*2.2. Real-Time State of Health Estimation*

Today's BMS technology is inadequate to accurately predict the state of health (SOH) of a battery. The available choices are either to prematurely replace the battery or to wait until an explicit failure event occurs. Both of these choices have undesirable consequences: premature replacement will result in increased cost to the end user and excessive waste to the environment; waiting out will negatively impact the safety and quality of experience to the end user.

Many of the methods proposed in the literature for SOH estimation are data driven methods. The existing approach to SOH estimation differ in terms of the features used to train and the machine learning topology used. For instance, both approaches presented in References [18,19] used neural networks; in Reference [18], the following features were used: change in the SOC, current, temperature and the internal circuit parameters; in Reference [19] the voltage curve was used as a feature. In References [20–22], support vector machines approach was employed for SOH classification. Feng et al. [20] used partial charging voltage curves as there feature to estimate the SOH online while [21] identified the charging time and capacity to be the features in order to estimate the SOH. In Reference [23], a sample entropy algorithm is used identify the measured terminal voltage under hybrid pulse power characterization current profile; this is then used as a feature to estimate the SOH using a sparse Bayesian predictive modeling. Yun and Qin [24] proposed the use of the time required for the terminal voltage to drop from and to a certain value as the feature to train.

Real-time SOH estimation remains one of the open problems in battery management system research.

### *2.3. Optimal Charging*

The state of the art in battery charging is primitive: time consuming, less efficient and less safe compared to gasoline refueling. Research on optimal charging algorithms (OCA) received significant attention in the recent past. One of the most common method of charging is the constant trickle current based charge strategy. Because a low charging current is used, it requires a long charging time (around 10 h) [25]; charging time can be reduced by increasing the charging current, however, as the batter OCV

increases due to charging, this will cause the battery terminal to reach a voltage that is above the safety threshold. Hence, the higher current that is applied at the initial stage needs to be reduced when the terminal voltage reaches a certain threshold value. Consequently, the *constant-current constant-voltage (CC-CV)* [25] strategy has become one of the widely used approaches to fast charging. In order to shorten the charging time and perpetuate the cycle life of the battery a multi-step constant-current charging is used in References [26,27]. The Taguchi-based methods for battery charging [28,29] uses orthogonal arrays to put forward a systematic method to find the optimal solution with guidelines for choosing the design parameters. Another strategy to use is the boost charging strategy, where a very high current is applied to close-to-fully discharged batteries [30]. In pulse-charging methods [31–35], the battery is exposed to very short rest or even deliberate discharging periods during the charging process. Soft-computing approaches can also be used to optimize the battery charging profile. In Reference [36], optimal charging is achieved by simplifying the problem to be in the form of an optimization problem with the objective function of maximizing the charge within 30 min. Through the use of a multistage constant current charging algorithm, the optimal solution can be obtained by using an ant-colony approach. The authors of Reference [37] proposed a universal voltage protocol, its goal is to enhance the charging efficiency and cycle life by applying a certain charging profile, this charging profile is determined based on the SOH of the battery, which is estimated during the optimization process [38]. Recently, in Reference [39], to find the optimal charging strategy, an optimization approach with cost function of time-to-charge and energy loss is used. However, an analytical solution has not been presented; rather a numerical solution is given to the problem. Many other approaches are presented in the literature for battery charging, such as data mining [40,41], genetic algorithm and neural network based strategies [42], Grey-predicted charging system [43].

## *2.4. Fast Characterization*

Two important offline characterizations required in a BMS are the SOC and SOH characterizations. In SOC characterization, the SOC is modelled against the OCV by collecting one full cycle of data (fully charged battery → fully discharged battery → fully charged battery) whereas the state of the art SOH characterization is done against the number of cycle requiring hundreds and even thousands of cycles of data. This makes SOC ans SOH characterization a time-consuming process. Hence, it is important to find ways to reduce characterization time.

One approach to reduce characterization time is to do it in real-time while the battery is in use. Some approaches for real-time SOC characterization were proposed in the past [44,45]. One of the drawbacks of these approaches is due to the fact that simpler OCV-SOC models need to be employed (due to computational bottlenecks in the BMS) for online estimation of parameters; this will lead to loss of accuracy [46]. Secondly, different sources of error can accumulate from other estimated parameters, that will be incorporated during OCV estimation [47]. Lastly, based on the SOC range that the battery goes through, the estimated OCV model will cover that SOC portion—which depends on the battery usage pattern. In orderfor OCV-SOC model to cover the entire SOC range, the battery has to undergo a complete discharge/charge profile—this cannot be guaranteed. In Reference [48], an approach that uses the data pieces-based parameter identification was proposed to estimate the entire OCV-SOC model. However, this approach has its own drawbacks where the modelling error can be high at the initial stages and the the convergence is not always insured.

Compared to SOC characterization, SOH characterization is nearly impossible to do in real-time locally for a particular battery pack. However, the abundance today's connectivity offers an alternative solution for real-time SOC characterization. Figure 2 depicts how a *cloud assisted BMS* can collect data from numerous batteries to estimate crucial parameters for real-time management of battery packs.

**Figure 2. Cloud assisted battery management system.** The abundance of todays connectivity allows crucial parameters related to SOC and SOH characteristics of battery packs in real-time. (image from Reference [49] being reprinted with permission from IEEE).

### *2.5. Battery Reuse*

As counties race towards decreasing their green house gas emissions, the public is being encouraged to use EVs by offering various incentives. As a result, the manufacturing of Lithium-ion (Li-ion) batteries is expected to increase very rapidly in the next few decades due to their expected use in electric vehicles [50]. Battery packs used in electric vehicles are expected to be replaced when they reach about 80% of their original capacity [11], since range is an important quality in EVs. Research on BMS algorithms so far has predominantly focused on the first use of the battery-pack. The batteries retired from the EVs are still an excellent medium of renewable energy storage in other applications, such as renewable energy storage systems [11]. However, it is still not well understood as to how usage affects the SOC and SOH characterizations of a battery pack. Environmental and usage conditions affect battery characteristics; based on how, when and where an EV was predominantly in use, its battery-pack might have significantly different reliability, efficiency and safety compared to another battery-pack that was made by the same manufacturer during the same assembly process. In other words, even though two batteries were identical twins out of the assembly line, after their first retirement, they would possess two different characteristics based on the patterns of environmental and usage conditions that they experienced. Hence, there is a need to invest in research and to develop BMS that ensure safe, reliable and efficient operation of EV batteries during their second use as renewable energy storage systems. Even though the electric vehicle production is expected to grow exponentially in the next few decades [13], research on battery reuse is still in early stages [51,52]. Figure 3 demonstrates the overall block diagram of a BMS during battery reuse. One of the important challenges here is that each used battery pack is different from one another.

**Figure 3. Battery reuse: from scrapyard to powering living rooms.** Millions of vehicles are scrapped each year due to accidents. In the case of electric vehicles, the batteries could be reused store renewable energy. However, more research needs to be done about managing used batteries of various size, chemistry and manufacturers.

### *2.6. Universality*

Existing BFG algorithms depend on prior characterization for accurate estimation of SOC and SOH [2,3]; as a result, their application is limited to certain type of batteries to which they have the parameters for. The state of the art BMS is constrained to a particular chemistry, manufacturer, and size of the battery to which it is characterized for, that is, the present-day BMS is not universal; this restricts battery selection and results in increased cost; also, such a restrictive BMS does not allow one to repurpose used battery packs for energy storage. In smaller, household, applications, custom battery chargers generate excessive electronic clutter and environmental waste.

The first ones to think about the universal battery systems were the battery charger designers who had to address the huge number of different chemistry and types of batteries that in each application requires its own customized charger; this increases the amount of electronic wastage and adds to the cost of the device. Hence, the problem of *universal battery charger* received attention in the literature [7,53–55]. Earlier versions of universal battery chargers are programmed to look for appropriate voltage to terminate the charging process. Most of them used a look-up table of incremental voltage in response to charging by a certain number of Coulombs [7,53–55].

A preliminary achievement regarding the universality objective is reported in Reference [52] where a probabilistic data association filter [56,57] was employed to associate the online measurements from batteries to their model parameters, thus, resulting in a *chemistry-adaptive BFG*. Further research needs to be done on this topic so that reliable algorithms can be developed to extend adaptivity for load-range, size, temperature, nominal voltage and age as well. This would require large computing power that the traditional battery management systems are not allocated for, for example, portable electronics. Cloud computing [58] allows one to outsource intense computing to external sources; that is, by combining information fusion with cloud computing, a greater deal of universality can be achieved in battery management systems, paving the way for optimal battery reuse (see Section 2.5) and reduced electronic clutter in households and work places.

### *2.7. Self Evaluation*

Battery management system evaluation is a very challenging research problem since there are no proven mathematical models to represent the complex features of a Li-ion battery, these features include power fade (PF), capacity fade (CF), temperature effects on parameters, aging, hysteresis and relaxation effects.

There is little literature focusing on BFG algorithm evaluation under realistic usage conditions. The importance of BMS evaluation is discussed in Reference [59]; in Reference [60], the need to minimize power dissipation and extend battery run-time for portable devices is discussed; the advantages of hardware-in-the-loop (HIL) testing to validate a BMS under various failure conditions was motivated in Reference [61]; and a HIL test to validate the BFG using a multi-cell battery pack was proposed in References [62,63].

Evaluating BMS algorithms is a time consuming task [16] that requires research to find efficient solutions. Particularly, the following aspects needs to be studied further:


### **3. Solutions Through Model Based Algorithms**

Figure 4 shows an overall block diagram of a BMS that consists of three important components [49]: BFG, OCA, and cell-balancing circuitry (CBC). The BFG is considered as the the primary component of a BMS since the BFG output is required in both the OCA and CBC. The BFG estimates the SOC and SOH of the battery-pack based on three measurements: voltage, current, and temperature. The OCA is responsible for regulating the battery charging by generating charging waveforms. The charing waveforms vary in complexity; at the simplest level, a charger applies a constant voltage across the battery terminal; in constant-current constant voltage (CC-CV) charging, the battery SOC (and hence the OCV) rises fast due to the relatively high current; then the charing is switched to CV in order to safeguard the battery from overcharging. Complex charging strategies closely monitor critical battery parameters and adaptively alter the charging pattern. The ultimate goal of an OCA is to charge the battery faster without negatively affecting its SOH [64,65]. When new, individual cells in the battery-packs have similar battery capacity and impedance. However, it is well know that after many charge/discharge cycles these parameters can deviate away from one other causing cell imbalance. Cell-imbalance has many drawbacks from reduced power output, reduced cycle life to catastrophic failures, including fire. The CBC helps to maintain the battery-pack balanced. In addition to this, CBC is also responsible for thermal balancing [66] of the battery-pack. In the remainder of this section, recent contributions to some of the BMS components are described.

The BMS consists of several smaller modules that are critical for improving its safety, efficiency and reliability. Many of todays research is focused on improving these individual modules. In the remainder of this section, we brief the details of the several BMS modules that were developed as improvements to the state of the art.

**Figure 4. Functional block diagram of a battery management system.** Three important components of a BMS are battery fuel gauge, optimal charging algorithm and cell balancing circuitry.

### *3.1. Normalized Open Circuit Voltage Characterization*

Open circuit voltage characterization is one of the most important elements of any BMS, as it allows one to estimate the SOC based on a given OCV. Earlier approaches to OCV modelling suggested to store different OCV-SOC parameters at different temperatures. It was shown in Reference [17] that the *normalized approach* to OCV characterization results in a single set of parameters for all temperatures. Further, various models for OCV characterization were evaluated in Reference [17]. Figure 5 shows the results of applying the normalized OCV modelling approach [17] for OCV-SOC characterization. The important advantage of the normalized modelling approach is that the OCV-SOC characterization does

not need to be repeated at multiple temperatures. Just one characterization at room temperature is shown to be enough to cover typical operational temperatures experienced by batteries.

**Figure 5. Normalized open-circuit voltage modelling.** It is shown that the OCV-SOC parameters obtained through the proposed normalized OCV modelling approach in Reference [17,67] showed little variations with temperature.

The combined model and its variations, such as the combined+3 model [17], remain one of the most used approaches to OCV modelling. Many existing OCV models, including combined model and its variants, suffer from the fact they are not defined at the extreme limits of the SOC, that is, (SOC = 0% and SOC = 100%). For example, let us consider the combined model equation where the OCV (*Vo*(*s*) relates to the SOC (*s*) as follows

$$V\_o(s) = \kappa\_0 + \frac{\kappa\_1}{s} + \kappa\_2 s + \kappa\_3 \ln(s) + \kappa\_4 \ln(1-s) \tag{1}$$

where it can be noticed that the function is undefined when *s* = 0 (SOC = 0%) and when *s* = 1 (SOC = 100%). Existing approaches to the above problem not very optimal. In References [67,68], the effect of not scaling on the performance of SOC estimation is formally quantified and an approach was presented to find the optimal scaling factor; further, it was shown in Reference [67] that the optimal scaling factor remained the same across different battery chemistries and temperatures. Figure 6 summarizes the results of scaling

**Figure 6. Scaling.** The scaling approach [67] reduces the worst case error in OCV modelling.

### *3.2. Equivalent Circuit Model Identification*

Li-ion batteries are powered through chemical reactions. modelling such chemical reactions using physics and chemistry result in very complex models that are challenging to solve. In contrast, ECM provided a simplistic, albeit adequately approximate representation of batteries and battery packs. Figure 7 shows a generalized ECM of a battery.

It was shown in Reference [69] that different approximations of ECM in Figure 8 can be used based on the battery load. Using the appropriate ECM can reduce computation time and complexity and give accurate results. Figure 8a shows the ECM when there is a constant low current load. In this mode the hysteresis can be neglected due to the small effect it has and the capacitors can be ommited due to the constant current. The remaining resistances can be lumped together to form the output resistance (*R*0).

The second mode is shown in Figure 8b. This model is used when there is a high current for extended periods of time. Due to the high current, hysteresis cannot be neglected anymore, and must be incorporated in the model. However, the capacitors can still be omitted due to the constant current. The remaining resistances can still be lumped together in one output resistance (*R*0).

Figure 8c shows model 3 that can be used when there is a dynamic load with a constant average load. In this case, the hysteresis cannot be ignored, along with the capacitor/resistor component (*C*1/*R*1). On the other hand, model 4 is shown in Figure 8d where there is a dynamic current with varying average load. Therefore, a second capacitor/resistor (*C*2/*R*2) need to be used for accurate battery modelling.

**Figure 7. Equivalent circuit model of a battery.** Identifying the battery model and estimating its parameters are crucial steps for all aspects of a battery management system, from state of charge estimation to optimal charging to charge and thermal balancing. In practice, reduced models, shown in Figure 8, are employed; In Reference [69] a unified approach to ECM model parameter estimation is developed.

The authors of Reference [69] proposed an approach that is based on weighted least squares method to identify the battery parameters online, which is inexpensive and has high accuracy. This method has the ability to switch between the battery models easily based on the current profile. Furthermore, instead of modelling the hysteresis as a function of the SOC, which can be very complex and inaccurate, the authors proposed a way to model the hysteresis as an error in the OCV, which has the added benefit of fast recovery when the initial SOC is inaccurate.

**Figure 8. Reduced equivalent circuit models of a battery.** Each model is appropriate for different types of loading condition as indicated.

## *3.3. Real-Time Battery Capacity Estimation*

Real-time battery capacity estimation is a very important factor to achieve a universal BMS. It is also one of the ways to improve the accuracy of SOC estimates. The work done in Reference [14] aims to establish an approach that can estimate the battery capacity in real-time. In this paper, two approaches to estimate the battery capacity were investigated; the first approach uses total least squares, while the second approach used the rest states and models the hysteresis as an error in the OCV. Furthermore, both approaches are fused together to estimate the battery capacity with a high accuracy. Finally, HIL approach was used to validate the estimation algorithm; the results showed that it is accurate within 1% of the true value.

### *3.4. Optimized Charging*

Optimal battery charging is one of the most active research areas of BMS. Figure 9 outlines the level-2 and level-1 charging goals.

**Figure 9.** Elements of a smart (optimal) charger.

A closed-form solution to the problem of optimally charging a Li-ion battery was presented in Reference [64] by considering a combination of three cost functions: time-to-charge (TTC), energy losses (EL), and a temperature rise index. It was theoretically shown in Reference [64] that the optimal charging strategy for the simple equivalent model case reduces to the well-known CC-CV policy with the value of the current in the CC stage being a function of the ratio of weighting on TTC and EL and of the resistance of the battery.

In Reference [65], two models were presented for normalized battery capacity: the LAR-*αηγ* model and the control variable dependent model. The first model is based on the number of cycles and the latter is a function of the number of cycles as well as two other charge control parameters, viz., maximum terminal voltage of the battery (*v*max) and maximum charge current (*i*max). In order to evaluate the accuracy of these models experimental data were gathered from aging experiments performed on Samsung GS4 battery. The results show that these methods are far more superior to the bi-exponential capacity model [70].

### *3.5. Adaptive Algorithms for Universality*

Developing a generalized BFG that is independent of battery chemistry can be broken down into two categories. The first category is to simply compile a library of all possible OCV parameters and to select the most suitable OCV model for fuel gauging through online deteciton. In other words, this first approach seeks to resolve the association ambiguity between several possible OCV parameters and the battery being monitored in a supervised way (e.g., employing nearest neighbor or any of the machine learning-based classifiers).

The second category seeks to use online data to estimate the OCV parameters [71–73]; an iterative process is used to keep the OCV parameters and battery capacity up to date. Since users can swap the battery at any time this can cause an issue where the BFG has to be aware of this change and adapt accordingly by restarting the OCV parameter estimation process. Additionally, this routine should only be applied when required. Further, the iterative estimation of SOC, OCV & ECM parameters and the battery capacity can lead to loss of robustness and instability for the BFG algorithm.

One of the first few approaches towards achieving chemistry adaptive BFG was reported in Reference [52] where the probabilistic data association (PDA) methodology was used to achieve this goal.

Here the ultimate goal is to be able to mange an arbitrary battery (present day BMS rely on parameters that are obtained from the same battery type). Figure 10 shows a demonstration of the chemistry adaptivity reported in Reference [52]. Chemistry adaptivity is a desired feature in the secondary applications of used batteries, for example, used EV batteries used in power grid.

**Figure 10. Chemistry adaptive BMS.** The proposed chemistry probabilistically selects the battery parameters based on the measured data from the battery (voltage and current). In the above demonstration, all three models were initialized with equal probability (1/3); within few samples of measured data, the PDA algorithm was able converge to the correct model. (**a**) OCV curves of different chemistries; (**b**) Model probabilities of the PDA algorithm [52].
