This paper presents informative battery charging, a novel approach for battery model parameter estimation during fast charge. Our solution comprises three distinct contributions: first, we develop a semi-explicit solution to an optimal fast charging problem for equivalent circuit models with health-conscious voltage constraints; second, we design optimal experiments for battery model parameter estimation; and third, we suggest a strategy for how the fast charging and experimentation currents can be combined while still satisfying constraints and maintaining acceptable charging times. Numerical results show that model parameters can be identified with lower variance if an optimal experiment is added to the charging procedure.

State-of-health estimates of batteries are essential for onboard electric vehicles in order to provide safe, reliable, and cost-effective battery operation. This paper suggests a method to estimate the 10-s discharge resistance, which is an established battery figure of merit from laboratory testing, without performing the laboratory test. Instead, a state-of-health estimate of batteries is obtained using data directly from their operational use, e.g., onboard electric vehicles. It is shown that simple dynamical battery models, based on a current input and a voltage output, with model parameters dependent on temperature and state of charge, can be derived using AutoRegressive with eXogenous input models, whose order can be adjusted to describe the complex battery behavior. Then, the 10-s discharge resistance can be conveniently computed from the identified model parameters. Moreover, the uncertainty of the estimated resistance values is provided by the method. The suggested method is validated with usage data from emulated electric vehicle operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately for a state-of-charge and temperature range spanning typical electric vehicle operating conditions. The identification of the model parameters and the resistance computation are very fast, rendering the method suitable for onboard application.

For the broad commercial success of electric vehicles (EVs), it is essential to deeply understand how batteries behave in this challenging application. This thesis has therefore been focused on studying automotive lithium-ion batteries in respect of their performance under EV operation. Particularly, the  need  for  simple  methods  estimating  the  state-of-health  (SOH)  of batteries during EV operation has been addressed in order to ensure safe, reliable, and cost-effective EV operation. Within  the  scope  of  this  thesis,  a  method  has  been  developed  that  can estimate the SOH indicators capacity and internal resistance. The method is solely based on signals that are available on-board during ordinary EV operation  such  as  the  measured  current,  voltage,  temperature,  and  the battery  management  system’s  state-of-charge  estimate.  The  approach  is based on data-driven battery models (support vector machines (SVM) or system  identification)  and  virtual  tests  in  correspondence  to  standard performance  tests  as  established  in  laboratory  testing  for  capacity  and resistance determination. The proposed method has been demonstrated for battery data collected in field tests and has also been verified in laboratory. After a first proof-of-concept of the method idea with battery pack data from a plug-in hybrid electric vehicle (PHEV) field test, the method was improved with the help of a laboratory study where battery electric vehicle (BEV) operation of a battery  cell  was  emulated  under  controlled  conditions  providing  a thorough validation possibility. Precise partial capacity and instantaneous resistance  estimations  could  be  derived  and  an  accurate  diffusion resistance estimation was achieved by including a current history variable in the SVM-based model. The dynamic system identification battery model gave precise total resistance estimates as well. The SOH estimation method was also applied to a data set from emulated hybrid electric vehicle (HEV) operation of a battery cell on board a heavy-duty vehicle, where on-board standard  test  validation  revealed  accurate  dynamic  voltage  estimation performance of the applied model even during high-current situations. In order to exhibit the method’s intended implementation, up-to-date SOH indicators have been estimated from driving data during a one-year time period.

During long and high current pulses, diffusion resistance becomes important in lithium-ion batteries. In such diffusion-intense situations, a static support vector machine-based battery model relying on instantaneous current, state-of-charge (SOC), and temperature is not sufficient to capture the time-dependent voltage characteristics. In order to account for the diffusion-related voltage dynamics, we suggest therefore the inclusion of current history in the data-driven battery model by moving averages of the recent current. The voltage estimation performance of six different dynamic battery models with additional current history input is studied during relevant test scenarios. All current history models improve the time-dependent voltage drop estimation compared to the static model, manifesting the beneficial effect of the additional current history input during diffusion-intense situations. The best diffusion resistance estimation results are obtained for the two-step voltage estimation models that incorporate a reciprocal square root of time weighing function for the current of the previous 100 s or an exponential time function with a 20 s time constant (1–8% relative error). Those current history models even improve the overall voltage estimation performance during the studied test scenarios (under 0.25% root-mean-square percentage error).

Capacity and resistance are state-of-health (SOH) indicators that are essential to monitor during the application of batteries on board electric vehicles. For state-of-health determination in laboratory environment, standard battery performance tests are established and well-functioning. Since standard performance tests are not available on-board a vehicle, we are developing a method where those standard tests are applied virtually to a support vector machine-based battery model. This data-driven model is solely based on variables available during ordinary electric vehicle (EV) operation such as battery current, voltage and temperature. This article contributes with a thorough experimental validation of this method, as well as the introduction of new features capacity estimation and temperature dependence. Typical EV battery usage data is generated and exposed to the suggested method in order to estimate capacity and resistance. These estimations are compared to direct measurements of the SOH indicators with standard tests. The obtained estimations of capacities and instantaneous resistances demonstrate good accuracy over a temperature and state-of-charge range typical for EV operating conditions and allow thus for online detection of battery degradation. The proposed method is also found to be suitable for on-board application in respect of processing power and memory restrictions.

In regard to the increasing market launch of plug-in hybrid electric vehicles (PHEVs), understanding battery pack performance under electric vehicle (EV) operating conditions is essential. As lifetime still remains an issue for battery packs, it is a necessity to monitor the battery pack's state-of-health (SOH) on-board. Standard performance tests for health evaluation do not apply since operation interruptions and additional testing equipment are beyond question during ordinary EV usage. We suggest a novel methodology of performance estimation from real-life battery data. On the basis of battery pack data collected during PHEV operation, a support vector machine model is constructed that serves as source for performance evaluation figures. The SOH indicator "10 s discharge resistance" as known from hybrid pulse power characterization (HPPC) tests is chosen to exemplify how performance degradation can be followed over a year.

In regard to the increasing market launch of plug-in hybrid electric vehicles (PHEVs), understanding battery pack performance under electric vehicle (EV) operating conditions is essential. As lifetime still remains an issue for battery packs, it is a necessity to monitor the battery pack's state-of-health (SOH) on-board. Standard laboratory performance tests for health evaluation do not apply since operation interruptions and additional testing equipment are out of the question during ordinary EV usage. We suggest a novel methodology of performance estimation from real-life battery data. On the basis of battery pack data collected during PHEV operation, a support vector machine model capturing battery behavior characteristics is constructed. By virtually testing this battery model, access to standard performance evaluation figures can be gained. The SOH indicator "10 s discharge resistance" as known from hybrid pulse power characterization (HPPC) tests is chosen to exemplify how performance can be followed over a year.

When Li-ion batteries are applied in challenging applications like the propulsion of electric vehicles, it is essential to understand their performance and degradation as a function of their use in order to predict and improve their life time. Therefore, data on the behavior of batteries during electric vehicle operation is documented and methods and strategies for the processing of the real-life data are developed. From battery data analysis, insight into the characteristics of Li-ion batteries in electric vehicles can be gained and battery stress factors can be identified. Ultimately, the impact of operation conditions and battery specifications on battery performance and especially on battery performance degradation are intended to be described quantitatively. Recommendations on the optimum operation of the battery in regard of prolonging battery life time are meant to be given.

State-of-health (SOH) estimates of batteries are essential on-board electric vehicles (EVs) in order to provide safe, reliable, and cost-effective battery operation. Here, we present an approach for the estimation of the battery SOH indicator internal resistance. Battery models are constructed on the basis of ordinary EV operating data. The 10 s discharge resistance, which is an established battery figure-of-merit from laboratory testing, can be conveniently computed from the identified model parameters. Dynamical battery models based on a current input and a voltage output with model parameters dependent on temperature and state-of-charge (SOC) are derived using AutoRegressive with eXogenous input (ARX) models. The suggested method is validated with usage data from emulated EV operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately by the proposed model for a SOC and temperature range spanning typical EV operating conditions (average relative estimation error of 1.5%). The method even provides an uncertainty interval for the resistance estimations, which is found to be very narrow. The linear identification of the model parameters and the resistance computation are very fast rendering the method suitable for on-board application.

This paper addresses the need for simple and cost-effective methods that detect the state-of-health (SOH) of batteries in vehicle applications solely based on data readily available from the battery management system without any knowledge of battery properties, prior laboratory measurements or additional equipment.

A power-optimized lithium-ion battery cell is operated in an emulated hybrid electric vehicle (HEV) environment on board a conventional heavy-duty truck. The HEV operation of the battery cell depends on the driving pattern of the truck within set limits. Beyond the HEV operation, the performance of the battery cell is periodically measured with on-board standard pulse and capacity tests. On basis of the battery operating data collected in the field test, support vector machine-based battery models are built. From a model input of current, temperature, state-of-charge, and current history, the model accurately estimates the battery voltage despite the tough HEV operating conditions with high current pulses. This data-driven battery model is used to estimate the battery cell’s charge and discharge resistance as well as capacity, i.e. the performance measures verified with the standard tests. These SOH indicators can be predicted by the model with adequate accuracy for on-board SOH detection and are followed throughout the one-year field test period.