Real-Time Battery Thermal Management for ElectricVehicles

Eugene Kim, Jinkyu Lee and Kang G. ShinReal-Time Computing Laboratory

Department of Electrical Engineering and Computer ScienceThe University of Michigan, Ann Arbor, MI 48109-2121, U.S.A.

{kimsun, jinkyul, kgshin}@umich.edu

ABSTRACTElectric vehicles (EVs) are powered by a large numberof battery cells, requiring an effective battery manage-ment system (BMS) to maintain the battery cells inan operational condition while providing the necessarypower efficiently. Temperature is one of the most impor-tant factors for battery operation, and existing BMSeshave thus employed simple thermal management poli-cies so as to prevent battery cells from very high andlow temperatures which may likely cause their explo-sion and malfunction, respectively. In this paper, westudy thermo-physical characteristics of battery cells,and design a battery thermal management system thatachieves efficiency and reliability by active thermal con-trols.

We first analyze the effect of a temperature change onthe basic operation of a battery cell. We then show howit can cause thermal and general problems, e.g., a ther-mal runaway that results in explosion of battery cells,and unbalanced state of charge (SoC) that degrades bat-tery cells’ performance. Based on this understandingof thermal behavior, we finally develop temperature-control approaches which are then used to design a bat-tery thermal management system. Our simulation re-sults demonstrate that the proposed thermal manage-ment system improves battery performance by up to58.4% without compromising reliability over the exist-ing simple thermal management.

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Categories and Subject DescriptorsC.3 [Special-Purpose and Application-Based Sys-tems]: Real-time and embedded systems; I.6.3 [Simulationand Modeling]: Applications

1. INTRODUCTIONWhile electric vehicles (EVs) become popular for their

environmental friendliness and low fuel cost, they havenot fully replaced internal combustion engine vehiclesdue to the risk of explosion of battery cells, the highprice for the large number of battery cells required, andthe limited availability of charging stations. As manyresearchers pointed out [1, 4, 14, 23], temperature is oneof the most critical factors in designing and operatingEVs. For example, an extremely high temperature maylead to explosion or performance degradation of batterycells. In contrast, a battery system operating at a verylow temperature might be dysfunctional or have a lowcapacity due to low reaction rates with freezing elec-trolytes. In addition, the discharge rate of cells varieswith temperature, altering their capacity.

To address the challenges related to temperature, mostautomotive manufacturers have developed their own ther-mal management systems for their EVs [9, 10]. Thatis, a BMS (Battery Management System) monitors thetemperature of battery cells, and triggers the thermalcontrol when temperature deviates from the normal op-erational range. This control includes both cooling andheating, and existing controls are all or nothing—whetheror not they cool/heat all the battery cells connected inparallel. However, such coarse-grained controls resultin a large safety margin and hence inefficiency. Moreimportantly, they do not exploit temperature for moreefficient management, in that more sophisticated con-trols of temperature even within the normal operationalrange may yield better battery performance.

The goal of this paper is to develop thermal man-agement for an efficient and reliable BMS. Efficiency isdefined by “the ratio of the useful energy delivered bya dynamic system to the energy supplied to it” [26]; weachieve efficiency by maximizing operation-time [18], or

the cumulative time for a BMS to provide the requiredpower after a full charge. We achieve reliability byguaranteeing that a BMS provides the required powerthroughout the given battery warranty period withoutan explosion or malfunction, while letting its cells un-dergo charge-and-discharge cycles.

To improve efficiency without compromising reliabil-ity, we need a cyber-physical perspective of battery ther-mal management, integrating and coordinating betweencyber and physical parts. For physical parts, we shouldunderstand thermo-physical characteristics of batterycells and external thermal stress conditions, as they havesignificant impact on battery performance. By care-fully accounting for these non-linear physical propertiesand abstracting the characteristics in the cyber spaceas shown in Fig. 1, we can develop desirable thermalmanagement that reduces the safety margin, therebyincreasing the efficiency of the entire battery system inEVs.

To achieve this goal, we use temperature as a controlknob; beyond a simple temperature control only for thenormal operational range, we design a battery thermalmanagement system, which actively controls tempera-ture for more efficient and reliable operations. This re-quires understanding the thermal and general issues ofbatteries that affect the efficiency and reliability. There-fore, we analyze the issues based on battery thermo-physical characteristics and their impact on the electri-cal state of battery cells. Based on this analysis, we de-rive strategies in achieving the goal, and then proposea battery thermal management system with cell-levelthermal controls. Our policy boosts the performanceof cells temporarily when high power is required, whileresting cells otherwise to reduce stresses; we will presentdetails of our policy along with their underlying princi-ples. To evaluate the proposed BMS, we adopt realis-tic workloads based on real driving patterns, and sim-ulate them with a widely-used battery simulator. Oursimulation results demonstrate the effectiveness of theproposed battery thermal management, improving theoperation-time up to 58.4%, without sacrificing reliabil-ity, over the existing BMS.

This paper makes the following three main contribu-tions:

• Abstraction of thermo-physical characteristics inthe cyber-space to address the issues associatedwith efficient and reliable thermal management;

• Design of a battery thermal management systemwith a new architecture, which is, to our best knowl-edge, the first comprehensive study to exploit tem-perature as a control knob for BMSes; and

• In-depth evaluation of the proposed thermal man-agement, demonstrating its significant improvementof efficiency without compromising reliability.

Abstraction Models

Channel control

Charging channel

Battery Manager

Energy sources

Battery array

Discharging channel Electric loads

Cyber space

Physical space

Cooling channel

Figure 1: Cyber-physical perspective for a BMS

Itotal

I2

→

- Vo +

Cell 2

- Vo_tot

+

Cell 1

Cell 3

Cell 8

Cell 7

Cell 9

Cell 5

Cell 4

Cell 6

Cell 11

Cell 10

Cell 12

Cell 14

Cell 13

Cell 15

Cell 17

Cell 16

Cell 18

Cell 20

Cell 19

Cell 21

Cell 23

Cell 22

Cell 24 Cell3

I3 →

Cell2

Cell1 I1

→

Battery pack ∋ {Module1, … , Module8} Battery module1 ∋ {Cell1, Cell2 , Cell3}

Module1 Module2 Module3 Module4 Module5 Module6 Module7 Module8

Figure 2: A battery pack with modules and cells

The paper is organized as follows. Section 2 presentsgeneral and thermal management approaches in existingBMSes, and Section 3 formally states the problem wewant to solve via thermal management. Section 4 de-scribes physical characteristics related to thermal andgeneral issues of large-scale battery systems, and Sec-tion 5 presents our battery thermal management sys-tem. Section 6 evaluates our system via realistic simu-lations, and finally, Section 7 concludes the paper.

2. BACKGROUNDAs illustrated in Fig. 2, a battery pack consists of some

interfaces (e.g., electrodes) and several battery modules,each of which is composed of several battery cells. Ina battery module, all the battery cells are connectedin parallel, reducing the possibility of battery failurecaused by a cell-level failure. The modules in a pack areusually connected in series, enabling the battery packto provide a high voltage and power.

A BMS is responsible for powering EVs while protect-ing hundreds/thousands of battery cells from damageand keeping them in an operational condition. For aBMS to perform these functions, battery cells shouldbe properly controlled because their safety and perfor-mance depend on stress conditions around them. Inthis section, we discuss the battery properties and therelated work thereof.

2.1 Battery Scheduling for Efficient Battery Man-agement

Rate-capacity and recovery effects are the most promi-nent physical properties for efficient battery manage-ment. The rate-capacity effect means that the higherthe discharge rate, the lower the deliverable capacity.The recovery effect means that a rest restores the outputvoltage temporarily dropped by large discharge current.Therefore, we can increase the capacity of batteries, byminimizing the discharge rate per cell and resting bat-tery cells.

State-of-Charge (SoC) represents percentage of deliv-erable charge, from 0% with no charge, to 100% withfull, and balancing SoC is one of the most critical issuesaffecting the performance of large-scale battery systemsbecause the performance of a large battery pack dependson the electrical state of the most worn battery cell inthe pack.

To achieve better battery performance, a line of workhas focused on battery scheduling that equalizes SoCand/or reduces the discharge rate [2, 11, 12, 17, 18].They control switches to draw energy from battery cellsat a proper discharge rate. For example, when an elec-tric motor needs high power, a battery manager con-nects all the cells to the electric load to improve batteryefficiency. In contrast, when the motor demands lowpower from the battery, the battery manager discon-nects some cells with low SoC, achieving recovery of theoutput voltage and equalization of SoC.

2.2 Thermal Management of Large-scale Bat-teries

Besides discharge behaviors and SoC balancing, bat-tery thermal characteristics are also important to theirefficiency, operation and safety. Of many characteris-tics, we will focus on the following two major charac-teristics. First, battery efficiency improves temporar-ily at “instant high temperature” due to the increasedchemical reaction rate and ion mobility. However, the“cumulative exposure to high temperature” causes thepermanent lifetime to decline because of its accelera-tion of irreversible side reaction. Therefore, most BM-Ses are required to restrict each cell within a certaintemperature range to achieve reasonable performance.Every EV must thus be equipped with a thermal man-agement system that keeps cell temperature within theoperational range, requiring both cooling and heating.Whenever the temperature of a battery pack deviatesfrom an operational temperature range, thermal man-agement is activated to guarantee thermal stability ofbatteries [9, 10, 16, 28, 29].

For cooling, the radiator transfers heat from the fluidinside to the air outside, thereby cooling the fluid, whichin turn cools batteries. Heating is also required fordriving at extremely cold temperature. For example,GM Chevy Volt [10] uses 144 thermal fins to activelycool/heat 288 battery cells with a coolant flow valve

controlling cooled/heated coolant flows. Ford Focus [9]is also equipped with an active liquid-cooling and heat-ing system for thermal management of its lithium-ionbattery packs.

So far, this simple approach has been effective for thenormal operation of battery cells during a vehicle war-ranty period; a warranty can be made by thoroughlytesting battery cells’ performance in thermal experimentchambers. However, such a passive, coarse-grained ther-mal control does not take full advantage of thermal man-agement systems. By understanding battery thermalcharacteristics and controlling temperature, we can im-prove battery’s capacity without compromising the life-time of battery cells. This is because heating the cell in-creases the battery performance instantaneously, whilecooling the cell for the situation of requiring low powercan delay the drop of lifetime. To regulate cell temper-ature systematically, we analyze battery dynamics andpropose active and cell-level thermal management formore efficient and reliable BMSes.

3. PROBLEM STATEMENTIn this paper, we would like to develop “good” ther-

mal management for more efficient and reliable batterysystems. For this, we state the problem of thermal man-agement for which we first introduce relevant terms.

A battery pack in EVs supplies DC power to an in-verter which operates the electric motors in EVs. To op-erate motors, a power inverter needs an applicable inputvoltage (Vapp) during the vehicle’s operation. Then, theoperation-time (`op) is defined as the cumulative timefor a battery pack to provide the required power withapplicable output voltage range after a full charge [18].

Therefore, a BMS should enable its battery pack tosupply the required power (Preq(t)) to electric motorswhile maintaining output voltage no less than the ap-plicable input voltage for a long operation-time. Mean-while, the operation-time should be kept long duringthe battery warranty period; otherwise, the vehicle re-quires a larger battery pack and/or batteries must berecharged more frequently.

In this paper, we want to develop a BMS that yields along operation-time during the warranty period by con-trolling the temperature of battery cells. We can con-trol battery temperature by selecting the coolant typein each thermal fin at each time instant to be cooled orheated, which will be detailed in Section 5.3. That is,the coolant type is used as a control knob for our BMS.Our goal is then to determine the coolant type at eachtime instant (Cfin(t)) such that the operation-time (`op)is maximized during the warranty period without anymalfunction or explosion, which is formally expressedas:Given stress conditions {Preq(t), Text(t)}0<t<twarr , de-termine {Cfin(t)}0<t<twarr

, such that `op is maximized

at twarr without any malfunction or explosion in [0, twarr],where Text(t) is temperature outside the battery pack,and 0 and twarr are the time at the beginning andend of the battery warranty period, respectively. Notethat the stress conditions and the control of Cfin(t)are valid/necessary only during operation of the EV in[0, twarr].

Note that `op monotonically decreases over time, be-cause the performance of a battery keeps on degradingover time and never recovering. Therefore, `op at anytime during the warranty is at least as much as that attwarr.

4. ABSTRACTION OF PHYSICAL DYNAM-ICS OF BATTERIES

Several battery dynamics under stress conditions af-fect the performance and safety of battery systems. Forexample, uncontrolled high temperature may cause ex-plosion, while an extremely low temperature reduces thebattery’s performance, potentially leading to failure topower the electric vehicle. Therefore, the impacts anddependencies of control knobs and external conditionson battery dynamics should be analyzed to improve thesafety and performance of a battery system.

To this end, we will first identify the factors that affectthe performance by bridging different abstraction mod-els for battery physical dynamics. Then, based on theunified abstraction model, we will address how a changein temperature affects battery physical dynamics, whichwill be a basis for our battery thermal management tobe described in Section 5.

4.1 Abstraction of Battery CharacteristicsBy analyzing the dependency on thermal conditions

and the impacts on states of batteries, we can figureout how controllable thermal conditions affect the out-put voltage of the entire battery system and each cell’stemperature.

4.1.1 Circuit-Based Battery Model (Vo = f(Rint, Id, SoC))Output voltage (Vo) dictates the operation-time of a

battery system, and the output voltage is greatly af-fected by battery cells’ internal states. A circuit-basedbattery model uses fundamental electric elements, suchas internal resistance (Rint), discharge current (Id), andopen-circuit voltage (Voc) to represent the cell’s internalstates, and explains the output voltage by basic circuittheory as described in Fig. 3 and Eq. (1).

When the cell is connected to an external load, anelectron flow (Id) occurs from the anode to the cathodethrough the external load. Open-circuit voltage (Voc) isthe difference of electrical potential between two termi-nals of the cell when disconnected from any circuit, andit largely depends on deliverable charges (SoC) in bat-tery cells. The internal resistance (Rint) represents all

SoC Voc

Power requirement

Vo

Id

Rint

Rint

+ -

↓ I

SoC

+

Vo

-

Voc

(SoC)

← Id

Figure 3: Circuit-based battery model

the factors causing voltage drop between open-circuitvoltage (Voc) and output voltage (Vo) when the powersource delivers current. In addition, large capacitors areadopted to represent states of deliverable charge (SoC)of cells. Then, the output voltage is expressed as [23]:

Vo = Voc − IdRint = f(SoC)− IdRint, (1)

which says that a change in internal resistance affectsoutput voltage as follows:

P1. Internal resistance⇒Output voltage (R ↑→ Vo ↓):As the internal resistance gets higher, the outputvoltage decreases.

Supplying the required power (Preq) to the electricload induces power dissipation (Pd) in a battery pack.That is, total power consumption (Pbat) in a batterypack (Pbat) consists of the two power consumption pa-rameters (Preq and Pd) as shown in the following equa-tion [23]:

Voc = Vo + IdRint ⇒ VocId = VoId + I2dRint

⇒ Pbat = Preq + Pd. (2)

Therefore, to use batteries efficiently and increase theoperation-time of a battery pack, a BMS should re-duce power dissipation (Pd) during operation, which isrecorded as follows.

P2. Internal resistance⇒ Power dissipation (R↑→Pd↑):As internal resistance gets higher, power dissipa-tion increases.

Since the output voltages of the cells connected inparallel should be the same and the variation of open-circuit voltage is small, discharge current depends oninternal resistance according to Eq. (1). Then, the dis-charge current of a cell with larger internal resistanceis lower than that of the other cells in the module, asrecorded in the following statement:

P3. Internal resistance ⇒ Discharge rate (R↑→Id↓):As internal resistance gets higher, the dischargerate per cell decreases in a module.

4.1.2 Relation between Internal Resistance and CellTemperature (Rint = f(Tcell, t))

As shown in P1 and P2, internal resistance is an im-portant parameter that affects batteries’ performance.

Tcell

Rint 0.9

1 1.1 1.2 1.3 1.4

0 8 16 24 Re

lati

ve r

esi

stan

ce

Time (month)

260K 290K 320K

(b)

0

2

4

6

8

253 273 293 313

Inte

rnal

re

sist

ance

(o

hm

)

Temperature (K) (a)

Figure 4: Internal resistance increases as tem-perature decreases, and relative internal resis-tance increases as time to expose to high temper-ature increases; both graphs are obtained withevaluation tools/settings to be described in Sec-tion 6.

Therefore, we describe how internal resistance varieswith thermal stresses during operation.

Before presenting the effect of temperature on internalresistance, we define two terms related to time interval.An operation cycle represents a cycle of a battery cell’soperation from its full charge to no charge. A calendarlife is a duration from a battery cell’s production to itswarranty period (e.g., 5 years). We describe how tem-perature affects internal resistance, when a given tem-perature lasts during (i) an operation cycle, and (ii) acalendar life.

Regarding (i), a higher temperature stimulates themobility of electron or ion, temporarily reducing thecell’s internal resistance and increasing its capacity (byP2). For example, as shown in Fig. 4(a), resistance with320K is smaller than that with 290K. Therefore, duringan operation cycle, the following relation holds:

P4. Temperature⇒ Internal resistance (T↑→R↓ (oper-

ation cycle)): As temperature during an operationcycle increases, internal resistance decreases.

In literature, relation between internal resistance andcell temperature is well-described by a polynomial model,and experimental results substantiate its effectiveness[27]:

Rint = c3T3cell + c2T

2cell + c1Tcell + c0. (3)

This model dictates cell temperature (Tcell) for the re-quired internal resistance (Rint).

On the other hand, a long-term temperature expo-sure affects internal resistance the other way around,as explained next. Battery performance deterioratesover time, regardless of whether the battery is used ornot, which is known as “calendar fade”, and it can berepresented by a rise of internal resistance as shown inFig. 4(b). There are two key factors influencing the cal-endar life, namely temperature (T ) and time (t), andempirical evidences show that these effects can be rep-resented by two relatively simple mathematical depen-dencies (t and T ). The extent of deterioration can beassessed by relative resistance (µ), which is expressed as

External environment

Battery

Tcell Tfin

External temperature

Id

Rint

Ther

mal

fin

Ther

mal

fin

Qs

Qt Qd

Figure 5: Battery thermal dynamics

[33, 34]:

µ(T ; t) = 1 + exp(β0 + β1 ·1T

) · tρ, (4)

where β0, β1, and ρ represent the model parameters.According to Eq. (4), the following statement holds:

P5. Temperature over time ⇒ Resistance (t↑, T↑→R↑(calendar life)): As time to high temperature expo-sure increases during a calendar life, internal resis-tance increases.

4.1.3 Thermal Behavior Model (Tcell = f(Tamb, Rint, Id))While internal resistance depends on cell temperature

(P4 and P5), cell temperature, in turn, varies with abattery internal state and external stress.

Any battery operation generates heat due to internalresistence when the cell delivers power to electric loads,which is also known as joule heating (I2

dRint loss). Somepart of generated heat (Qd) would be released on thesurface of cells (Qt), and the remaining heat is absorbedinto cell materials (Qs), which can be calculated by [3]:

Qd = I2dRint, Qs = Ccell

dTcelldt

,

Qt = Ah(Tcell − Tamb),

where A is the surface area, h the heat transfer coeffi-cient, Tamb temperature around the cell, and Ccell heatcapacity, respectively. Note that ambient temperature(Tamb) is affected by external temperature (Text) andtemperature of thermal fins (Tfin).

Eq. (5) shows Bernardi’s energy balance [3], and itcan explain cell temperature (Tcell) variation by heatgeneration (Qd) as shown in Fig. 5. Then, Eq. (5) canbe solved by Eq. (6) as follows.

Qd = Qt +Qs, (5)

Tcell(t+∆t) = Tcell(t)+∆t[c1(Tcell−Tamb)+c0I2dRint],

(6)where t is current time and ∆t time interval. Based onthe equation, the following statement holds.

P6. Discharge current ⇒ Heat generation (Id↑→T↑):As discharge current increases, heat generation in-creases.

If we bridge all the battery characteristics discussed inP1–P6, we have an abstract layer from the temperature

SoC Voc

Tcell Tfin

Power requirement External temperature

Vo

Id Th

erm

al

man

agement

Op

era

tio

n t

ime

Rint

Figure 6: Abstraction of battery dynamics

of thermal fins (Tfin) to operation-time, as described inFig. 6.

4.2 Battery Physical Dynamics According to aThermal Change

Our goal is to develop thermal management so asto increase battery operation-time during its warrantyperiod without explosion and malfunction. To figureout battery dynamics affecting the operation-time andsafety, we constructed an abstraction layer by accumu-lating several physical dependencies as shown in Fig. 6.We revisit the physical dependencies (P1–P6) on theabstraction to analyze the battery physical dynamics,which will be a basis for our thermal management to bepresented in Section 5.

4.2.1 Thermal runaway (P3, P4 and P6)Thermal runaway is one of the most serious thermal

issues affecting safety of a battery cell, and results inextremely high temperature and current. A rise in thetemperature of a battery cell decreases its internal re-sistance (by P4) and increases its current (by P3) rel-atively in a parallel connection, which, in turn, raisesits temperature (by P6); this process may repeat, as il-lustrated in Fig. 7. Since extremely high temperature(above around 80◦C) may cause an explosion due to de-composition of materials [23, 14], the thermal stabilityof battery cells should be maintained.

Explosion T↑R↓ R↓I↑ I↑T↑

Figure 7: Increase in temperature of a cellcauses a further increase in the temperature, po-tentially resulting in explosions of the batterycells because of material decomposition insidethe cells.

4.2.2 Malfunction at low temperature (P1 and P4)Each BMS has a specification of applicable output

voltage (Vapp). The BMS should guarantee delivery ofthe required power within Vapp range. However, a de-crease in temperature increases internal resistance (byP4), and it may cause a higher voltage drop (by P1) forsupplying the same current. Output voltage (Vo) drops

below the applicable voltage (Vapp) making a power in-verter unable to operate in a specified condition, andhence failing to provide the required power to the vehi-cle’s electric motors. Fig. 8 illustrates how a low tem-perature causes malfunction.

Malfunction T↓R↑ R↑Vo↓ Insufficient

supply voltage

Figure 8: Low temperature leads to high inter-nal resistance and voltage drop.

4.2.3 Short operation-time at lower temperature (P2and P4)

A decrease in temperature reduces the mobility ofcharges and chemical reaction in cells, which can berepresented by the increased internal resistance (by P4).This increased internal resistance implies a larger powerdissipation during operation (by P2), which reduces operation-time as shown in Fig. 9. For example, it is reported thata drop of just 20 Celsius degrees can drain 10 – 20% ofa battery’s charge [8].

Shorten lifetime T↓R↑ Large power dissipation R↑Pd↑

Figure 9: Low temperature leads to higher in-ternal resistance and power dissipation.

4.2.4 Performance degradation due to continuous ex-posure to high temperature (P1, P2 and P5)

Operating under continuous exposure to high tem-perature induces a rapid increase in internal resistance(by P5) due to the acceleration of irreversible side re-actions [23]. The increase in internal resistance causesenergy dissipation of cells, shortening their operation-time. Faster performance degradation may also causebattery malfunction even during its warranty period (byP1 and P2), potentially leading to tragic accidents or fi-nancial loss. Figs. 8 and 9 illustrate what we explainedso far.

4.2.5 Unbalanced SoC (P4, P5 and P3)Uneven temperature distribution causes different re-

sistances between battery cells in a battery pack (byP4 and P5). Since the discharge rate depends on in-ternal resistance (by P3), the discharge rates availablefor different cells are uneven, leading to unbalanced SoCamong the cells in a battery pack as shown in Fig. 10.

SoC unbalancing t↑,T↑R↑ T↑R↓ R↑↓I↓↑

Figure 10: Uneven degradation and tempera-ture of cells cause different internal resistances ofbattery cells, leading to different discharge cur-rent and SoCes.

4.2.6 Rate-capacity effect and recovery effect (P4,P5, P3 and P6)

A higher discharge rate causes more capacity loss yield-ing larger voltage drop due to heat loss (by P5), calledrate-capacity effect explained in Section 2.1. In caseof a very low (or zero) discharge rate, the battery canrecover its capacity loss to some extent during high-rate of discharge, called recovery effect, also discussedin Section 2.1. Uneven temperature distribution maycause more unexpected discharge current (by P3, P4,P5), leading to more rate-capacity effect in some cellsas shown in Fig. 11. Also, uneven discharge rates causedifferent amounts of heat loss (by P6), causing unbal-anced SoC of cells in a battery pack.

Rate-capacity effect ↑

Recovery effect ↓ T↑R↓ R↓I↑

Figure 11: Uneven states of cells yield differentrate-capacity/recovery effects, leading to unbal-anced electrical states.

5. THE PROPOSED BATTERY THERMALMANAGEMENT

So far, we have investigated and abstracted the phys-ical characteristics of batteries. We have then uncov-ered their effects on thermal dynamics that dictate theperformance of a large-scale battery system. Based onthese, we now identify the thermal requirements consid-ering the thermal dynamics, and then develop a batterythermal management policy which can satisfy the re-quirements based on the abstraction.

5.1 Requirements for efficient and reliable BM-Ses

To enhance the battery’s operation-time, we need tocontrol the physical dynamics we discussed thus far.To protect battery cells from thermal runaway (Sec-tion 4.2.1) and malfunction at low temperature (Sec-tion 4.2.2), the operation temperature (Tcell) should liebetween its upper (Tup) and lower (Tlow) bounds as fol-lows.

R1. Tlow < Tcell < Tup.

According to P3, delivering power with high temper-ature reduces internal resistance and improves the ca-pacity of a battery cell during an operation cycle. Un-fortunately, continuous/frequent exposure to high tem-perature also accelerates the degradation of battery cellsduring a calendar life (as shown in P4 and Section 4.2.4).According to these two characteristics, to improve ca-pacity of cells while increasing lifetime, we should

R2’. Maximize[Tcell(t)

]for 0 < t < twarr ∈ OPERATION,

and

-10 190 390 590 790 990 1190 1390 1590 1790 1990

-100 -80 -60 -40 -20

0 20 40 60 80

600 650 700 750 800 PW

(W

)

Spe

ed

(K

m/h

)

Time (s)

Speed PW

Figure 12: Driving pattern and power require-ments, generated by a vehicle simulator with realdriving records

R3’. Minimize[ ∫ twarr

0Tcell(t)dt|t ∈ OPERATION

],

where OPERATION is a set of time intervals in whichan EV operates.

However, we cannot achieve R2’ and R3’ at the sametime, because maximizing cell temperature will increasecumulative cell temperature. Therefore, existing stud-ies on battery thermal management attempted to deter-mine a static operation temperature range [24, 6], ratherthan dynamic thermal control, and therefore they can-not fully address the two requirements.

To achieve R2’ and R3’ together, we should focus ona property: EVs require power intermittently, ratherthan continuously. For example, requiring high powerfor rapid accelerations consumes most energy as shownin Fig. 12. This means, we do not have to heat cellsall the time for reduction of energy dissipation, becauseheating the cells only during the period of high power re-quirement can reduce energy dissipation mostly. There-fore, separating the management period based on thepower requirement can greatly reduce energy dissipa-tion without losing potential performance improvementmuch. That is, we divide OPERATION into two: WORKand REST, which denote a set of time intervals in OP-ERATION in which a battery cell should provide a highpower, and a lower power (or no power) is required,respectively; by definition, OPERATION = WORK ∪REST. Then, we modify R2’ and R3’ such that it ispossible to achieve them together by scheduling heat-ing/cooling based on their working/resting status: max-imizing working cell temperature (Tcell ↑, if t ∈WORK)and minimizing cumulative resting cells temperature (Tcell ↓,if t ∈ REST), as recorded below.

R2. Maximize[Tcell(t)

]for 0 < t < twarr ∈ WORK,

and

R3. Minimize[ ∫ twarr

0Tcell(t)dt|t ∈ REST

],

Also, we should take into account issues that previouswork focused on. First, we have to equalize the SoCof cells during operation-time (as mentioned in Section4.2.5), because just one deep discharged battery cell canlead to significant reduction of capacity of the entirebattery pack. Therefore, we enforce

R4. Minimize deviation[SoCes

].

By rate-capacity effect, a higher discharge rate causesinefficiency of a battery. To reduce inefficient energyloss by excessive high discharge rates, we set a dischargecurrent limit (Idlim) where the rate-capacity effect doesnot have much impact on the cell’s capacity, and enforcecells to operate within the tolerable discharge rate range(≤ Idlim), if possible, as follows.

R5. Id ≤ Idlim if possible.

Meeting R1–R5 will reduce energy dissipation andprotect cells from unbalanced SoC, explosion, and mal-function. By controlling the temperature of each cell,we can directly meet R1–R3 and also alter dischargerate, addressing R4 and R5. That is, by P3 and P4, wecan decrease (increase) discharge rate by lowering (rais-ing) temperature. To realize such a control, we needa battery thermal management system and a cell-levelthermal management architecture, which are detailednext.

5.2 Main thermal management: after startinga vehicle

We now propose a battery thermal management sys-tem (TMS) that meets R1–R5. Its basic principle is toheat (cool) cells to be high-discharged (rested or low-discharged). Based on each cell’s SoC and the requiredpower, our TMS determines the type of coolant for eachcell to realize the principle and achieve our goal. Algo-rithm 1 describes how the proposed TMS operates.

5.2.1 Update states of cells (Lines 2–6)We update all the states of battery cells in a pack to

support the following steps. We can directly measureeach cell’s output voltage and current via sensors, andthen estimate the cell’s SoC, Rint, and Voc based on themeasured values [30, 20, 5, 22].

5.2.2 Set target output voltage (Lines 7–11)This step regulates the target output voltage (Vo)

based on the power requirements. Increasing Vo helpspower the vehicle because the following steps heat thebattery cells to reduce internal resistance in order tosupply the target output voltage. Unfortunately, thepower requirement may change abruptly (e.g., due tosudden acceleration or deceleration of the vehicle) andthermal control takes time. Therefore, we predict thepower requirement by analyzing the driving patterns [35,15] before segmenting the driving path for effective man-agement in the subsequent steps.

5.2.3 Calculate the target discharge current (Line 13)This calculates efficient discharge current (Id) of cells

for small heat dissipation and SoC balancing. As dis-cussed in R5, we need to make each cell’s discharge

Algorithm 1 Algorithm for thermal management afterstarting operation1: for Each module do2: measure Vo

3: for Each cell X (the n-th cell) in the module do4: measure Id[n] and Tcell[n];5: estimate SoC[n], Voc[n] and Rint[n];6: end for7: if High power is required then8: increase Vo;9: else10: decrease Vo;11: end if12: for Each cell X (the n-th cell) in the module do13: choose efficient Id[n];14: choose Rint[n] from Eq. (1);15: choose Tcell[n] from Eq. (3);16: choose Tfin[n] from Eq. (6) and thermal distribution in

a pack;17: if Tfin[n] ≥ previous Tfin[n] then18: HeatingSet ← HeatingSet ∪ {X};19: else20: CoolingSet ← CoolingSet ∪ {X};21: end if22: if Tcell[n] ≥ Tup then23: CoolingSet ← CollingSet ∪ {X};24: HeatingSet ← HeatingSet \ {X};25: end if26: end for27: end for

rate no larger than Idlim for reduction of inefficient en-ergy dissipation. Suppose Idlim ≤ Preq

N ·Vtotholds, where

Preq is the required power, N is the number of parallel-connected cells, and Vtot is the total output voltage.Then, by making each cell discharged at Itot

N , we cansupply the required power without excessive heat dissi-pation, where Itot is total discharge current. To meetR4, we set Id based on the SoC when the level of powerrequirement (Preq) is satisfied; cells with higher SoCshould work more. Therefore, the discharge current (Id)is set to Itot

N if Idlim ≤ Preq

N ·Vtotholds, and Itot

SoCΣSoC oth-

erwise, which is recorded as:

Id =

{Itot

N , if Idlim ≤ Preq

N ·Vtot,

ItotSoC

ΣSoC , otherwise.

5.2.4 Calculate the required thermal fins’ tempera-ture (Lines 14–16)

After setting Id and Vo, we can calculate the requiredRint and Tcell from Eqs. (1) and (3) as:

c3T3cell + c2T

2cell + c1Tcell + c0 = Rint =

Voc − VoId

,

while Eq. (6) calculates the desirable ambient tempera-ture (Tamb).

Cell temperature depends on ambient temperature in-cluding the temperature of thermal fins, and the ambi-ent temperature can be estimated by updating tempera-ture distribution in a battery pack as shown in Fig. 13.Note that the temperature distribution can be calcu-lated based on temperatures measured by sensors in a

CELL1

CELL2

CELL3

CELL4

CELL5

Cell

Thermal fins

External

Ambient

Figure 13: Temperature distribution and ambi-ent temperature of cells in a battery pack

Battery pack

Electric motor

Heating channel

↑ ↑

Cooling channel

+

Power inverter

Rad

iato

r

He

ate

r

↓

-

Figure 14: Thermal management for batterypacks

pack. Based on the temperature distribution, we canobtain thermal fins’ temperatures that achieve the tar-get ambient temperature.

5.2.5 Select the coolant type for thermal fins (Lines17–25)

Lines 17–21 determine the coolant type (heated orcooled) for cells based on current and required fin tem-peratures. In Lines 22–25, we choose the cells whosetemperature is higher than the upper limit, and movethem from HeatingSet to maintain thermal stability.

As discussed earlier, the way of calculating Id ad-dresses R4 and R5. Then, all the steps will meet R2and R3, since cells are heated (cooled) when they needhigh power (otherwise). The final step (Lines 22–25)guarantees R1. So, Algorithm 1 meets R1–R5.

5.3 Thermal Management ArchitectureR1–R3 can be met by the existing thermal architec-

ture, since it can regulate the temperature of batterycells in a timely manner. To satisfy R4 and R5, we needa new architecture which can cool/heat each cell selec-tively as shown in Fig. 14. The new architecture is notmuch different from existing architectures. While ex-isting BMSes are already been equipped with the heat-ing/cooling capability (e.g., [10, 9]), we need to addmore coolant flow valves between the heating/coolingchannel and thermal fins for each cell, as shown in thefigure. Each coolant control valve should be able to se-lect the type (either heating or cooling) of coolant forcells.

6. EVALUATION

Cell status simulation Cell degradation model

Random driving pattern Monthly temperature pattern

Energy dissipation Output voltage

Sub-module status simulation

Pack status simulation

Operation-time

Cell degradation

Figure 15: Evaluation tools

We now evaluate the performance improvement bythe proposed TMS, focusing on whether or not the goalstated in Section 3 is met. We first introduce the perfor-mance metrics and evaluation tools/settings to be used.We then present the evaluation results, and finally makeremarks on the TMS implementation.

6.1 Evaluation metricsTo evaluate the performance of the proposed TMS, we

conducted various simulations, and recorded relevant in-ternal battery states. To check if the goal is achieved ornot, we extracted the information of battery operation-time. Also, to analyze the simulation results, we useenergy dissipation to see how fast energy in batteries isconsumed, and internal resistance to figure out degra-dation rate of batteries.

6.2 Evaluation tools and settingsTo study battery behavior under the proposed TMS,

we designed a comprehensive simulator which can calcu-late each cell’s internal states under realistic situations.Also, to obtain realistic thermal stress values for eachcell, three US cities are selected: Anchorage, AK; AnnArbor, MI; and Phoenix, AZ (denoted by AC, AA andPH, respectively), which represent an extremely cold,moderate, and hot weather environments. Fig. 15 pro-vides an overview, components of which are describednext.

6.2.1 Dualfoil5 (cell-level battery simulator)Dualfoil5 is a popular battery simulator written in

Fortran, and can simulate various types of batteries in-cluding the lithium-metal, lithium-ion, and sodium-ionbatteries [7]. The program reads the load profile asa sequence of constant current steps, and the batterylifetime is obtained from the output by reading off thetime at which the cell potential drops below the cutoffvoltage. The equations and methods used in the pro-gram rely on an electrochemical model that describesthe charge and discharge of a lithium ion battery devel-oped by Marc Doyle et al. [7].

6.2.2 CarSim with driving patterns (power require-ment)

To acquire realistic power requirements, we exploit USdriving patterns [32], a daily driving pattern model [31,

-1019039059079099011901390159017901990

-100-80-60-40-200

20406080

0 500 1000 1500 2000

PW

(W

)

Spe

ed

(K

m/h

)

Time (s)

Speed PW

Figure 16: Speed and power requirement profiles

21], and CarSim [25]. CarSim is a well-known andwidely-used vehicle modeling tool, simulating the dy-namic behavior of vehicles under specified driving con-ditions, and calculating the required power during driv-ing. We obtain the power requirement profile as fol-lows. First, a daily driving pattern model with a set ofUS real driving patterns yields daily driving patterns.These daily driving patterns are then fed to CarSim togenerate the power requirement. Fig. 16 shows a set ofspeed and power requirement profiles generated by thisprocess.

6.3 Evaluation resultsWe evaluate the following three thermal management

schemes:

• BASE: no thermal management;

• EX: existing approaches described in Section 2 (pack-level cooling/heating only for thermal stability);and

• MSC: our thermal management described in Sec-tion 5 (active cell-level cooling/heating for efficientBMSes).

For these evaluations, we generated realistic drivingand external temperature profiles before simulating thebehavior of a battery system with the above three schemes.The three metrics described in Section 6.1 are then ex-tracted from the simulation results.

6.3.1 Operation-times after one-year operationWe ran simulation and extracted operation-times of

each scheme under various stress conditions. Averageoperation-times are then calculated to show the over-all performance of each scheme. Table 1 shows the ra-tio of the average operation-time of MSC (and EX), tothat of BASE. As shown in the table, MSC improves theoperation-time by up to 204% and 58.4%, respectivelyover BASE and EX. Also, MSC keeps all the batterieswithin a tolerable temperature range to prevent explo-sion or malfunction as shown in Fig. 17; in AC, somebatteries operate at extremely cold temperature underBASE, potentially leading to malfunction or inefficientusage of the battery pack, whereas all the cells in apack operate within a tolerable temperature range un-der MSC and EX.

0

6000

12000

18000

0 1 2 3 4 5 6 7 8 9 10 11 12

Dis

sip

atio

n e

ne

rgy

(Wh

/m^

2)

time (month)

BASE MSC EX

Figure 18: Cumulative energy dissipation forone-year operation in AA

Average `op (Sec)City, Month AC AA PH

EX/BASE 1.92 0.92 1.03MSC/BASE 3.04 1.40 1.62

Table 1: Average operation-time

From Table 1, we can observe that MSC’s improve-ment in AC is more pronounced than that in AA andPH, because there is more room for performance im-provement in colder areas, which we will elaborate inSection 6.3.2. As shown in Table 1, MSC effectively ex-ploits such room for improvement. For a more detailedanalysis for the improvement of MSC, we investigateenergy dissipation and performance degradation in thefollowing subsections.

6.3.2 Energy dissipationOur TMS in Section 5 includes several methods to

decrease energy dissipation. They actually contribute tothe extension of operation-time, since reducing energydissipation increases available energy in the cells, which,in turn, extends the operation-time.

We compare the energy dissipation of the three ther-mal management schemes when the car operates in AC,AA and PH. Table 2 shows energy dissipation whendrivers operate their cars repeating the same drivingpattern every day for a year. Fig. 18 shows an ex-ample energy dissipation of each management scheme.Compared to BASE, MSC reduces energy dissipation by70.6% in AC, 63.8% in AA, and 48.4% in PH. MSC is evenbetter than EX in that it can reduce more energy dissi-pation by up to 58.6% than EX. This reduction of energydissipation extends the operation-time under MSC.

Cumulative energy dissipation (Wh/m2)City AC AA PH

BASE 20978 16216 10474EX 14910 13450 9976.5MSC 6167.5 5864.6 5403.5

Table 2: Energy dissipation after one year of op-eration

An interesting point to note is that the difference be-tween energy dissipation of MSC (or EX) and that ofBASE is significant in AC. This can be reasoned as fol-

Figure 17: Example of thermal management in AC; C x means Cell x

0.0027

0.0028

0.0029

0.003

0.0031

0.0032

0.0033

0 2 4 6 8 10

inte

rnal

re

sist

ance

(o

hm

*m^2

)

time (month)

MSC

EX

BASE

0.99

1.01

1.03

1.05

1.07

1.09

0 2 4 6 8 10

rela

tive

re

sist

ance

(μ

)

time (month)

Figure 19: Relative resistance and internal re-sistance in AA

lows. In a hotter area, the efficiency is higher (or smallerenergy dissipation) even without any thermal manage-ment (i.e., BASE), because high temperature increasesthe chemical reaction rate in a battery. So, in AC wheretemperature is extremely low in winter, MSC and EXheat the cells to attain effective operation temperature;we observe from Fig. 17 that EX and MSC yield a toler-able temperature range for battery cells. This reducesenergy dissipation.

MSC reduces energy dissipation significantly more thanEX, since it not only timely cools/heats the cells in or-der to reduce energy dissipation, but also selectivelycools/heats the cells for SoC equalization. Therefore,each cell operates more efficiently under MSC, by re-ducing temperature differences between cells. The re-duction of energy dissipation is one of the dominantreasons for improving the operation-time.

6.3.3 Degradation of battery performanceThe degree of battery performance degradation de-

pends on its cumulative exposure to high temperature.To evaluate the performance degradation, we comparecells’ degradation levels, which are represented by rela-tive resistance and the absolute value of internal resis-tance, under each thermal management scheme in AC,AA and PH during a 1-year period. Of these three loca-tions, we chose AA, since the results for other locationsexhibit a similar or same trend.

Figs. 19(a) and (b) plot relative resistance and theabsolute value of internal resistance, respectively. Asshown in Fig. 19(a), MSC leads to a little bit fasterdegradation than the other schemes, because MSC ac-tively cools/heats each cell. However, such active con-trol reduces the absolute value of internal resistance;as shown in Fig. 19(b), internal resistance after one-

year usage under MSC is smaller than that under otherschemes. This implies that MSC, despite higher rela-tive resistance, is still more efficient for up to one year.These results validate an increase of operation-time dis-cussed in Section 6.3.1, i.e., MSC is efficient in using abattery pack.

6.4 Remarks on implementationWe can construct the TMS based on the exsisting

thermal managmenet system for cooling and heating en-gines and batteries in the current vehicles on the mar-ket. To implement the BMS that accommodates theproposed TMS, some additional hardware should be in-stalled in each battery pack. Measuring the output volt-age and discharge current of cells requires sensors, asother BMSes [13, 19, 11] do. Additional control flowvalves are also required to cell-level thermal controls;their performance and reliability should be consideredsince they directly affect the effectiveness of batterymanagement. It would be interesting to design a BMSwith efficient and reliable management of hardware sen-sors and actuators by considering their physical charac-teristics along with battery dynamics. This is part ofour future work.

7. CONCLUSIONTo resolve increasing demand to make EVs less ex-

pensive and safer, BMSes should cope with thermal andgeneral issues that affect efficiency and reliability of BM-Ses. A thermal management is the key to addressingthese issues, since temperature has significant impact onelectrical states of BMSes. In this paper, we presentedhow to achieve efficient and reliable BMSes using ther-mal controls. We proposed a battery thermal manage-ment scheme that cools/heats battery cells timely andselectively based on the analysis for impacts of powerrequirements and temperature variation on electricalstates of battery cells. To support this scheme, we alsoproposed thermal management architecture, which isable to cool/heat battery cells selectively. Our evalua-tion with realistic simulation showed that our approachmakes a significant improvement in BMSes’ efficiencywithout compromising their reliability, over a simplemethod often seen in the existing BMSes.

It would be interesting to explore ways of improvingthermal management techniques based on the proposed

architecture. If we use a more accurate and adaptiveabstraction model instead of the fundamental model ofcurrent version, the performance would be enhanced.Also, we can improve the efficiency of BMSes by heat-ing/cooling cells in advance with more accurate powerrequirement prediction [35, 15].

AcknowledgementThe work reported in this paper was supported in partby the NSF under grants CNS-1138200 and CNS-1329702.

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