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Battery aging is a main concern within hybrid and electrical cars. Determining the current state-of-health (SOH) of the battery on board of a vehicle is still a challenging task. Electrochemical Impedance Spectroscopy (EIS) is an established laboratory method for the characterization of electrochemical energy storages such as Lithium-Ion (Li-Ion) cells. EIS provides a lot of information about electrochemical processes and their change due to aging. Therefore it can be used to estimate the current SOH of a cell. Standard EIS methods require the excitation of the cell with a certain waveform for obtaining the impedance spectrum. This waveform can be a series of monofrequent sinusoidal signals or a time-domain current pulse with a dedicated Fourier spectrum. However, any form of dedicated perturbation is not generally applicable on board of an electric vehicle. This work presents a new passive spectroscopy method, which obtains the impedance spectrum directly out of real driving data.

Battery safety is the most critical requirement for the energy storage systems in hybrid and electric vehicles. The allowable battery temperature is limited with respect to the battery chemistry in order to avoid the risk of thermal runaway. Battery temperature monitoring is already implemented in electric vehicles, however only cell surface temperature can be measured at reasonable cost using conventional sensors. The internal cell temperature may exceed the surface temperature significantly at high current due to the finite internal electrical and thermal cell resistance. In this work, a novel approach for internal cell temperature measurement is proposed applying on board impedance spectroscopy. The method considers the temperature coefficient of the complex internal cell impedance. It can be observed by current and voltage measurements as usually performed by standard battery management systems.

Lithium ion technology is state of the art for actual hybrid and electrical vehicles. It is well known that lithium ion performance and safety characteristics strongly depend on temperature. Thus, reliable temperature measurement and control concepts for lithium ion cells are mandatory for applications in electrical cars. Temperature sensors for all individual cells increase the battery complexity and cost of a battery management system. Normally, temperature is measured on module level in current battery packs, without observation of the individual cell temperature. Sensorless cell impedance-based temperature measurement concepts have been published and are validated in laboratory studies. Dedicated test equipment is usually applied, which is not useful for automotive series application. This work describes a practical approach to enable impedance-based sensorless internal temperature measurement for all individual cells using state-of-the art battery management system components.

This work provides a new method for estimating the capacity of an automotive Lithium-Ion cell under real application conditions present in Hybrid and Electrical vehicles. Reliable online capacity estimation is needed for accurate prediction of the remaining electrical driving range. This is a crucial criterion for customer acceptance of Electrical vehicles. Dynamic excitations of real driving cycles, temperature variation as well as the variation of electrical battery behavior with capacity and resistance degradation are challenges that need to be overcome. For this paper, a long-term aging study on 120 automotive Lithium-Ion cells is evaluated with respect to the correlation between electrical cell behavior, temperature and the cell capacity over the complete cell lifetime. The results are used for a dynamic state-space model which provides the current-voltage relationship valid for all aging states of the battery.

A test center for aging analysis and characterization of Lithium-Ion batteries for automotive applications is optimized by means of a dedicated cell tester. The new power tester offers high current magnitude with fast rise time in order to generate arbitrary charge and discharge waveforms, which are identical to real power net signals in vehicles. Upcoming hybrid and electrical cars show fast current transients due to the implemented power electronics like inverter or DC/DC converter. The various test procedures consider single and coupled effects from current profile, state of charge and temperature. They are simultaneously applied on several cells in order to derive statistical significance. Comprehensive safely functions on both the hardware and the software level ensure proper operation of the complex system.

The performance of electrical vehicles strongly depends on characteristics of its energy storage system. A typical lithium-ion battery system is supervised by a battery management system to optimize operation and ensure safety over its whole lifecycle. Advanced battery management systems apply sophisticated fast charging procedures and active cell balancing. In future, impedance spectroscopy based on driving current stimulation for online estimation of the energy storage’s state of health can be expected. For efficient development and testing of such battery management systems it is impractical to use real lithium-ion cells in arbitrary condition of state of charge, temperature and state of health. Consequently, hardware in the loop cell emulators are state of the art. Most of them are limited to low frequency operation. In this paper, a novel modular wide bandwidth high performance lithium-ion cell emulator is introduced.

Plug-In Hybrid Electric Vehicles (PHEV) are becoming increasingly important as an intermediate step on the roadmap to Battery Electric Vehicles (BEV). Li-Ion is the most important battery technology for future hybrid and electrical vehicles. Cycle life of batteries for automotive applications is a major concern of design and development on vehicles with electrified powertrain. Cell manufacturers present various cell chemistries based on Li-Ion technology. For choosing cells with the best cycle life performance appropriate test methods and criteria must be obtained. Cells must be stressed with accelerated aging methods, which correlate with real life conditions. There is always a conflict between high accelerating factors for fast results on the one hand and best accordance with reality on the other hand. Investigations are done on three different Li-Ion cell types which are applicable in the use of PHEVs.

Lithium-ion batteries are the most favored energy storage technology for high-efficiency hybrid and electrical vehicles. Online State-of-Charge (SOC) estimation is required for this application to estimate the remaining cruising distance. However, variation of battery parameters with temperature and cycle life has to be taken into account in order to achieve high accuracy. In this work electrical tests on lithium-ion cells in different States-of-Health are performed and used to extract model parameters such as open-circuit-voltage and impedance. High precision test equipment has been developed to accurately track the true SOC of the cell during measurements. A strong influence of cycle-life on electrical battery behavior is observed. A dynamic cell model based on the measurement results including temperature and aging effects is generated and subsequently used for SOC estimation with an Extended-Kalman-Filter.

Fast charging capability is one of the key requirements for the success of electric vehicles. Considering the growing energy storage capacity of automotive batteries, fast charging can only be achieved using high-power charging systems. This leads to increased power dissipation inside the battery cells. The resulting heat generation inside the battery cell is a critical effect, as cell safety, performance and life time strongly depend on cell temperature and current. This must be considered by a simultaneous current and thermal battery management strategy, which requires reliable information about the individual cell temperature. Sensorless cell temperature can be derived from the cell impedance, where the charging current profile is superimposed by an excitation current and the resulting cell voltages are observed by the battery management system (BMS). An efficient algorithm for the impedance and temperature calculation can be implemented in actual BMS.