to-cell variation within a battery pack is a major challenge towards accurate estimation of the SoP, particularly when the cells get aged. This paper presents a hybrid model predictive and fuzzy logic based control system to accurately estimate the SoP for series-connected Lithium-ion cells. The estimation strategy consists of two steps; the power capability for a single new cell is first reckoned under the light of the model predictive control algorithm. The second step is devoted to designation of a model-less fuzzy logic based control system to compensate for the concurrent aging state and State of Charge (SoC) differences among the cells. Accordingly, the present approach only utilizes the actual values of current and cell voltages together with the Electric Circuit Model (ECM) parameters of the new cell, which are identified off-line. Moreover, it benefits from a closed-loop framework which ends in an accurate and reliable SoP estimation. An experimental setup consisting of fresh and aged LiFePO4 cell samples is designed and the extracted data are utilized to verify the proposed estimation method in a feed-forward simulation model for a HEV. The results indicate that the proposed method can estimate the pack SoP accurately while the safe operation for the cells is guaranteed.

Long-life and high-rate LiVPO4F/C nanocrystals modified with graphene as cathode material for lithium-ion batteries

Graphene modified LiVPO4F/C nanocomposite has been firstly investigated as cathode material for lithium-ion batteries. The LiVPO4F/C nanocrystals embedded on reduced graphene oxide sheets are synthesized via a sol–gel method. The obtained sample of graphene modified LiVPO4F/C is studied comparatively with LiVPO4F/C by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectra and various electrochemical tests. The results reveal that the modification of LiVPO4F/C nanocrystals with graphene can form an effective conducting network, which can greatly improve the electronic conductivity and lithium ion transport. Thus, the as-synthesized nanocomposite exhibits excellent high-rate capability and cycling stability. In the voltage range of 3.0–4.5V, the graphene modified LiVPO4F/C delivers a reversible discharge capacity of 151.6 (nearly to its theoretical capability of 156mAhg−1) and 147.8mAhg−1 at 0.1 and 0.5C, respectively. It also achieves an improved cyclability with capacity retention ratio of 91.4% after 300cycles at a higher rate of 10C. Therefore, it is of great potential use as a cathode material in rechargeable lithium-ion batteries for hybrid-electric vehicles and electric vehicles.

Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles

State of health (SOH) determination becomes an increasingly important issue for a safe and reliable operation of lithium-ion batteries in hybrid electric vehicles (HEVs). Characteristic performance parameters as capacity and resistance change over lifetime and have to be determined precisely.This work deduces two different parameter estimation methods to identify the SOH of battery resistance and investigates the feasibility of an application in HEVs. First, a knowledge-based algorithm of a developed structured neural network (SNN). Thereby, the structure of the network is adopted from the mathematical description of the electrical equivalent circuit model. Two main advantages expected from a SNN compared to a regular neural network are: first a reduced structure and complexity of the network through predefined functions and thus faster computation, second the possibility to get access to internal parameters of the model. In order to verify a proper operation and performance of the developed SNN, a model-based second parameter estimation method is used with the well established the extended Kalman filter (EKF) algorithm.Furthermore, the developed algorithms are applied on real-vehicle data of a HEV battery at begin of life and after 170,000km. A verification of the identified states against reference data based on electro-chemical impedance spectroscopy shows nearby identical results for SNN and EKF. Additionally, a comparison of implementation effort and computation time isgiven.

Optimizing a Nickelate Cathode for Lithium-Ion Batteries for Hybrid-Electric Vehicles

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